@article{SCOPUS_ID:85020465075,

title = {Lessons from the CAGI-4 Hopkins clinical panel challenge},

author = {John-Marc Chandonia and Aashish Adhikari and Marco Carraro and Aparna Chhibber and Garry R. Cutting and Yao Fu and Alessandra Gasparini and David T. Jones and Andreas Kramer and Kunal Kundu and Hugo Y. K. Lam and Emanuela Leonardi and John Moult and Lipika R. Pal and David B. Searls and Sohela Shah and Shamil Sunyaev and Silvio C. E. Tosatto and Yizhou Yin and Bethany A. Buckley},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85020465075&origin=inward},

doi = {10.1002/humu.23225},

year  = {2017},

date = {2017-01-01},

journal = {Human Mutation},

volume = {38},

number = {9},

pages = {1155-1168},

publisher = {John Wiley and Sons Inc},

abstract = {© 2017 Wiley Periodicals, Inc.The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.},

note = {Cited by: 6; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85024484623,

title = {A community proposal to integrate proteomics activities in ELIXIR},

author = {Juan Antonio Vizcaíno and Mathias Walzer and Rafael C. Jiménez and Wout Bittremieux and David Bouyssié and Christine Carapito and Fernando Corrales and Myriam Ferro and Albert J. R. Heck and Peter Horvatovich and Martin Hubalek and Lydie Lane and Kris Laukens and Fredrik Levander and Frederique Lisacek and Petr Novak and Magnus Palmblad and Damiano Piovesan and Alfred Pühler and Veit Schwämmle and Dirk Valkenborg and Merlijn Rijswijk and Jiri Vondrasek and Martin Eisenacher and Lennart Martens and Oliver Kohlbacher},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85024484623&origin=inward},

doi = {10.12688/f1000research.11751.1},

year  = {2017},

date = {2017-01-01},

journal = {F1000Research},

volume = {6},

publisher = {Faculty of 1000 Ltdinfo@f1000.com},

abstract = {© 2017 Vizcaíno JA et al.Computational approaches have been major drivers behind the progress of proteomics in recent years. The aim of this white paper is to provide a framework for integrating computational proteomics into ELIXIR in the near future, and thus to broaden the portfolio of omics technologies supported by this European distributed infrastructure. This white paper is the direct result of a strategy meeting on 'The Future of Proteomics in ELIXIR' that took place in March 2017 in Tübingen (Germany), and involved representatives of eleven ELIXIR nodes. These discussions led to a list of priority areas in computational proteomics that would complement existing activities and close gaps in the portfolio of tools and services offered by ELIXIR so far. We provide some suggestions on how these activities could be integrated into ELIXIR's existing platforms, and how it could lead to a new ELIXIR use case in proteomics. We also highlight connections to the related field of metabolomics, where similar activities are ongoing. This white paper could thus serve as a starting point for the integration of computational proteomics into ELIXIR. Over the next few months we will be working closely with all stakeholders involved, and in particular with other representatives of the proteomics community, to further refine this paper.},

note = {Cited by: 11; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85020523355,

title = {In silico study of the structure and function of Streptococcus mutans plasmidic proteins},

author = {Silvia Caprari and Giovanni Minervini and Valentina Brandi and Fabio Polticelli},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85020523355&origin=inward},

doi = {10.1515/bams-2017-0012},

year  = {2017},

date = {2017-01-01},

journal = {Bio-Algorithms and Med-Systems},

volume = {13},

number = {2},

pages = {51-61},

publisher = {Walter de Gruyter GmbH},

abstract = {© 2017 Walter de Gruyter GmbH, Berlin/Boston 2017.The Gram-positive bacterium Streptococcus mutans is the principal causative agent of human tooth decay, an oral disease that affects the majority of the world's population. Although the complete S. mutans genome is known, approximately 700 proteins are still annotated as hypothetical proteins, as no three-dimensional structure or homology with known proteins exists for them. Thus, the significant portion of genomic sequences coding for unknown-function proteins makes the knowledge of pathogenicity and survival mechanisms of S. mutans still incomplete. Plasmids are found in virtually every species of Streptococcus, and some of these mediate resistance to antibiotics and pathogenesis. However, there are strains of S. mutans that contain plasmids, such as LM7 and UA140, to which no function has been assigned yet. In this work, we describe an in silico study of the structure and function of all the S. mutans proteins encoded by pLM7 and pUA140 plasmids to gain insight into their biological function. A combination of different structural bioinformatics methodologies led to the identification of plasmidic proteins potentially required for the bacterial survival and pathogenicity. The structural information obtained on these proteins can be used to select novel targets for the design of innovative therapeutic agents towards S. mutans.},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85016141786,

title = {InterPro in 2017-beyond protein family and domain annotations},

author = {Robert D. Finn and Teresa K. Attwood and Patricia C. Babbitt and Alex Bateman and Peer Bork and Alan J. Bridge and Hsin-Yu Chang and Zsuzsanna Dosztanyi and Sara El-Gebali and Matthew Fraser and Julian Gough and David Haft and Gemma L. Holliday and Hongzhan Huang and Xiaosong Huang and Ivica Letunic and Rodrigo Lopez and Shennan Lu and Aron Marchler-Bauer and Huaiyu Mi and Jaina Mistry and Darren A. Natale and Marco Necci and Gift Nuka and Christine A. Orengo and Youngmi Park and Sebastien Pesseat and Damiano Piovesan and Simon C. Potter and Neil D. Rawlings and Nicole Redaschi and Lorna Richardson and Catherine Rivoire and Amaia Sangrador-Vegas and Christian Sigrist and Ian Sillitoe and Ben Smithers and Silvano Squizzato and Granger Sutton and Narmada Thanki and Paul D. Thomas and Silvio C. E. Tosatto and Cathy H. Wu and Ioannis Xenarios and Lai-Su Yeh and Siew-Yit Young and Alex L. Mitchell},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85016141786&origin=inward},

doi = {10.1093/nar/gkw1107},

year  = {2017},

date = {2017-01-01},

journal = {Nucleic Acids Research},

volume = {45},

number = {D1},

pages = {D190-D199},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© 2016 The Author(s).InterPro (http://www.ebi.ac.uk/interpro/) is a freely available database used to classify protein sequences into families and to predict the presence of important domains and sites. InterProScan is the underlying software that allows both protein and nucleic acid sequences to be searched against InterPro's predictive models, which are provided by its member databases. Here, we report recent developments with InterPro and its associated software, including the addition of two new databases (SFLD and CDD), and the functionality to include residue-level annotation and prediction of intrinsic disorder. These developments enrich the annotations provided by InterPro, increase the overall number of residues annotated and allow more specific functional inferences.},

note = {Cited by: 1145; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85029008824,

title = {Second ISCB Latin American Student Council Symposium (LA-SCS) 2016},

author = {Alexander Miguel Monzon and Marcia A. Hasenahuer and Estefanía Mancini and Nilson Coimbra and Fiorella Cravero and Javier Cáceres-Molina and César A Ramírez-Sarmiento and Nicolas Palopoli and Pieter Meysman and R Gonzalo Parra},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85029008824&origin=inward},

doi = {10.12688/f1000research.12321.1},

year  = {2017},

date = {2017-01-01},

journal = {F1000Research},

volume = {6},

publisher = {NLM (Medline)},

abstract = {This report summarizes the scientific content and activities of the second edition of the Latin American Symposium (LA-SCS), organized by the Student Council (SC) of the International Society for Computational Biology (ISCB), held in conjunction with the Fourth Latin American conference from the International Society for Computational Biology (ISCB-LA 2016) in Buenos Aires, Argentina, on November 19, 2016.},

note = {Cited by: 4; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85023169351,

title = {SODA: Prediction of protein solubility from disorder and aggregation propensity},

author = {Lisanna Paladin and Damiano Piovesan and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85023169351&origin=inward},

doi = {10.1093/nar/gkx412},

year  = {2017},

date = {2017-01-01},

journal = {Nucleic Acids Research},

volume = {45},

number = {W1},

pages = {W236-W240},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© 2017 The Author(s).Solubility is an important, albeit not well understood, feature determining protein behavior. It is of paramount importance in protein engineering, where similar folded proteins may behave in very different ways in solution. Here we present SODA, a novel method to predict the changes of protein solubility based on several physico-chemical properties of the protein. SODA uses the propensity of the protein sequence to aggregate as well as intrinsic disorder, plus hydrophobicity and secondary structure preferences to estimate changes in solubility. It has been trained and benchmarked on two different datasets. The comparison to other recently published methods shows that SODA has state-of-the-art performance and is particularly well suited to predict mutations decreasing solubility. The method is fast, returning results for single mutations in seconds. A usage example estimating the full repertoire of mutations for a human germline antibody highlights several solubility hotspots on the surface.},

note = {Cited by: 52; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85016112986,

title = {DisProt 7.0: A major update of the database of disordered proteins},

author = {Damiano Piovesan and Francesco Tabaro and Ivan Mičetić and Marco Necci and Federica Quaglia and Christopher J. Oldfield and Maria Cristina Aspromonte and Norman E. Davey and Radoslav Davidović and Zsuzsanna Dosztányi and Arne Elofsson and Alessandra Gasparini and András Hatos and Andrey V. Kajava and Lajos Kalmar and Emanuela Leonardi and Tamas Lazar and Sandra Macedo-Ribeiro and Mauricio Macossay-Castillo and Attila Meszaros and Giovanni Minervini and Nikoletta Murvai and Jordi Pujols and Daniel B. Roche and Edoardo Salladini and Eva Schad and Antoine Schramm and Beata Szabo and Agnes Tantos and Fiorella Tonello and Konstantinos D. Tsirigos and Nevena Veljković and Salvador Ventura and Wim Vranken and Per Warholm and Vladimir N. Uversky and A. Keith Dunker and Sonia Longhi and Peter Tompa and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85016112986&origin=inward},

doi = {10.1093/nar/gkw1056},

year  = {2017},

date = {2017-01-01},

journal = {Nucleic Acids Research},

volume = {45},

number = {D1},

pages = {D219-D227},

publisher = {Oxford University Press},

abstract = {© The Author(s) 2016.The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.},

note = {Cited by: 205; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84995948500,

title = {Mapping pathogenic mutations suggests an innovative structural model for the pendrin (SLC26A4) transmembrane domain},

author = {Claudio Bassot and Giovanni Minervini and Emanuela Leonardi and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84995948500&origin=inward},

doi = {10.1016/j.biochi.2016.10.002},

year  = {2017},

date = {2017-01-01},

journal = {Biochimie},

volume = {132},

pages = {109-120},

publisher = {Elsevier B.V.},

abstract = {© 2016 Elsevier B.V. and Société Française de Biochimie et Biologie Moléculaire (SFBBM)Human pendrin (SLC26A4) is an anion transporter mostly expressed in the inner ear, thyroid and kidney. SLC26A4 gene mutations are associated with a broad phenotypic spectrum, including Pendred Syndrome and non-syndromic hearing loss with enlarged vestibular aqueduct (ns-EVA). No experimental structure of pendrin is currently available, making phenotype-genotype correlations difficult as predictions of transmembrane (TM) segments vary in number. Here, we propose a novel three-dimensional (3D) pendrin transmembrane domain model based on the SLC26Dg transporter. The resulting 14 TM topology was found to include two non-canonical transmembrane segments crucial for pendrin activity. Mutation mapping of 147 clinically validated pathological mutations shows that most affect two previously undescribed TM regions.},

note = {Cited by: 20},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85026471401,

title = {Dynamic scaffolds for neuronal signaling: In silico analysis of the TANC protein family},

author = {Alessandra Gasparini and Silvio C. E. Tosatto and Alessandra Murgia and Emanuela Leonardi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85026471401&origin=inward},

doi = {10.1038/s41598-017-05748-5},

year  = {2017},

date = {2017-01-01},

journal = {Scientific Reports},

volume = {7},

number = {1},

publisher = {Nature Publishing GroupHoundmillsBasingstoke, HampshireRG21 6XS},

abstract = {© 2017 The Author(s).The emergence of genes implicated across multiple comorbid neurologic disorders allows to identify shared underlying molecular pathways. Recently, investigation of patients with diverse neurologic disorders found TANC1 and TANC2 as possible candidate disease genes. While the TANC proteins have been reported as postsynaptic scaffolds influencing synaptic spines and excitatory synapse strength, their molecular functions remain unknown. Here, we conducted a comprehensive in silico analysis of the TANC protein family to characterize their molecular role and understand possible neurobiological consequences of their disruption. The known Ankyrin and tetratricopeptide repeat (TPR) domains have been modeled. The newly predicted N-terminal ATPase domain may function as a regulated molecular switch for downstream signaling. Several putative conserved protein binding motifs allowed to extend the TANC interaction network. Interestingly, we highlighted connections with different signaling pathways converging to modulate neuronal activity. Beyond a known role for TANC family members in the glutamate receptor pathway, they seem linked to planar cell polarity signaling, Hippo pathway, and cilium assembly. This suggests an important role in neuron projection, extension and differentiation.},

note = {Cited by: 21; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85016144082,

title = {RepeatsDB 2.0: Improved annotation, classification, search and visualization of repeat protein structures},

author = {Lisanna Paladin and Layla Hirsh and Damiano Piovesan and Miguel A. Andrade-Navarro and Andrey V. Kajava and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85016144082&origin=inward},

doi = {10.1093/nar/gkw1136},

year  = {2017},

date = {2017-01-01},

journal = {Nucleic Acids Research},

volume = {45},

number = {D1},

pages = {D308-D312},

publisher = {Oxford University Press},

abstract = {© 2016 The Author(s).RepeatsDB 2.0 (URL: http://repeatsdb.bio.unipd.it/) is an update of the database of annotated tandem repeat protein structures. Repeat proteins are a widespread class of non-globular proteins carrying heterogeneous functions involved in several diseases. Here we provide a new version of RepeatsDB with an improved classification schema including high quality annotations for ∼5400 protein structures. RepeatsDB 2.0 features information on start and end positions for the repeat regions and units for all entries. The extensive growth of repeat unit characterization was possible by applying the novel ReUPred annotation method over the entire Protein Data Bank, with data quality is guaranteed by an extensive manual validation for >60% of the entries. The updated web interface includes a new search engine for complex queries and a fully re-designed entry page for a better overview of structural data. It is now possible to compare unit positions, together with secondary structure, fold information and Pfam domains. Moreover, a new classification level has been introduced on top of the existing scheme as an independent layer for sequence similarity relationships at 40%, 60% and 90% identity.},

note = {Cited by: 25; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85019446021,

title = {Ca2+ binding to F-ATP synthase β subunit triggers the mitochondrial permeability transition},

author = {Valentina Giorgio and Victoria Burchell and Marco Schiavone and Claudio Bassot and Giovanni Minervini and Valeria Petronilli and Francesco Argenton and Michael Forte and Silvio Tosatto and Giovanna Lippe and Paolo Bernardi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85019446021&origin=inward},

doi = {10.15252/embr.201643354},

year  = {2017},

date = {2017-01-01},

journal = {EMBO Reports},

volume = {18},

number = {7},

pages = {1065-1076},

publisher = {Wiley-VCH Verlaginfo@wiley-vch.de},

abstract = {© 2017 The AuthorsF-ATP synthases convert the electrochemical energy of the H+ gradient into the chemical energy of ATP with remarkable efficiency. Mitochondrial F-ATP synthases can also undergo a Ca2+-dependent transformation to form channels with properties matching those of the permeability transition pore (PTP), a key player in cell death. The Ca2+ binding site and the mechanism(s) through which Ca2+ can transform the energy-conserving enzyme into a dissipative structure promoting cell death remain unknown. Through in vitro, in vivo and in silico studies we (i) pinpoint the “Ca2+-trigger site” of the PTP to the catalytic site of the F-ATP synthase β subunit and (ii) define a conformational change that propagates from the catalytic site through OSCP and the lateral stalk to the inner membrane. T163S mutants of the β subunit, which show a selective decrease in Ca2+-ATP hydrolysis, confer resistance to Ca2+-induced, PTP-dependent death in cells and developing zebrafish embryos. These findings are a major advance in the molecular definition of the transition of F-ATP synthase to a channel and of its role in cell death.},

note = {Cited by: 179; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85016091177,

title = {Simultaneous quantification of protein order and disorder},

author = {Pietro Sormanni and Damiano Piovesan and Gabriella T. Heller and Massimiliano Bonomi and Predrag Kukic and Carlo Camilloni and Monika Fuxreiter and Zsuzsanna Dosztanyi and Rohit V Pappu and M Madan Babu and Sonia Longhi and Peter Tompa and A Keith Dunker and Vladimir N Uversky and Silvio C E Tosatto and Michele Vendruscolo},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85016091177&origin=inward},

doi = {10.1038/nchembio.2331},

year  = {2017},

date = {2017-01-01},

journal = {Nature Chemical Biology},

volume = {13},

number = {4},

pages = {339-342},

publisher = {Nature Publishing GroupHoundmillsBasingstoke, HampshireRG21 6XS},

note = {Cited by: 100},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85019402721,

title = {Performance of in silico tools for the evaluation of p16INK4a (CDKN2A) variants in CAGI},

author = {Marco Carraro and Giovanni Minervini and Manuel Giollo and Yana Bromberg and Emidio Capriotti and Rita Casadio and Roland Dunbrack and Lisa Elefanti and Pietro Fariselli and Carlo Ferrari and Julian Gough and Panagiotis Katsonis and Emanuela Leonardi and Olivier Lichtarge and Chiara Menin and Pier Luigi Martelli and Abhishek Niroula and Lipika R. Pal and Susanna Repo and Maria Chiara Scaini and Mauno Vihinen and Qiong Wei and Qifang Xu and Yuedong Yang and Yizhou Yin and Jan Zaucha and Huiying Zhao and Yaoqi Zhou and Steven E. Brenner and John Moult and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85019402721&origin=inward},

doi = {10.1002/humu.23235},

year  = {2017},

date = {2017-01-01},

journal = {Human Mutation},

volume = {38},

number = {9},

pages = {1042-1050},

publisher = {John Wiley and Sons Inc},

abstract = {© 2017 Wiley Periodicals, Inc.Correct phenotypic interpretation of variants of unknown significance for cancer-associated genes is a diagnostic challenge as genetic screenings gain in popularity in the next-generation sequencing era. The Critical Assessment of Genome Interpretation (CAGI) experiment aims to test and define the state of the art of genotype–phenotype interpretation. Here, we present the assessment of the CAGI p16INK4a challenge. Participants were asked to predict the effect on cellular proliferation of 10 variants for the p16INK4a tumor suppressor, a cyclin-dependent kinase inhibitor encoded by the CDKN2A gene. Twenty-two pathogenicity predictors were assessed with a variety of accuracy measures for reliability in a medical context. Different assessment measures were combined in an overall ranking to provide more robust results. The R scripts used for assessment are publicly available from a GitHub repository for future use in similar assessment exercises. Despite a limited test-set size, our findings show a variety of results, with some methods performing significantly better. Methods combining different strategies frequently outperform simpler approaches. The best predictor, Yang&Zhou lab, uses a machine learning method combining an empirical energy function measuring protein stability with an evolutionary conservation term. The p16INK4a challenge highlights how subtle structural effects can neutralize otherwise deleterious variants.},

note = {Cited by: 13; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85027511944,

title = {Crohn disease risk prediction—Best practices and pitfalls with exome data},

author = {Manuel Giollo and David T. Jones and Marco Carraro and Emanuela Leonardi and Carlo Ferrari and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85027511944&origin=inward},

doi = {10.1002/humu.23177},

year  = {2017},

date = {2017-01-01},

journal = {Human Mutation},

volume = {38},

number = {9},

pages = {1193-1200},

publisher = {John Wiley and Sons Inc.P.O.Box 18667NewarkNJ 07191-8667},

abstract = {© 2017 Wiley Periodicals, Inc.The Critical Assessment of Genome Interpretation (CAGI) experiment is the first attempt to evaluate the state-of-the-art in genetic data interpretation. Among the proposed challenges, Crohn disease (CD) risk prediction has become the most classic problem spanning three editions. The scientific question is very hard: can anybody assess the risk to develop CD given the exome data alone? This is one of the ultimate goals of genetic analysis, which motivated most CAGI participants to look for powerful new methods. In the 2016 CD challenge, we implemented all the best methods proposed in the past editions. This resulted in 10 algorithms, which were evaluated fairly by CAGI organizers. We also used all the data available from CAGI 11 and 13 to maximize the amount of training samples. The most effective algorithms used known genes associated with CD from the literature. No method could evaluate effectively the importance of unannotated variants by using heuristics. As a downside, all CD datasets were strongly affected by sample stratification. This affected the performance reported by assessors. Therefore, we expect that future datasets will be normalized in order to remove population effects. This will improve methods comparison and promote algorithms focused on causal variants discovery.},

note = {Cited by: 13; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85014243730,

title = {Conformational diversity analysis reveals three functional mechanisms in proteins},

author = {Alexander Miguel Monzon and Diego Javier Zea and María Silvina Fornasari and Tadeo E. Saldaño and Sebastian Fernandez-Alberti and Silvio C. E. Tosatto and Gustavo Parisi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85014243730&origin=inward},

doi = {10.1371/journal.pcbi.1005398},

year  = {2017},

date = {2017-01-01},

journal = {PLoS Computational Biology},

volume = {13},

number = {2},

publisher = {Public Library of Science},

abstract = {© 2017 Monzon et al.Protein motions are a key feature to understand biological function. Recently, a large-scale analysis of protein conformational diversity showed a positively skewed distribution with a peak at 0.5 Å C-alpha root-mean-square-deviation (RMSD). To understand this distribution in terms of structure-function relationships, we studied a well curated and large dataset of textasciitilde 5,000 proteins with experimentally determined conformational diversity. We searched for global behaviour patterns studying how structure-based features change among the available conformer population for each protein. This procedure allowed us to describe the RMSD distribution in terms of three main protein classes sharing given properties. The largest of these protein subsets (textasciitilde 60%), which we call “rigid” (average RMSD = 0.83 Å), has no disordered regions, shows low conformational diversity, the largest tunnels and smaller and buried cavities. The two additional subsets contain disordered regions, but with differential sequence composition and behaviour. Partially disordered proteins have on average 67% of their conformers with disordered regions, average RMSD = 1.1 Å, the highest number of hinges and the longest disordered regions. In contrast, malleable proteins have on average only 25% of disordered conformers and average RMSD = 1.3 Å, flexible cavities affected in size by the presence of disordered regions and show the highest diversity of cognate ligands. Proteins in each set are mostly non-homologous to each other, share no given fold class, nor functional similarity but do share features derived from their conformer population. These shared features could represent conformational mechanisms related with biological functions.},

note = {Cited by: 42; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85032200484,

title = {Homology modeling in a dynamical world},

author = {Alexander Miguel Monzon and Diego Javier Zea and Cristina Marino-Buslje and Gustavo Parisi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85032200484&origin=inward},

doi = {10.1002/pro.3274},

year  = {2017},

date = {2017-01-01},

journal = {Protein Science},

volume = {26},

number = {11},

pages = {2195-2206},

publisher = {Blackwell Publishing Ltdcustomerservices@oxonblackwellpublishing.com},

abstract = {© 2017 The Protein SocietyA key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.},

note = {Cited by: 20; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85020488911,

title = {Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges},

author = {Binghuang Cai and Biao Li and Nikki Kiga and Janita Thusberg and Timothy Bergquist and Yun-Ching Chen and Noushin Niknafs and Hannah Carter and Collin Tokheim and Violeta Beleva-Guthrie and Christopher Douville and Rohit Bhattacharya and Hui Ting Grace Yeo and Jean Fan and Sohini Sengupta and Dewey Kim and Melissa Cline and Tychele Turner and Mark Diekhans and Jan Zaucha and Lipika R. Pal and Chen Cao and Chen-Hsin Yu and Yizhou Yin and Marco Carraro and Manuel Giollo and Carlo Ferrari and Emanuela Leonardi and Silvio C. E. Tosatto and Jason Bobe and Madeleine Ball and Roger A. Hoskins and Susanna Repo and George Church and Steven E. Brenner and John Moult and Julian Gough and Mario Stanke and Rachel Karchin and Sean D. Mooney},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85020488911&origin=inward},

doi = {10.1002/humu.23265},

year  = {2017},

date = {2017-01-01},

journal = {Human Mutation},

volume = {38},

number = {9},

pages = {1266-1276},

publisher = {John Wiley and Sons Inc},

abstract = {© 2017 Wiley Periodicals, Inc.The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.},

note = {Cited by: 12; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85039065468,

title = {Novel interactions of the von Hippel-Lindau (pVHL) tumor suppressor with the CDKN1 family of cell cycle inhibitors},

author = {Giovanni Minervini and Raffaele Lopreiato and Raissa Bortolotto and Antonella Falconieri and Geppo Sartori and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85039065468&origin=inward},

doi = {10.1038/srep46562},

year  = {2017},

date = {2017-01-01},

journal = {Scientific Reports},

volume = {7},

publisher = {Nature Publishing GroupHoundmillsBasingstoke, HampshireRG21 6XS},

abstract = {© The Author(s) 2017.Germline inactivation of the von Hippel-Lindau (VHL) tumor suppressor predisposes patients to develop different highly vascularized cancers. pVHL targets the hypoxia-inducible transcription factor (HIF-1α) for degradation, modulating the activation of various genes involved in hypoxia response. Hypoxia plays a relevant role in regulating cell cycle progression, inducing growth arrest in cells exposed to prolonged oxygen deprivation. However, the exact molecular details driving this transition are far from understood. Here, we present novel interactions between pVHL and the cyclin-dependent kinase inhibitor family CDKN1 (p21, p27 and p57). Bioinformatics analysis, yeast two-hybrid screening and co-immunoprecipitation assays were used to predict, dissect and validate the interactions. We found that the CDKN1 proteins share a conserved region mimicking the HIF-1α motif responsible for pVHL binding. Intriguingly, a p27 site-specific mutation associated to cancer is shown to modulate this novel interaction. Our findings suggest a new connection between the pathways regulating hypoxia and cell cycle progression.},

note = {Cited by: 6; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85022033200,

title = {Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges},

author = {Roxana Daneshjou and Yanran Wang and Yana Bromberg and Samuele Bovo and Pier L Martelli and Giulia Babbi and Pietro Di Lena and Rita Casadio and Matthew Edwards and David Gifford and David T Jones and Laksshman Sundaram and Rajendra Rana Bhat and Xiaolin Li and Lipika R. Pal and Kunal Kundu and Yizhou Yin and John Moult and Yuxiang Jiang and Vikas Pejaver and Kymberleigh A. Pagel and Biao Li and Sean D. Mooney and Predrag Radivojac and Sohela Shah and Marco Carraro and Alessandra Gasparini and Emanuela Leonardi and Manuel Giollo and Carlo Ferrari and Silvio C E Tosatto and Eran Bachar and Johnathan R. Azaria and Yanay Ofran and Ron Unger and Abhishek Niroula and Mauno Vihinen and Billy Chang and Maggie H Wang and Andre Franke and Britt-Sabina Petersen and Mehdi Pirooznia and Peter Zandi and Richard McCombie and James B. Potash and Russ B. Altman and Teri E. Klein and Roger A. Hoskins and Susanna Repo and Steven E. Brenner and Alexander A. Morgan},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85022033200&origin=inward},

doi = {10.1002/humu.23280},

year  = {2017},

date = {2017-01-01},

journal = {Human Mutation},

volume = {38},

number = {9},

pages = {1182-1192},

publisher = {John Wiley and Sons Inc},

abstract = {© 2017 Wiley Periodicals, Inc.Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype–phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype–phenotype relationships.},

note = {Cited by: 37; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84953293603,

title = {Downregulation of GABAA Receptor Recycling Mediated by HAP1 Contributes to Neuronal Death in In Vitro Brain Ischemia},

author = {Miranda Mele and Maria Cristina Aspromonte and Carlos B. Duarte},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84953293603&origin=inward},

doi = {10.1007/s12035-015-9661-9},

year  = {2017},

date = {2017-01-01},

journal = {Molecular Neurobiology},

volume = {54},

number = {1},

pages = {45-57},

publisher = {Humana Press Inc.humana@humanapr.com},

abstract = {© 2016, Springer Science+Business Media New York.Downregulation of GABAergic synaptic transmission contributes to the increase in overall excitatory activity in the ischemic brain. A reduction of GABAA receptor (GABAAR) surface expression partly accounts for this decrease in inhibitory activity, but the mechanisms involved are not fully elucidated. In this work, we investigated the alterations in GABAAR trafficking in cultured rat hippocampal neurons subjected to oxygen/glucose deprivation (OGD), an in vitro model of global brain ischemia, and their impact in neuronal death. The traffic of GABAAR was evaluated after transfection of hippocampal neurons with myc-tagged GABAAR β3 subunits. OGD decreased the rate of GABAAR β3 subunit recycling and reduced the interaction of the receptors with HAP1, a protein involved in the recycling of the receptors. Furthermore, OGD induced a calpain-mediated cleavage of HAP1. Transfection of hippocampal neurons with HAP1A or HAP1B isoforms reduced the OGD-induced decrease in surface expression of GABAAR β3 subunits, and HAP1A maintained the rate of receptor recycling. Furthermore, transfection of hippocampal neurons with HAP1 significantly decreased OGD-induced cell death. These results show a key role for HAP1 protein in the downmodulation of GABAergic neurotransmission during cerebral ischemia, which contributes to neuronal demise.},

note = {Cited by: 24},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85011277975,

title = {Desmoplasia influenced recurrence of disease and mortality in stage III colorectal cancer within five years after surgery and adjuvant therapy},

author = {Maddalena Zippi and Giorgio De Toma and Giovanni Minervini and Claudio Cassieri and Roberta Pica and Diodoro Colarusso and Simon Stock and Pietro Crispino},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85011277975&origin=inward},

doi = {10.4103/1319-3767.199114},

year  = {2017},

date = {2017-01-01},

journal = {Saudi Journal of Gastroenterology},

volume = {23},

number = {1},

pages = {39-44},

publisher = {Medknow PublicationsB9, Kanara Business Centre, off Link Road, Ghatkopar (E)Mumbai400 075},

abstract = {Background/Aims: In patients with colon cancer who undergo resection for potential cure, 40-60% have advanced locoregional disease (stage III). Those who are suitable for adjuvant treatment had a definite disease-free-survival benefit. The aim of the present study was to demonstrate whether the presence of desmoplasia influenced the mortality rate of stage III colorectal cancer (CRC) within 5 years from the surgery and adjuvant therapy. Patients and Methods: Sixty-five patients with stage III CRC underwent resection and adjuvant therapy. Qualitative categorization of desmoplasia was obtained using Ueno's stromal CRC classification. Desmoplasia was related to mortality using Spearman correlation and stratified with other histological variables (inflammation, grading) that concurred to the major determinant of malignancy (venous invasion and lymph nodes) using the Chi-square test. Result: The 5-year survival rate was 65% and the relapse rate was 37%. The mortality rate in patients with immature desmoplasia was 86%, 27% in intermediate desmoplasia, and 0% in mature desmoplasia (Spearman correlation coefficient: -0.572},

note = {Cited by: 20; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85021350985,

title = {FELLS: Fast estimator of latent local structure},

author = {Damiano Piovesan and Ian Walsh and Giovanni Minervini and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85021350985&origin=inward},

doi = {10.1093/bioinformatics/btx085},

year  = {2017},

date = {2017-01-01},

journal = {Bioinformatics},

volume = {33},

number = {12},

pages = {1889-1891},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author 2017. Published by Oxford University Press. All rights reserved.Motivation: The behavior of a protein is encoded in its sequence, which can be used to predict distinct features such as secondary structure, intrinsic disorder or amphipathicity. Integrating these and other features can help explain the context-dependent behavior of proteins. However, most tools focus on a single aspect, hampering a holistic understanding of protein structure. Here, we present Fast Estimator of Latent Local Structure (FELLS) to visualize structural features from the protein sequence. FELLS provides disorder, aggregation and low complexity predictions as well as estimated local propensities including amphipathicity. A novel fast estimator of secondary structure (FESS) is also trained to provide a fast response. The calculations required for FELLS are extremely fast and suited for large-scale analysis while providing a detailed analysis of difficult cases. Availability and Implementation: The FELLS web server is available from URL: http://protein.bio.unipd.it/fells/. The server also exposes RESTful functionality allowing programmatic prediction requests. An executable version of FESS for Linux can be downloaded from URL: protein.bio.unipd.it/download/.},

note = {Cited by: 56; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85019671065,

title = {MobiDB-lite: Fast and highly specific consensus prediction of intrinsic disorder in proteins},

author = {Marco Necci and Damiano Piovesan and Zsuzsanna Dosztanyi and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85019671065&origin=inward},

doi = {10.1093/bioinformatics/btx015},

year  = {2017},

date = {2017-01-01},

journal = {Bioinformatics},

volume = {33},

number = {9},

pages = {1402-1404},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author 2017. Published by Oxford University Press. All rights reserved.Motivation: Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains. Results: Here, we have focused on providing a single consensus-based prediction, MobiDB-lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases.},

note = {Cited by: 151; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84995791171,

title = {Correct machine learning on protein sequences: A peer-reviewing perspective},

author = {Ian Walsh and Gianluca Pollastri and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84995791171&origin=inward},

doi = {10.1093/bib/bbv082},

year  = {2016},

date = {2016-01-01},

journal = {Briefings in Bioinformatics},

volume = {17},

number = {5},

pages = {831-840},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author 2015. Published by Oxford University Press.Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning.},

note = {Cited by: 52; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84959159515,

title = {Identification of repetitive units in protein structures with ReUPred},

author = {Layla Hirsh and Damiano Piovesan and Lisanna Paladin and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84959159515&origin=inward},

doi = {10.1007/s00726-016-2187-2},

year  = {2016},

date = {2016-01-01},

journal = {Amino Acids},

volume = {48},

number = {6},

pages = {1391-1400},

publisher = {Springer-Verlag Wienmichaela.bolli@springer.at},

abstract = {© 2016, Springer-Verlag Wien.Over the last decade, numerous studies have demonstrated the fundamental importance of tandem repeat (TR) proteins in many biological processes. A plethora of new repeat structures have also been solved. The recently published RepeatsDB provides information on TR proteins. However, a detailed structural characterization of repetitive elements is largely missing, as repeat unit annotation is manually curated and currently covers only 3 % of the bona fide TR proteins. Repeat Protein Unit Predictor (ReUPred) is a novel method for the fast automatic prediction of repeat units and repeat classification using an extensive Structure Repeat Unit Library (SRUL) derived from RepeatsDB. ReUPred uses an iterative structural search against the SRUL to find repetitive units. On a test set of solenoid proteins, ReUPred is able to correctly detect 92 % of the proteins. Unlike previous methods, it is also able to correctly classify solenoid repeats in 89 % of cases. It also outperforms two recent state-of-the-art methods for the repeat unit identification problem. The accurate prediction of repeat units increases the number of annotated repeat units by an order of magnitude compared to the sequence-based Pfam classification. ReUPred is implemented in Python for Linux and freely available from the URL: http://protein.bio.unipd.it/reupred/.},

note = {Cited by: 14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84970969226,

title = {CoDNaS 2.0: A comprehensive database of protein conformational diversity in the native state},

author = {Alexander Miguel Monzon and Cristian Oscar Rohr and María Silvina Fornasari and Gustavo Parisi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84970969226&origin=inward},

doi = {10.1093/database/baw038},

year  = {2016},

date = {2016-01-01},

journal = {Database},

volume = {2016},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author(s) 2016. Published by Oxford University Press.CoDNaS (conformational diversity of the native state) is a protein conformational diversity database. Conformational diversity describes structural differences between conformers that define the native state of proteins. It is a key concept to understand protein function and biological processes related to protein functions. CoDNaS offers a well curated database that is experimentally driven, thoroughly linked, and annotated. CoDNaS facilitates the extraction of key information on small structural differences based on protein movements. CoDNaS enables users to easily relate the degree of conformational diversity with physical, chemical and biological properties derived from experiments on protein structure and biological characteristics. The new version of CoDNaS includes 70% of all available protein structures, and new tools have been added that run sequence searches, display structural flexibility profiles and allow users to browse the database for different structural classes. These tools facilitate the exploration of protein conformational diversity and its role in protein function.},

note = {Cited by: 54; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84954534031,

title = {Computational analysis of prolyl hydroxylase domain-containing protein 2 (PHD2) mutations promoting polycythemia insurgence in humans},

author = {Giovanni Minervini and Federica Quaglia and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84954534031&origin=inward},

doi = {10.1038/srep18716},

year  = {2016},

date = {2016-01-01},

journal = {Scientific Reports},

volume = {6},

publisher = {Nature Publishing GroupHoundmillsBasingstoke, HampshireRG21 6XS},

abstract = {Idiopathic erythrocytosis is a rare disease characterized by an increase in red blood cell mass due to mutations in proteins of the oxygen-sensing pathway, such as prolyl hydroxylase 2 (PHD2). Here, we present a bioinformatics investigation of the pathological effect of twelve PHD2 mutations related to polycythemia insurgence. We show that few mutations impair the PHD2 catalytic site, while most localize to non-enzymatic regions. We also found that most mutations do not overlap the substrate recognition site, suggesting a novel PHD2 binding interface. After a structural analysis of both binding partners, we suggest that this novel interface is responsible for PHD2 interaction with the LIMD1 tumor suppressor.},

note = {Cited by: 10; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84982105552,

title = {VHLdb: A database of von Hippel-Lindau protein interactors and mutations},

author = {Francesco Tabaro and Giovanni Minervini and Faiza Sundus and Federica Quaglia and Emanuela Leonardi and Damiano Piovesan and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84982105552&origin=inward},

doi = {10.1038/srep31128},

year  = {2016},

date = {2016-01-01},

journal = {Scientific Reports},

volume = {6},

publisher = {Nature Publishing GroupHoundmillsBasingstoke, HampshireRG21 6XS},

abstract = {© The Author(s) 2016.Mutations in von Hippel-Lindau tumor suppressor protein (pVHL) predispose to develop tumors affecting specific target organs, such as the retina, epididymis, adrenal glands, pancreas and kidneys. Currently, more than 400 pVHL interacting proteins are either described in the literature or predicted in public databases. This data is scattered among several different sources, slowing down the comprehension of pVHL's biological role. Here we present VHLdb, a novel database collecting available interaction and mutation data on pVHL to provide novel integrated annotations. In VHLdb, pVHL interactors are organized according to two annotation levels, manual and automatic. Mutation data are easily accessible and a novel visualization tool has been implemented. A user-friendly feedback function to improve database content through community-driven curation is also provided. VHLdb presently contains 478 interactors, of which 117 have been manually curated, and 1,074 mutations. This makes it the largest available database for pVHL-related information.},

note = {Cited by: 32; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84951850515,

title = {Assessment of phenolic herbicide toxicity and mode of action by different assays},

author = {Cinzia Bettiol and Stefania De Vettori and Giovanni Minervini and Elisa Zuccon and Davide Marchetto and Annamaria Volpi Ghirardini and Emanuele Argese},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84951850515&origin=inward},

doi = {10.1007/s11356-015-5958-5},

year  = {2016},

date = {2016-01-01},

journal = {Environmental Science and Pollution Research},

volume = {23},

number = {8},

pages = {7398-7408},

publisher = {Springer Verlagservice@springer.de},

abstract = {© 2015, Springer-Verlag Berlin Heidelberg.A phytotoxicity assay based on seed germination/root elongation has been optimized and used to evaluate the toxic effects of some phenolic herbicides. The method has been improved by investigating the influence of experimental conditions. Lepidium sativum was chosen as the most suitable species, showing high germinability, good repeatability of root length measurements, and low sensitivity to seed pretreatment. DMSO was the most appropriate solvent carrier for less water-soluble compounds. Three dinitrophenols and three hydroxybenzonitriles were tested: dinoterb, DNOC, 2,4-dinitrophenol, chloroxynil, bromoxynil, and ioxynil. Toxicity was also determined using the Vibrio fischeri Microtox® test, and a highly significant correlation was found between EC50 values obtained by the two assays. Dinoterb was the most toxic compound. The toxicity of hydroxybenzonitriles followed the order: ioxynil >bromoxynil >chloroxynil; L. sativum exhibited a slightly higher sensitivity than V. fischeri to these compounds. A QSAR analysis highlighted the importance of hydrophobic, electronic, and hydrogen-bonding interactions, in accordance with a mechanism of toxic action based on protonophoric uncoupling of oxidative phosphorylation. The results suggest that the seed germination/root elongation assay with L. sativum is a valid tool for the assessment of xenobiotic toxicity and can be recommended as part of a test battery.},

note = {Cited by: 24; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84962061561,

title = {Evolutionary Conserved Positions Define Protein Conformational Diversity},

author = {Tadeo E. Saldaño and Alexander M. Monzon and Gustavo Parisi and Sebastian Fernandez-Alberti},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84962061561&origin=inward},

doi = {10.1371/journal.pcbi.1004775},

year  = {2016},

date = {2016-01-01},

journal = {PLoS Computational Biology},

volume = {12},

number = {3},

publisher = {Public Library of Science},

abstract = {© 2016 Saldaño et al.Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.},

note = {Cited by: 22; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84963973482,

title = {Disorder transitions and conformational diversity cooperatively modulate biological function in proteins},

author = {Diego Javier Zea and Alexander Miguel Monzon and Claudia Gonzalez and María Silvina Fornasari and Silvio C. E. Tosatto and Gustavo Parisi},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84963973482&origin=inward},

doi = {10.1002/pro.2931},

year  = {2016},

date = {2016-01-01},

journal = {Protein Science},

volume = {25},

number = {6},

pages = {1138-1146},

publisher = {Blackwell Publishing Ltdcustomerservices@oxonblackwellpublishing.com},

abstract = {© 2016 The Protein Society.Structural differences between conformers sustain protein biological function. Here, we studied in a large dataset of 745 intrinsically disordered proteins, how ordered-disordered transitions modulate structural differences between conformers as derived from crystallographic data. We found that almost 50% of the proteins studied show no transitions and have low conformational diversity while the rest show transitions and a higher conformational diversity. In this last subset, 60% of the proteins become more ordered after ligand binding, while 40% more disordered. As protein conformational diversity is inherently connected with protein function our analysis suggests differences in structure-function relationships related to order-disorder transitions.},

note = {Cited by: 16; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84976872277,

title = {Tools and data services registry: A community effort to document bioinformatics resources},

author = {Jon Ison and Kristoffer Rapacki and Hervé Ménager and Matúš Kalaš and Emil Rydza and Piotr Chmura and Christian Anthon and Niall Beard and Karel Berka and Dan Bolser and Tim Booth and Anthony Bretaudeau and Jan Brezovsky and Rita Casadio and Gianni Cesareni and Frederik Coppens and Michael Cornell and Gianmauro Cuccuru and Kristian Davidsen and Gianluca Della Vedova and Tunca Dogan and Olivia Doppelt-Azeroual and Laura Emery and Elisabeth Gasteiger and Thomas Gatter and Tatyana Goldberg and Marie Grosjean and Björn Gruüing and Manuela Helmer-Citterich and Hans Ienasescu and Vassilios Ioannidis and Martin Closter Jespersen and Rafael Jimenez and Nick Juty and Peter Juvan and Maximilian Koch and Camille Laibe and Jing-Woei Li and Luana Licata and Fabien Mareuil and Ivan Mičetić and Rune Møllegaard Friborg and Sebastien Moretti and Chris Morris and Steffen Möller and Aleksandra Nenadic and Hedi Peterson and Giuseppe Profiti and Peter Rice and Paolo Romano and Paola Roncaglia and Rabie Saidi and Andrea Schafferhans and Veit Schwämmle and Callum Smith and Maria Maddalena Sperotto and Heinz Stockinger and Radka Svobodová Varěková and Silvio C. E. Tosatto and Victor De La Torre and Paolo Uva and Allegra Via and Guy Yachdav and Federico Zambelli and Gert Vriend and Burkhard Rost and Helen Parkinson and Peter Løngreen and Søren Brunak},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84976872277&origin=inward},

doi = {10.1093/nar/gkv1116},

year  = {2016},

date = {2016-01-01},

journal = {Nucleic Acids Research},

volume = {44},

number = {D1},

pages = {D38-D47},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author(s) 2015.Life sciences are yielding huge data sets that underpin scientific discoveries fundamental to improvement in human health, agriculture and the environment. In support of these discoveries, a plethora of databases and tools are deployed, in technically complex and diverse implementations, across a spectrum of scientific disciplines. The corpus of documentation of these resources is fragmented across the Web, with much redundancy, and has lacked a common standard of information. The outcome is that scientists must often struggle to find, understand, compare and use the best resources for the task at hand. Here we present a community-driven curation effort, supported by ELIXIR-the European infrastructure for biological information-that aspires to a comprehensive and consistent registry of information about bioinformatics resources. The sustainable upkeep of this Tools and Data Services Registry is assured by a curation effort driven by and tailored to local needs, and shared amongst a network of engaged partners. As of November 2015, the registry includes 1785 resources, with depositions from 126 individual registrations including 52 institutional providers and 74 individuals. With community support, the registry can become a standard for dissemination of information about bioinformatics resources: we welcome everyone to join us in this common endeavour. The registry is freely available at https://bio.tools.},

note = {Cited by: 113; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84986207718,

title = {An expanded evaluation of protein function prediction methods shows an improvement in accuracy},

author = {Yuxiang Jiang and Tal Ronnen Oron and Wyatt T. Clark and Asma R. Bankapur and Daniel D'Andrea and Rosalba Lepore and Christopher S. Funk and Indika Kahanda and Karin M. Verspoor and Asa Ben-Hur and Da Chen Emily Koo and Duncan Penfold-Brown and Dennis Shasha and Noah Youngs and Richard Bonneau and Alexandra Lin and Sayed M. E. Sahraeian and Pier Luigi Martelli and Giuseppe Profiti and Rita Casadio and Renzhi Cao and Zhaolong Zhong and Jianlin Cheng and Adrian Altenhoff and Nives Skunca and Christophe Dessimoz and Tunca Dogan and Kai Hakala and Suwisa Kaewphan and Farrokh Mehryary and Tapio Salakoski and Filip Ginter and Hai Fang and Ben Smithers and Matt Oates and Julian Gough and Petri Törönen and Patrik Koskinen and Liisa Holm and Ching-Tai Chen and Wen-Lian Hsu and Kevin Bryson and Domenico Cozzetto and Federico Minneci and David T. Jones and Samuel Chapman and Dukka Bkc and Ishita K. Khan and Daisuke Kihara and Dan Ofer and Nadav Rappoport and Amos Stern and Elena Cibrian-Uhalte and Paul Denny and Rebecca E. Foulger and Reija Hieta and Duncan Legge and Ruth C. Lovering and Michele Magrane and Anna N. Melidoni and Prudence Mutowo-Meullenet and Klemens Pichler and Aleksandra Shypitsyna and Biao Li and Pooya Zakeri and Sarah ElShal and Léon-Charles Tranchevent and Sayoni Das and Natalie L. Dawson and David Lee and Jonathan G. Lees and Ian Sillitoe and Prajwal Bhat and Tamás Nepusz and Alfonso E. Romero and Rajkumar Sasidharan and Haixuan Yang and Alberto Paccanaro and Jesse Gillis and Adriana E. Sedeño-Cortés and Paul Pavlidis and Shou Feng and Juan M. Cejuela and Tatyana Goldberg and Tobias Hamp and Lothar Richter and Asaf Salamov and Toni Gabaldon and Marina Marcet-Houben and Fran Supek and Qingtian Gong and Wei Ning and Yuanpeng Zhou and Weidong Tian and Marco Falda and Paolo Fontana and Enrico Lavezzo and Stefano Toppo and Carlo Ferrari and Manuel Giollo and ...},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84986207718&origin=inward},

doi = {10.1186/s13059-016-1037-6},

year  = {2016},

date = {2016-01-01},

journal = {Genome Biology},

volume = {17},

number = {1},

publisher = {BioMed Central Ltd.info@biomedcentral.com},

abstract = {© 2016 The Author(s).Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.},

note = {Cited by: 289; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85014179927,

title = {The RING 2.0 web server for high quality residue interaction networks},

author = {Damiano Piovesan and Giovanni Minervini and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85014179927&origin=inward},

doi = {10.1093/nar/gkw315},

year  = {2016},

date = {2016-01-01},

journal = {Nucleic Acids Research},

volume = {44},

number = {1},

pages = {W367-W374},

publisher = {Oxford University Press},

abstract = {© 2016 Oxford University Press. All rights reserved.Residue interaction networks (RINs) are an alternative way of representing protein structures where nodes are residues and arcs physico–chemical interactions. RINs have been extensively and successfully used for analysing mutation effects, protein folding, domain–domain communication and catalytic activity. Here we present RING 2.0, a new version of the RING software for the identification of covalent and non-covalent bonds in protein structures, including π–π stacking and π–cation interactions. RING 2.0 is extremely fast and generates both intra and inter-chain interactions including solvent and ligand atoms. The generated networks are very accurate and reliable thanks to a complex empirical reparameterization of distance thresholds performed on the entire Protein Data Bank. By default, RING output is generated with optimal parameters but the web server provides an exhaustive interface to customize the calculation. The network can be visualized directly in the browser or in Cytoscape. Alternatively, the RING-Viz script for Pymol allows visualizing the interactions at atomic level in the structure. The web server and RING-Viz, together with an extensive help and tutorial, are available from URL: http://protein.bio.unipd.it/ring.},

note = {Cited by: 343; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84984695671,

title = {Naïve Bayes ant colony optimization for designing high dimensional experiments},

author = {Borrotti and Minervini and De Lucrezia and Poli},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84984695671&origin=inward},

doi = {10.1016/j.asoc.2016.08.018},

year  = {2016},

date = {2016-01-01},

journal = {Applied Soft Computing Journal},

volume = {49},

pages = {259-268},

publisher = {Elsevier Ltd},

abstract = {© 2016 Elsevier B.V.In a large number of experimental problems, high dimensionality of the search area and economical constraints can severely limit the number of experimental points that can be tested. Within these constraints, classical optimization techniques perform poorly, in particular, when little a priori knowledge is available. In this work we investigate the possibility of combining approaches from statistical modeling and bio-inspired algorithms to effectively explore a huge search space, sampling only a limited number of experimental points. To this purpose, we introduce a novel approach, combining ant colony optimization (ACO) and naïve Bayes classifier (NBC) that is, the naïve Bayes ant colony optimization (NACO) procedure. We compare NACO with other similar approaches developing a simulation study. We then derive the NACO procedure with the goal to design artificial enzymes with no sequence homology to the extant one. Our final aim is to mimic the natural fold of 200 amino acids 1AGY serine esterase from Fusarium solani.},

note = {Cited by: 11; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84968752860,

title = {Addressing the role of conformational diversity in protein structure prediction},

author = {Nicolas Palopoli and Alexander Miguel Monzon and Gustavo Parisi and Maria Silvina Fornasari},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84968752860&origin=inward},

doi = {10.1371/journal.pone.0154923},

year  = {2016},

date = {2016-01-01},

journal = {PLoS ONE},

volume = {11},

number = {5},

publisher = {Public Library of Scienceplos@plos.org},

abstract = {© 2016 Palopoli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.},

note = {Cited by: 12; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84996538112,

title = {Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe},

author = {Marco Necci and Damiano Piovesan and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84996538112&origin=inward},

doi = {10.1002/pro.3041},

year  = {2016},

date = {2016-01-01},

journal = {Protein Science},

volume = {25},

number = {12},

pages = {2164-2174},

publisher = {Blackwell Publishing Ltdcustomerservices@oxonblackwellpublishing.com},

abstract = {© 2016 The Protein SocietyIntrinsic disorder (ID) in proteins has been extensively described for the last decade; a large-scale classification of ID in proteins is mostly missing. Here, we provide an extensive analysis of ID in the protein universe on the UniProt database derived from sequence-based predictions in MobiDB. Almost half the sequences contain an ID region of at least five residues. About 9% of proteins have a long ID region of over 20 residues which are more abundant in Eukaryotic organisms and most frequently cover less than 20% of the sequence. A small subset of about 67,000 (out of over 80 million) proteins is fully disordered and mostly found in Viruses. Most proteins have only one ID, with short ID evenly distributed along the sequence and long ID overrepresented in the center. The charged residue composition of Das and Pappu was used to classify ID proteins by structural propensities and corresponding functional enrichment. Swollen Coils seem to be used mainly as structural components and in biosynthesis in both Prokaryotes and Eukaryotes. In Bacteria, they are confined in the nucleoid and in Viruses provide DNA binding function. Coils & Hairpins seem to be specialized in ribosome binding and methylation activities. Globules & Tadpoles bind antigens in Eukaryotes but are involved in killing other organisms and cytolysis in Bacteria. The Undefined class is used by Bacteria to bind toxic substances and mediate transport and movement between and within organisms in Viruses. Fully disordered proteins behave similarly, but are enriched for glycine residues and extracellular structures.},

note = {Cited by: 44; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84994246426,

title = {Secretion-Positive LGI1 Mutations Linked to Lateral Temporal Epilepsy Impair Binding to ADAM22 and ADAM23 Receptors},

author = {Emanuela Dazzo and Emanuela Leonardi and Elisa Belluzzi and Sandro Malacrida and Libero Vitiello and Elisa Greggio and Silvio C. E. Tosatto and Carlo Nobile},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84994246426&origin=inward},

doi = {10.1371/journal.pgen.1006376},

year  = {2016},

date = {2016-01-01},

journal = {PLoS Genetics},

volume = {12},

number = {10},

publisher = {Public Library of Scienceplos@plos.org},

abstract = {© 2016 Dazzo et al.Autosomal dominant lateral temporal epilepsy (ADTLE) is a focal epilepsy syndrome caused by mutations in the LGI1 gene, which encodes a secreted protein. Most ADLTE-causing mutations inhibit LGI1 protein secretion, and only a few secretion-positive missense mutations have been reported. Here we describe the effects of four disease-causing nonsynonymous LGI1 mutations, T380A, R407C, S473L, and R474Q, on protein secretion and extracellular interactions. Expression of LGI1 mutant proteins in cultured cells shows that these mutations do not inhibit protein secretion. This finding likely results from the lack of effects of these mutations on LGI1 protein folding, as suggested by 3D protein modelling. In addition, immunofluorescence and co-immunoprecipitation experiments reveal that all four mutations significantly impair interaction of LGI1 with the ADAM22 and ADAM23 receptors on the cell surface. These results support the existence of a second mechanism, alternative to inhibition of protein secretion, by which ADLTE-causing LGI1 mutations exert their loss-of-function effect extracellularly, and suggest that interactions of LGI1 with both ADAM22 and ADAM23 play an important role in the molecular mechanisms leading to ADLTE.},

note = {Cited by: 19; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84929143784,

title = {The Victor C++ library for protein representation and advanced manipulation},

author = {Layla Hirsh and Damiano Piovesan and Manuel Giollo and Carlo Ferrari and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84929143784&origin=inward},

doi = {10.1093/bioinformatics/btu773},

year  = {2015},

date = {2015-01-01},

journal = {Bioinformatics},

volume = {31},

number = {7},

pages = {1138-1140},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author 2014.Motivation: Protein sequence and structure representation and manipulation require dedicated software libraries to support methods of increasing complexity. Here, we describe the VIrtual Constrution TOol for pRoteins (Victor) C++ library, an open source platform dedicated to enabling inexperienced users to develop advanced tools and gathering contributions from the community. The provided application examples cover statistical energy potentials, profile-profile sequence alignments and ab initio loop modeling. Victor was used over the last 15 years in several publications and optimized for efficiency. It is provided as a GitHub repository with source files and unit tests, plus extensive online documentation, including a Wiki with help files and tutorials, examples and Doxygen documentation.},

note = {Cited by: 5; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84937831206,

title = {Insights into the proline hydroxylase (PHD) family, molecular evolution and its impact on human health},

author = {Giovanni Minervini and Federica Quaglia and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84937831206&origin=inward},

doi = {10.1016/j.biochi.2015.07.009},

year  = {2015},

date = {2015-01-01},

journal = {Biochimie},

volume = {116},

pages = {114-124},

publisher = {Elsevier},

abstract = {© 2015 Elsevier B.V. and Société Française de Biochimie et Biologie Moléculaire (SFBBM).Abstract PHDs (proline hydroxylases) are a small protein family found in all organisms, considered the central regulator of the molecular hypoxia response due to PHDs being completely inactivated under low oxygen concentration. At physiological oxygen concentration, PHDs drive the degradation of the HIF-1α (hypoxia-inducible factor 1-α), which is responsible for upregulating the expression of genes involved in the cellular response to hypoxia. Hypoxia is a common feature of most tumors, in particular during metastasis development. Indeed, cancer reacts by activating pathways promoting new blood vessel formation and activating strategies aimed to improve survival. In this scenario, the PHD family regulates the activation of HIF-1α and cell-cycle regulation. Several PHD mutations were found in cancer patients, underlining their importance for human health. Here, we propose a Bayesian model able to predict the pathological effect of human PHD mutations and their correlation with cancer outcome. The model was developed through an integrative in silico approach, where data collected from the literature has been coupled with sequence evolution and structural analysis. The model was used to assess 135 human PHD variants. Finally, bioinformatics characterization was used to demonstrate how few amino acid changes are able to explain the functional specialization of PHD family members and their physiological role in human health.},

note = {Cited by: 19},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84928914099,

title = {BOOGIE: Predicting blood groups from high throughput sequencing data},

author = {Manuel Giollo and Giovanni Minervini and Marta Scalzotto and Emanuela Leonardi and Carlo Ferrari and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84928914099&origin=inward},

doi = {10.1371/journal.pone.0124579},

year  = {2015},

date = {2015-01-01},

journal = {PLoS ONE},

volume = {10},

number = {4},

publisher = {Public Library of Scienceplos@plos.org},

abstract = {© 2015 Giollo et al.Over the last decade, we have witnessed an incredible growth in the amount of available genotype data due to high throughput sequencing (HTS) techniques. This information may be used to predict phenotypes of medical relevance, and pave the way towards personalized medicine. Blood phenotypes (e.g. ABO and Rh) are a purely genetic trait that has been extensively studied for decades, with currently over thirty known blood groups. Given the public availability of blood group data, it is of interest to predict these phenotypes from HTS data which may translate into more accurate blood typing in clinical practice. Here we propose BOOGIE, a fast predictor for the inference of blood groups from single nucleotide variant (SNV) databases. We focus on the prediction of thirty blood groups ranging from the well known ABO and Rh, to the less studied Junior or Diego. BOOGIE correctly predicted the blood group with 94% accuracy for the Personal Genome Project whole genome profiles where good quality SNV annotation was available. Additionally, our tool produces a high quality haplotype phase, which is of interest in the context of ethnicity-specific polymorphisms or traits. The versatility and simplicity of the analysis make it easily interpretable and allow easy extension of the protocol towards other phenotypes. BOOGIE can be downloaded from URL http://protein.bio.unipd.it/download/.},

note = {Cited by: 32; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84942811339,

title = {Structural protein reorganization and fold emergence investigated through amino acid sequence permutations},

author = {Giovanni Minervini and Alessandro Masiero and Emilio Potenza and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84942811339&origin=inward},

doi = {10.1007/s00726-014-1849-1},

year  = {2015},

date = {2015-01-01},

journal = {Amino Acids},

volume = {47},

number = {1},

pages = {147-152},

publisher = {Springer-Verlag Wienmichaela.bolli@springer.at},

abstract = {© 2014 Springer-Verlag Wien.Correlation between random amino acid sequences and protein folds suggests that proteins autonomously evolved the most stable folds, with stability and function evolving subsequently, suggesting the existence of common protein ancestors from which all modern proteins evolved. To test this hypothesis, we shuffled the sequences of 10 natural proteins and obtained 40 different and apparently unrelated folds. Our results suggest that shuffled sequences are sufficiently stable and may act as a basis to evolve functional proteins. The common secondary structure of modern proteins is well represented by a small set of permuted sequences, which also show the emergence of intrinsic disorder and aggregation-prone stretches of the polypeptide chain.},

note = {Cited by: 2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84923886677,

title = {Conformational diversity and the emergence of sequence signatures during evolution},

author = {Gustavo Parisi and Diego Javier Zea and Alexander Miguel Monzon and Cristina Marino-Buslje},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84923886677&origin=inward},

doi = {10.1016/j.sbi.2015.02.005},

year  = {2015},

date = {2015-01-01},

journal = {Current Opinion in Structural Biology},

volume = {32},

pages = {58-65},

publisher = {Elsevier Ltd},

abstract = {© 2015 Elsevier Ltd.Proteins' native structure is an ensemble of conformers in equilibrium, including all their respective functional states and intermediates. The induced-fit first and the pre-equilibrium theories later, described how structural changes are required to explain the allosteric and cooperative behaviours in proteins, which are key to protein function. The conformational ensemble concept has become a key tool in explaining an endless list of essential protein properties such as function, enzyme and antibody promiscuity, signal transduction, protein-protein recognition, origin of diseases, origin of new protein functions, evolutionary rate and order-disorder transitions, among others. Conformational diversity is encoded by the amino acid sequence and such a signature can be evidenced through evolutionary studies as evolutionary rate, conservation and coevolution.},

note = {Cited by: 34},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84947047197,

title = {Protein function prediction using guilty by association from interaction networks},

author = {Damiano Piovesan and Manuel Giollo and Carlo Ferrari and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84947047197&origin=inward},

doi = {10.1007/s00726-015-2049-3},

year  = {2015},

date = {2015-01-01},

journal = {Amino Acids},

volume = {47},

number = {12},

pages = {2583-2592},

publisher = {Springer-Verlag Wienmichaela.bolli@springer.at},

abstract = {© 2015 Springer-Verlag Wien.Protein function prediction from sequence using the Gene Ontology (GO) classification is useful in many biological problems. It has recently attracted increasing interest, thanks in part to the Critical Assessment of Function Annotation (CAFA) challenge. In this paper, we introduce Guilty by Association on STRING (GAS), a tool to predict protein function exploiting protein-protein interaction networks without sequence similarity. The assumption is that whenever a protein interacts with other proteins, it is part of the same biological process and located in the same cellular compartment. GAS retrieves interaction partners of a query protein from the STRING database and measures enrichment of the associated functional annotations to generate a sorted list of putative functions. A performance evaluation based on CAFA metrics and a fair comparison with optimized BLAST similarity searches is provided. The consensus of GAS and BLAST is shown to improve overall performance. The PPI approach is shown to outperform similarity searches for biological process and cellular compartment GO predictions. Moreover, an analysis of the best practices to exploit protein-protein interaction networks is also provided.},

note = {Cited by: 24},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84938248396,

title = {Isoform-specific interactions of the von Hippel-Lindau tumor suppressor protein},

author = {Giovanni Minervini and Gabriella M. Mazzotta and Alessandro Masiero and Elena Sartori and Samantha Corrà and Emilio Potenza and Rodolfo Costa and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84938248396&origin=inward},

doi = {10.1038/srep12605},

year  = {2015},

date = {2015-01-01},

journal = {Scientific Reports},

volume = {5},

publisher = {Nature Publishing GroupHoundmillsBasingstoke, HampshireRG21 6XS},

abstract = {© 2015, Macmillan Publishers Limited. All rights reserved.Deregulation of the von Hippel-Lindau tumor suppressor protein (pVHL) is considered one of the main causes for malignant renal clear-cell carcinoma (ccRCC) insurgence. In human, pVHL exists in two isoforms, pVHL19 and pVHL30 respectively, displaying comparable tumor suppressor abilities. Mutations of the p53 tumor suppressor gene have been also correlated with ccRCC insurgence and ineffectiveness of treatment. A recent proteomic analysis linked full length pVHL30 with p53 pathway regulation through complex formation with the p14ARF oncosuppressor. The alternatively spliced pVHL19, missing the first 53 residues, lacks this interaction and suggests an asymmetric function of the two pVHL isoforms. Here, we present an integrative bioinformatics and experimental characterization of the pVHL oncosuppressor isoforms. Predictions of the pVHL30 N-terminus three-dimensional structure suggest that it may exist as an ensemble of structured and disordered forms. The results were used to guide Yeast two hybrid experiments to highlight isoform-specific binding properties. We observed that the physical pVHL/p14ARF interaction is specifically mediated by the 53 residue long pVHL30 N-terminal region, suggesting that this N-terminus acts as a further pVHL interaction interface. Of note, we also observed that the shorter pVHL19 isoform shows an unexpected high tendency to form homodimers, suggesting an additional isoform-specific binding specialization.},

note = {Cited by: 23; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84942045189,

title = {Exploration of alternative splicing events in ten different grapevine cultivars},

author = {Emilio Potenza and Milvia Luisa Racchi and Lieven Sterck and Emanuela Coller and Elisa Asquini and Silvio C. E. Tosatto and Riccardo Velasco and Yves Van Peer and Alessandro Cestaro},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84942045189&origin=inward},

doi = {10.1186/s12864-015-1922-5},

year  = {2015},

date = {2015-01-01},

journal = {BMC Genomics},

volume = {16},

number = {1},

publisher = {BioMed Central Ltd.info@biomedcentral.com},

abstract = {© 2015 Potenza et al.Background: The complex dynamics of gene regulation in plants are still far from being fully understood. Among many factors involved, alternative splicing (AS) in particular is one of the least well documented. For many years, AS has been considered of less relevant in plants, especially when compared to animals, however, since the introduction of next generation sequencing techniques the number of plant genes believed to be alternatively spliced has increased exponentially. Results: Here, we performed a comprehensive high-throughput transcript sequencing of ten different grapevine cultivars, which resulted in the first high coverage atlas of the grape berry transcriptome. We also developed findAS, a software tool for the analysis of alternatively spliced junctions. We demonstrate that at least 44 % of multi-exonic genes undergo AS and a large number of low abundance splice variants is present within the 131.622 splice junctions we have annotated from Pinot noir. Conclusions: Our analysis shows that textasciitilde 70 % of AS events have relatively low expression levels, furthermore alternative splice sites seem to be enriched near the constitutive ones in some extent showing the noise of the splicing mechanisms. However, AS seems to be extensively conserved among the 10 cultivars.},

note = {Cited by: 20; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84928995861,

title = {Comprehensive large-scale assessment of intrinsic protein disorder},

author = {Ian Walsh and Manuel Giollo and Tomas Di Domenico and Carlo Ferrari and Olav Zimmermann and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84928995861&origin=inward},

doi = {10.1093/bioinformatics/btu625},

year  = {2015},

date = {2015-01-01},

journal = {Bioinformatics},

volume = {31},

number = {2},

pages = {201-208},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author 2014. Published by Oxford University Press. All rights reserved.Motivation: Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.g. Protein Data Bank (PDB) chains. MobiDB has only recently started to collect disorder annotations from multiple experimental structures. Results: MobiDB annotates disorder for UniProt sequences, allowing us to conduct the first large-scale assessment of fast disorder predictors on 25 833 different sequences with X-ray crystallographic structures. In addition to a comprehensive ranking of predictors, this analysis produced the following interesting observations. (i) The predictors cluster according to their disorder definition, with a consensus giving more confidence. (ii) Previous assessments appear over-reliant on data annotated at the PDB chain level and performance is lower on entire UniProt sequences. (iii) Long disordered regions are harder to predict. (iv) Depending on the structural and functional types of the proteins, differences in prediction performance of up to 10%are observed.},

note = {Cited by: 140; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84930518283,

title = {Protein sequence annotation by means of community detection},

author = {Giuseppe Profiti and Damiano Piovesan and Pier Luigi Martelli and Piero Fariselli and Rita Casadio},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84930518283&origin=inward},

doi = {10.2174/157489361002150518122954},

year  = {2015},

date = {2015-01-01},

journal = {Current Bioinformatics},

volume = {10},

number = {2},

pages = {139-143},

publisher = {Bentham Science Publishers},

abstract = {© 2015 Bentham Science PublishersIn the postgenomic era different electronic procedures are available for protein sequence annotation, the process of enriching, with structural and functional features, any protein after electronic translation from its correspondent gene or mRNA. The demand of reliable annotation systems is particularly urgent given the volume of genomic data that are daily produced by next generation sequencing machines. In this paper we present a procedure that enhances the annotation performance of the previously described Bologna Annotation Resource (BAR+). BAR is based on clustering of the graphs representing the similarity between a large number of protein sequences and here we apply community detection algorithms to detect subclusters within any graph. When the cluster is endowed with specific Gene Ontology terms associated both to Biological Process and Molecular Function, the application of our procedure allows a fine tuning of the annotation process and generates subclusters where proteins sharing strictly related GO terms are grouped.},

note = {Cited by: 0},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84946098212,

title = {MobiDB 2.0: An improved database of intrinsically disordered and mobile proteins},

author = {Emilio Potenza and Tomás Di Domenico and Ian Walsh and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84946098212&origin=inward},

doi = {10.1093/nar/gku982},

year  = {2015},

date = {2015-01-01},

journal = {Nucleic Acids Research},

volume = {43},

number = {D1},

pages = {D315-D320},

publisher = {Oxford University Pressjnl.info@oup.co.uk},

abstract = {© The Author(s) 2014.MobiDB (http://mobidb.bio.unipd.it/) is a database of intrinsically disordered and mobile proteins. Intrinsically disordered regions are key for the function of numerous proteins. Here we provide a new version of MobiDB, a centralized source aimed at providing the most complete picture on different flavors of disorder in protein structures covering all UniProt sequences (currently over 80 million). The database features three levels of annotation: manually curated, indirect and predicted. Manually curated data is extracted from the DisProt database. Indirect data is inferred from PDB structures that are considered an indication of intrinsic disorder. The 10 predictors currently included (three ESpritz flavors, two IUPred flavors, two DisEMBL flavors, GlobPlot, VSL2b and JRONN) enable MobiDB to provide disorder annotations for every protein in absence of more reliable data. The new version also features a consensus annotation and classification for long disordered regions. In order to complement the disorder annotations, MobiDB features additional annotations from external sources. Annotations from the UniProt database include post-translational modifications and linear motifs. Pfam annotations are displayed in graphical form and are link-enabled, allowing the user to visit the corresponding Pfam page for further information. Experimental protein-protein interactions from STRING are also classified for disorder content.},

note = {Cited by: 167; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:85005917703,

title = {Heterozygous Reelin Mutations Cause Autosomal-Dominant Lateral Temporal Epilepsy},

author = {Emanuela Dazzo and Manuela Fanciulli and Elena Serioli and Giovanni Minervini and Patrizia Pulitano and Simona Binelli and Carlo Di Bonaventura and Concetta Luisi and Elena Pasini and Salvatore Striano and Pasquale Striano and Giangennaro Coppola and Angela Chiavegato and Slobodanka Radovic and Alessandro Spadotto and Sergio Uzzau and Angela La Neve and Anna Teresa Giallonardo and Oriano Mecarelli and Silvio C. E. Tosatto and Ruth Ottman and Roberto Michelucci and Carlo Nobile},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-85005917703&origin=inward},

doi = {10.1016/j.ajhg.2015.04.020},

year  = {2015},

date = {2015-01-01},

journal = {American Journal of Human Genetics},

volume = {96},

number = {6},

pages = {992-1000},

publisher = {Cell Presssubs@cell.com},

abstract = {© 2015 The American Society of Human GeneticsAutosomal-dominant lateral temporal epilepsy (ADLTE) is a genetic epilepsy syndrome clinically characterized by focal seizures with prominent auditory symptoms. ADLTE is genetically heterogeneous, and mutations in LGI1 account for fewer than 50% of affected families. Here, we report the identification of causal mutations in reelin (RELN) in seven ADLTE-affected families without LGI1 mutations. We initially investigated 13 ADLTE-affected families by performing SNP-array linkage analysis and whole-exome sequencing and identified three heterozygous missense mutations co-segregating with the syndrome. Subsequent analysis of 15 small ADLTE-affected families revealed four additional missense mutations. 3D modeling predicted that all mutations have structural effects on protein-domain folding. Overall, RELN mutations occurred in 7/40 (17.5%) ADLTE-affected families. RELN encodes a secreted protein, Reelin, which has important functions in both the developing and adult brain and is also found in the blood serum. We show that ADLTE-related mutations significantly decrease serum levels of Reelin, suggesting an inhibitory effect of mutations on protein secretion. We also show that Reelin and LGI1 co-localize in a subset of rat brain neurons, supporting an involvement of both proteins in a common molecular pathway underlying ADLTE. Homozygous RELN mutations are known to cause lissencephaly with cerebellar hypoplasia. Our findings extend the spectrum of neurological disorders associated with RELN mutations and establish a link between RELN and LGI1, which play key regulatory roles in both the developing and adult brain.},

note = {Cited by: 100; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}