PUBLICATIONS

@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: 169; 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: 59; Open Access},

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: 21; 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: 56; 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: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: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: 57; 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: 39; 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: 29; 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: 24; 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: 17; 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: 122; 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: 317; 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: 364; 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: 14; 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: 46; 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: 21; 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: 37; 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: 36},

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: 27},

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: 25; 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: 22; 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: 144; 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: 174; 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: 104; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84979866841,

title = {INGA: Protein function prediction combining interaction networks, domain assignments and sequence similarity},

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

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84979866841&origin=inward},

doi = {10.1093/nar/gkv523},

year  = {2015},

date = {2015-01-01},

journal = {Nucleic Acids Research},

volume = {43},

number = {W1},

pages = {W134-W140},

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

abstract = {© 2015 The Author(s).Identifying protein functions can be useful for numerous applications in biology. The prediction of gene ontology (GO) functional terms from sequence remains however a challenging task, as shown by the recent CAFA experiments. Here we present INGA, a web server developed to predict protein function from a combination of three orthogonal approaches. Sequence similarity and domain architecture searches are combined with protein-protein interaction network data to derive consensus predictions for GO terms using functional enrichment. The INGA server can be queried both programmatically through RESTful services and through a web interface designed for usability. The latter provides output supporting the GO term predictions with the annotating sequences. INGA is validated on the CAFA-1 data set and was recently shown to perform consistently well in the CAFA-2 blind test. The INGA web server is available from URL: http://protein.bio.unipd.it/inga.},

note = {Cited by: 72; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84942198879,

title = {Unfoldome variation upon plant-pathogen interactions: strawberry infection by Colletotrichum acutatum},

author = {Elena Baraldi and Emanuela Coller and Lisa Zoli and Alessandro Cestaro and Silvio C. E. Tosatto and Barbara Zambelli},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84942198879&origin=inward},

doi = {10.1007/s11103-015-0353-7},

year  = {2015},

date = {2015-01-01},

journal = {Plant Molecular Biology},

volume = {89},

number = {1-2},

pages = {49-65},

publisher = {Kluwer Academic Publishersrbk@louisiana.edu},

abstract = {© 2015, Springer Science+Business Media Dordrecht.Intrinsically disordered proteins (IDPs) are proteins that lack secondary and/or tertiary structure under physiological conditions. These proteins are very abundant in eukaryotic proteomes and play crucial roles in all molecular mechanisms underlying the response to environmental challenges. In plants, different IDPs involved in stress response have been identified and characterized. Nevertheless, a comprehensive evaluation of protein disorder in plant proteomes under abiotic or biotic stresses is not available so far. In the present work the transcriptome dataset of strawberry (Fragariaxananassa) fruits interacting with the fungal pathogen Colletotrichum acutatum was actualized onto the woodland strawberry (Fragaria vesca) genome. The obtained cDNA sequences were translated into protein sequences, which were subsequently subjected to disorder analysis. The results, providing the first estimation of disorder abundance associated to plant infection, showed that the proteome activated in the strawberry red fruit during the active fungal propagation is remarkably depleted in disorder. On the other hand, in the resistant white fruit, no significant disorder reduction is observed in the proteins expressed in response to fungal infection. Four representative proteins, FvSMP, FvPRKRIP, FvPCD-4 and FvFAM32A-like, predicted as mainly disordered and never experimentally characterized before, were isolated, and the absence of structure was validated at the secondary and tertiary level using circular dichroism and differential scanning fluorimetry. Their quaternary structure was also established using light scattering. The results are discussed considering the role of protein disorder in plant defense.},

note = {Cited by: 4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84947284654,

title = {Comparison of protein repeat classifications based on structure and sequence families},

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

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84947284654&origin=inward},

doi = {10.1042/BST20150079},

year  = {2015},

date = {2015-01-01},

journal = {Biochemical Society Transactions},

volume = {43},

pages = {832-837},

publisher = {Portland Press Ltd},

abstract = {© 2015 Authors; published by Portland Press Limited.Tandem repeats (TR) in proteins are common in nature and have several unique functions. They come in various forms that are frequently difficult to recognize from a sequence. A previously proposed structural classification has been recently implemented in the RepeatsDB database. This defines five main classes, mainly based on repeat unit length, with subclasses representing specific folds. Sequence-based classifications, such as Pfam, provide an alternative classification based on evolutionarily conserved repeat families. Here, we discuss a detailed comparison between the structural classes in RepeatsDB and the corresponding Pfam repeat families and clans. Most instances are found to map one-to-one between structure and sequence. Some notable exceptions such as leucine-rich repeats (LRRs) and α-solenoids are discussed.},

note = {Cited by: 10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84959549790,

title = {Structural in silico dissection of the collagen v interactome to identify genotype-phenotype correlations in classic Ehlers-Danlos Syndrome (EDS)},

author = {Lisanna Paladin and Silvio C. E. Tosatto and Giovanni Minervini},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84959549790&origin=inward},

doi = {10.1016/j.febslet.2015.11.022},

year  = {2015},

date = {2015-01-01},

journal = {FEBS Letters},

volume = {589},

number = {24},

pages = {3871-3878},

publisher = {Wiley Blackwell},

abstract = {© 2015 Federation of European Biochemical Societies.Collagen V mutations are associated with Elhers-Danlos syndrome (EDS), a group of heritable collagenopathies. Collagen V structure is not available and the disease-causing mechanism is unclear. To address this issue, we manually curated missense mutations suspected to promote classic type EDS (cEDS) insurgence from the literature and performed a genotype-phenotype correlation study. Further, we generated a homology model of the collagen V triple helix to evaluate the pathogenic effects. The resulting structure was used to map known protein-protein interactions enriched with in silico predictions. An interaction network model for collagen V was created. We found that cEDS heterogeneous manifestations may be explained by the involvement in two different extracellular matrix pathways, related to cell adhesion and tissue repair or cell differentiation, growth and apoptosis.},

note = {Cited by: 14; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84902324935,

title = {Design and analysis of a Petri Net model of the Von Hippel-Lindau (VHL) tumor suppressor interaction network},

author = {Giovanni Minervini and Elisabetta Panizzoni and Manuel Giollo and Alessandro Masiero and Carlo Ferrari and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84902324935&origin=inward},

doi = {10.1371/journal.pone.0096986},

year  = {2014},

date = {2014-01-01},

journal = {PLoS ONE},

volume = {9},

number = {6},

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

abstract = {Von Hippel-Lindau (VHL) syndrome is a hereditary condition predisposing to the development of different cancer forms, related to germline inactivation of the homonymous tumor suppressor pVHL. The best characterized function of pVHL is the ubiquitination dependent degradation of Hypoxia Inducible Factor (HIF) via the proteasome. It is also involved in several cellular pathways acting as a molecular hub and interacting with more than 200 different proteins. Molecular details of pVHL plasticity remain in large part unknown. Here, we present a novel manually curated Petri Net (PN) model of the main pVHL functional pathways. The model was built using functional information derived from the literature. It includes all major pVHL functions and is able to credibly reproduce VHL syndrome at the molecular level. The reliability of the PN model also allowed in silico knockout experiments, driven by previous model analysis. Interestingly, PN analysis suggests that the variability of different VHL manifestations is correlated with the concomitant inactivation of different metabolic pathways. © 2014 Minervini et al.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84903728406,

title = {Mapping and annotating obesity-related genes in pig and human genomes},

author = {Pier Luigi Martelli and Luca Fontanesi and Damiano Piovesan and Piero Fariselli and Rita Casadio},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84903728406&origin=inward},

doi = {10.2174/09298665113209990053},

year  = {2014},

date = {2014-01-01},

journal = {Protein and Peptide Letters},

volume = {21},

number = {8},

pages = {840-846},

publisher = {Bentham Science PublishersP.O. Box 294Bussum1400 AG},

abstract = {Background. Obesity is a major health problem in both developed and emerging countries. Obesity is a complex disease whose etiology involves genetic factors in strong interplay with environmental determinants and lifestyle. The discovery of genetic factors and biological pathways underlying human obesity is hampered by the difficulty in controlling the genetic background of human cohorts. Animal models are then necessary to further dissect the genetics of obesity. Pig has emerged as one of the most attractive models, because of the similarity with humans in the mechanisms regulating the fat deposition. Results. We collected the genes related to obesity in humans and to fat deposition traits in pig. We localized them on both human and pig genomes, building a map useful to interpret comparative studies on obesity. We characterized the collected genes structurally and functionally with BAR+ and mapped them on KEGG pathways and on STRING protein interaction network. Conclusions. The collected set consists of 361 obesity related genes in human and pig genomes. All genes were mapped on the human genome, and 54 could not be localized on the pig genome (release 2012). Only for 3 human genes there is no counterpart in pig, confirming that this animal is a good model for human obesity studies. Obesity related genes are mostly involved in regulation and signaling processes/pathways and relevant connection emerges between obesity-related genes and diseases such as cancer and infectious diseases. © 2014 Bentham Science Publishers.},

note = {Cited by: 3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84902011183,

title = {CDKN2A Unclassified Variants in Familial Malignant Melanoma: Combining Functional and Computational Approaches for Their Assessment},

author = {Maria Chiara Scaini and Giovanni Minervini and Lisa Elefanti and Paola Ghiorzo and Lorenza Pastorino and Silvia Tognazzo and Simona Agata and Monica Quaggio and Daniela Zullato and Giovanna Bianchi-Scarrà and Marco Montagna and Emma D'Andrea and Chiara Menin and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84902011183&origin=inward},

doi = {10.1002/humu.22550},

year  = {2014},

date = {2014-01-01},

journal = {Human Mutation},

volume = {35},

number = {7},

pages = {828-840},

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

abstract = {CDKN2A codes for two oncosuppressors by alternative splicing of two first exons: p16INK4a and p14ARF. Germline mutations are found in about 40% of melanoma-prone families, and most of them are missense mutations mainly affecting p16INK4a. A growing number of p16INK4a variants of uncertain significance (VUS) are being identified but, unless their pathogenic role can be demonstrated, they cannot be used for identification of carriers at risk. Predicting the effect of these VUS by either a "standard" in silico approach, or functional tests alone, is rather difficult. Here, we report a protocol for the assessment of any p16INK4a VUS, which combines experimental and computational tools in an integrated approach. We analyzed p16INK4a VUS from melanoma patients as well as variants derived through permutation of conserved p16INK4a amino acids. Variants were expressed in a p16INK4a-null cell line (U2-OS) and tested for their ability to block proliferation. In parallel, these VUS underwent in silico prediction analysis and molecular dynamics simulations. Evaluation of in silico and functional data disclosed a high agreement for 15/16 missense mutations, suggesting that this approach could represent a pilot study for the definition of a protocol applicable to VUS in general, involved in other diseases, as well. © 2014 WILEY PERIODICALS, INC.},

note = {Cited by: 17},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84891766568,

title = {RepeatsDB: A database of tandem repeat protein structures},

author = {Tomás Di Domenico and Emilio Potenza and Ian Walsh and Gonzalo Parra and Manuel Giollo and Giovanni Minervini and Damiano Piovesan and Awais Ihsan and Carlo Ferrari and Andrey V. Kajava and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84891766568&origin=inward},

doi = {10.1093/nar/gkt1175},

year  = {2014},

date = {2014-01-01},

journal = {Nucleic Acids Research},

volume = {42},

number = {D1},

abstract = {RepeatsDB (http://repeatsdb.bio.unipd.it/) is a database of annotated tandem repeat protein structures. Tandem repeats pose a difficult problem for the analysis of protein structures, as the underlying sequence can be highly degenerate. Several repeat types haven been studied over the years, but their annotation was done in a case-by-case basis, thus making large-scale analysis difficult. We developed RepeatsDB to fill this gap. Using state-of-the-art repeat detection methods and manual curation, we systematically annotated the Protein Data Bank, predicting 10 745 repeat structures. In all, 2797 structures were classified according to a recently proposed classification schema, which was expanded to accommodate new findings. In addition, detailed annotations were performed in a subset of 321 proteins. These annotations feature information on start and end positions for the repeat regions and units. RepeatsDB is an ongoing effort to systematically classify and annotate structural protein repeats in a consistent way. It provides users with the possibility to access and download high-quality datasets either interactively or programmatically through web services. © 2013 The Author(s). Published by Oxford University Press.},

note = {Cited by: 59; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84904786762,

title = {PASTA 2.0: An improved server for protein aggregation prediction},

author = {Ian Walsh and Flavio Seno and Silvio C. E. Tosatto and Antonio Trovato},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84904786762&origin=inward},

doi = {10.1093/nar/gku399},

year  = {2014},

date = {2014-01-01},

journal = {Nucleic Acids Research},

volume = {42},

number = {W1},

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

abstract = {The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/. © 2014 The Author(s).},

note = {Cited by: 394; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84925286085,

title = {NeEMO: A method using residue interaction networks to improve prediction of protein stability upon mutation},

author = {Manuel Giollo and Alberto J. M. Martin and Ian Walsh and Carlo Ferrari and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84925286085&origin=inward},

doi = {10.1186/1471-2164-15-S4-S7},

year  = {2014},

date = {2014-01-01},

journal = {BMC Genomics},

volume = {15},

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

abstract = {© 2014 Manuel et al.Background: The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes. Results: Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/. Conclusions: NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.},

note = {Cited by: 96; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84898906108,

title = {RUBI: Rapid proteomic-scale prediction of lysine ubiquitination and factors influencing predictor performance},

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

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84898906108&origin=inward},

doi = {10.1007/s00726-013-1645-3},

year  = {2014},

date = {2014-01-01},

journal = {Amino Acids},

volume = {46},

number = {4},

pages = {853-862},

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

abstract = {Post-translational modification of protein lysines was recently shown to be a common feature of eukaryotic organisms. The ubiquitin modification is regarded as a versatile regulatory mechanism with many important cellular roles. Large-scale datasets are becoming available for H. sapiens ubiquitination. However, using current experimental techniques the vast majority of their sites remain unidentified and in silico tools may offer an alternative. Here, we introduce Rapid UBIquitination (RUBI) a sequence-based ubiquitination predictor designed for rapid application on a genome scale. RUBI was constructed using an iterative approach. At each iteration, important factors which influenced performance and its usability were investigated. The final RUBI model has an AUC of 0.868 on a large cross-validation set and is shown to outperform other available methods on independent sets. Predicted intrinsic disorder is shown to be weakly anti-correlated to ubiquitination for the H. sapiens dataset and improves performance slightly. RUBI predicts the number of ubiquitination sites correctly within three sites for ca. 80 % of the tested proteins. The average potentially ubiquitinated proteome fraction is predicted to be at least 25 % across a variety of model organisms, including several thousand possible H. sapiens proteins awaiting experimental characterization. RUBI can accurately predict ubiquitination on unseen examples and has a signal across different eukaryotic organisms. The factors which influenced the construction of RUBI could also be tested in other post-translational modification predictors. One of the more interesting factors is the influence of intrinsic protein disorder on ubiquitinated lysines where residues with low disorder probability are preferred. © 2013 Springer-Verlag Wien.},

note = {Cited by: 35; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84908207041,

title = {Identification of Four Novel PCDH19 Mutations and Prediction of Their Functional Impact},

author = {Emanuela Leonardi and Stefano Sartori and Marilena Vecchi and Elisa Bettella and Roberta Polli and Luca De Palma and Clementina Boniver and Alessandra Murgia},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84908207041&origin=inward},

doi = {10.1111/ahg.12082},

year  = {2014},

date = {2014-01-01},

journal = {Annals of Human Genetics},

volume = {78},

number = {6},

pages = {389-398},

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

abstract = {© 2014 John Wiley & Sons Ltd/University College London.The PCDH19 gene encodes protocadherin-19, a transmembrane protein with six cadherin (EC) domains, containing adhesive interfaces likely to be involved in neuronal connection. Over a hundred mostly private mutations have been identified in girls with epilepsy, with or without intellectual disability (ID). Furthermore, transmitting hemizygous males are devoid of seizures or ID, making it difficult to establish the pathogenic nature of newly identified variants. Here, we describe an integrated approach to evaluate the pathogenicity of four novel PCDH19 mutations. Segregation analysis has been complemented with an in silico analysis of mutation effects at the protein level. Using sequence information, we compared different computational prediction methods. We used homology modeling to build structural models of two PCDH19 EC-domains, and compared wild-type and mutant models to identify differences in residue interactions or biochemical properties of the model surfaces. Our analysis suggests different molecular effects of the novel mutations in exerting their pathogenic role. Two of them interfere with or alter functional residues predicted to mediate ligand or protein binding, one alters the EC-domain folding stability; the frame-shift mutation produces a truncated protein lacking the intracellular domain. Interestingly, the girl carrying the putative loss of function mutation presents the most severe phenotype.},

note = {Cited by: 18; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84906097745,

title = {Evaluation of the steric impact of flavin adenine dinucleotide in Drosophila melanogaster cryptochrome function},

author = {Alessandro Masiero and Simona Aufiero and Giovanni Minervini and Stefano Moro and Rodolfo Costa and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84906097745&origin=inward},

doi = {10.1016/j.bbrc.2014.07.038},

year  = {2014},

date = {2014-01-01},

journal = {Biochemical and Biophysical Research Communications},

volume = {450},

number = {4},

pages = {1606-1611},

publisher = {Academic Press Inc.apjcs@harcourt.com},

abstract = {Photoreceptors are crucial components for circadian rhythm entrainment in animals, plants, fungi and cyanobacteria. Cryptochromes (CRYs) are flavin adenine dinucleotide (FAD) containing photoreceptors, and FAD is responsible for signal transduction, in contrast to photolyases where it promotes DNA-damage repair. In this work, we investigated an alternative role for FAD in CRY. We analyzed the Drosophila melanogaster CRY crystal structure by means of molecular dynamics, elucidating how this large co-factor within the receptor could be crucial for CRY structural stability. The co-factor appears indeed to improve receptor motility, providing steric hindrance. Moreover, multiple sequence alignments revealed that conserved motifs in the C-terminal tail could be necessary for functional stability. © 2014 Elsevier Inc. All rights reserved.},

note = {Cited by: 4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84881520207,

title = {The role of the N-terminal tail for the oligomerization, folding and stability of human frataxin},

author = {Santiago E. Faraj and Leandro Venturutti and Ernesto A. Roman and Cristina B. Marino-Buslje and Astor Mignone and Silvio C. E. Tosatto and José M. Delfino and Javier Santos},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84881520207&origin=inward},

doi = {10.1016/j.fob.2013.07.004},

year  = {2013},

date = {2013-01-01},

journal = {FEBS Open Bio},

volume = {3},

pages = {310-320},

abstract = {The N-terminal stretch of human frataxin (hFXN) intermediate (residues 42-80) is not conserved throughout evolution and, under defined experimental conditions, behaves as a random-coil. Overexpression of hFXN56-210 in Escherichia coli yields a multimer, whereas the mature form of hFXN (hFXN81-210) is monomeric. Thus, cumulative experimental evidence points to the N-terminal moiety as an essential element for the assembly of a high molecular weight oligomer. The secondary structure propensity of peptide 56-81, the moiety putatively responsible for promoting protein-protein interactions, was also studied. Depending on the environment (TFE or SDS), this peptide adopts α-helical or β-strand structure. In this context, we explored the conformation and stability of hFXN56-210. The biophysical characterization by fluorescence, CD and SEC-FPLC shows that subunits are well folded, sharing similar stability to hFXN90-210. However, controlled proteolysis indicates that the N-terminal stretch is labile in the context of the multimer, whereas the FXN domain (residues 81-210) remains strongly resistant. In addition, guanidine hydrochloride at low concentration disrupts intermolecular interactions, shifting the ensemble toward the monomeric form. The conformational plasticity of the N-terminal tail might impart on hFXN the ability to act as a recognition signal as well as an oligomerization trigger. Understanding the fine-tuning of these activities and their resulting balance will bear direct relevance for ultimately comprehending hFXN function. © 2013 The Authors.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84879314610,

title = {How to inherit statistically validated annotation within BAR+ protein clusters},

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

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84879314610&origin=inward},

doi = {10.1186/1471-2105-14-S3-S4},

year  = {2013},

date = {2013-01-01},

journal = {BMC Bioinformatics},

volume = {14},

number = {SUPPL.3},

abstract = {Background: In the genomic era a key issue is protein annotation, namely how to endow protein sequences, upon translation from the corresponding genes, with structural and functional features. Routinely this operation is electronically done by deriving and integrating information from previous knowledge. The reference database for protein sequences is UniProtKB divided into two sections, UniProtKB/TrEMBL which is automatically annotated and not reviewed and UniProtKB/Swiss-Prot which is manually annotated and reviewed. The annotation process is essentially based on sequence similarity search. The question therefore arises as to which extent annotation based on transfer by inheritance is valuable and specifically if it is possible to statistically validate inherited features when little homology exists among the target sequence and its template(s).Results: In this paper we address the problem of annotating protein sequences in a statistically validated manner considering as a reference annotation resource UniProtKB. The test case is the set of 48,298 proteins recently released by the Critical Assessment of Function Annotations (CAFA) organization. We show that we can transfer after validation, Gene Ontology (GO) terms of the three main categories and Pfam domains to about 68% and 72% of the sequences, respectively. This is possible after alignment of the CAFA sequences towards BAR+, our annotation resource that allows discriminating among statistically validated and not statistically validated annotation. By comparing with a direct UniProtKB annotation, we find that besides validating annotation of some 78% of the CAFA set, we assign new and statistically validated annotation to 14.8% of the sequences and find new structural templates for about 25% of the chains, half of which share less than 30% sequence identity to the corresponding template/s.Conclusion: Inheritance of annotation by transfer generally requires a careful selection of the identity value among the target and the template in order to transfer structural and/or functional features. Here we prove that even distantly remote homologs can be safely endowed with structural templates and GO and/or Pfam terms provided that annotation is done within clusters collecting cluster-related protein sequences and where a statistical validation of the shared structural and functional features is possible. © 2013 Piovesan et al.; licensee BioMed Central Ltd.},

note = {Cited by: 7; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84892759581,

title = {SUS-BAR: A database of pig proteins with statistically validated structural and functional annotation},

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

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84892759581&origin=inward},

doi = {10.1093/database/bat065},

year  = {2013},

date = {2013-01-01},

journal = {Database},

volume = {2013},

abstract = {Given the relevance of the pig proteome in different studies, including human complex maladies, a statistical validation of the annotation is required for a better understanding of the role of specific genes and proteins in the complex networks underlying biological processes in the animal. Presently, approximately 80% of the pig proteome is still poorly annotated, and the existence of protein sequences is routinely inferred automatically by sequence alignment towards preexisting sequences. In this article, we introduce SUS-BAR, a database that derives information mainly from UniProt Knowledgebase and that includes 26 206 pig protein sequences. In SUS-BAR, 16 675 of the pig protein sequences are endowed with statistically validated functional and structural annotation. Our statistical validation is determined by adopting a cluster-centric annotation procedure that allows transfer of different types of annotation, including structure and function. Each sequence in the database can be associated with a set of statistically validated Gene Ontologies (GOs) of the three main subontologies (Molecular Function, Biological Process and Cellular Component), with Pfam functional domains, and when possible, with a cluster Hidden Markov Model that allows modelling the 3D structure of the protein. A database search allows some statistics demonstrating the enrichment in both GO and Pfam annotations of the pig proteins as compared with UniProt Knowledgebase annotation. Searching in SUS-BAR allows retrieval of the pig protein annotation for further analysis. The search is also possible on the basis of specific GO terms and this allows retrieval of all the pig sequences participating into a given biological process, after annotation with our system. Alternatively, the search is possible on the basis of structural information, allowing retrieval of all the pig sequences with the same structural characteristics. © The Author(s) 2013. Published by Oxford University Press.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84879800008,

title = {Early-onset epileptic encephalopathy in a girl carrying a truncating mutation of the ARX gene: Rethinking the ARX phenotype in females},

author = {Bettella and Di Rosa and Polli and Leonardi and Tortorella and Sartori and Murgia},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84879800008&origin=inward},

doi = {10.1111/cge.12034},

year  = {2013},

date = {2013-01-01},

journal = {Clinical Genetics},

volume = {84},

number = {1},

pages = {82-85},

abstract = {Severe early-onset epilepsy is due to a number of known causes, although a clear etiology is not identifiable in up to a third of all the cases. Pathogenic sequence variations in the ARX gene have been described almost exclusively in males, whereas heterozygous female relatives, such as mothers, sisters and even grandmothers have been largely reported as asymptomatic or mildly affected. To investigate the pathogenic role of ARX in refractory epilepsy of early onset even in females, we have screened the ARX sequence in a population of 50 female subjects affected with unexplained epileptic encephalopathy with onset in the first year of life. We report the identification of a novel truncating mutation of the coding region of the ARX gene in a girl with a structurally normal brain. Our findings confirm the role of ARX in the pathogenesis of early epilepsy and underline the importance of screening of the ARX gene in both male and female subjects with otherwise unexplained early onset epileptic encephalopathy.© 2012 John Wiley & Sons A/S.},

note = {Cited by: 13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84879373603,

title = {Protein conformational diversity correlates with evolutionary rate},

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

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84879373603&origin=inward},

doi = {10.1093/molbev/mst065},

year  = {2013},

date = {2013-01-01},

journal = {Molecular Biology and Evolution},

volume = {30},

number = {7},

pages = {1500-1503},

abstract = {Native state of proteins is better represented by an ensemble of conformers in equilibrium than by only one structure. The extension of structural differences between conformers characterizes the conformational diversity of the protein. In this study, we found a negative correlation between conformational diversity and protein evolutionary rate. Conformational diversity was expressed as the maximum root mean square deviation (RMSD) between the available conformers in Conformational Diversity of Native State database. Evolutionary rate estimations were calculated using 16 different species compared with human sharing at least 700 orthologous proteins with known conformational diversity extension. The negative correlation found is independent of the protein expression level and comparable in magnitude and sign with the correlation between gene expression level and evolutionary rate. Our findings suggest that the structural constraints underlying protein dynamism, essential for protein function, could modulate protein divergence. © 2013 The Author.},

note = {Cited by: 29; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84893166471,

title = {PANADA: Protein Association Network Annotation, Determination and Analysis},

author = {Alberto J. M. Martin and Ian Walsh and Tomás Di Domenico and Ivan Mičetić and Silvio C. E. Tosatto},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84893166471&origin=inward},

doi = {10.1371/journal.pone.0078383},

year  = {2013},

date = {2013-01-01},

journal = {PLoS ONE},

volume = {8},

number = {11},

abstract = {Increasingly large numbers of proteins require methods for functional annotation. This is typically based on pairwise inference from the homology of either protein sequence or structure. Recently, similarity networks have been presented to leverage both the ability to visualize relationships between proteins and assess the transferability of functional inference. Here we present PANADA, a novel toolkit for the visualization and analysis of protein similarity networks in Cytoscape. Networks can be constructed based on pairwise sequence or structural alignments either on a set of proteins or, alternatively, by database search from a single sequence. The Panada web server, executable for download and examples and extensive help files are available at URL: http://protein.bio.unipd.it/panada/. © 2013 Martin et al.},

note = {Cited by: 7; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{SCOPUS_ID:84878095702,

title = {FFPred 2.0: Improved Homology-Independent Prediction of Gene Ontology Terms for Eukaryotic Protein Sequences},

author = {Federico Minneci and Damiano Piovesan and Domenico Cozzetto and David T. Jones},

url = {https://www.scopus.com/record/display.uri?eid=2-s2.0-84878095702&origin=inward},

doi = {10.1371/journal.pone.0063754},

year  = {2013},

date = {2013-01-01},

journal = {PLoS ONE},

volume = {8},

number = {5},

abstract = {To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information - either experimentally validated or computationally inferred by similarity - remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy. © 2013 Minneci et al.},

note = {Cited by: 43; Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}