Alexander Miguel Monzon

@article{DelConte2024W306,

title = {RING 4.0: Faster residue interaction networks with novel interaction types across over 35,000 different chemical structures},

author = { Alessio Del Conte and Giorgia F Camagni and Damiano Clementel and Giovanni Minervini and Alexander Miguel Monzon and Carlo Ferrari and Damiano Piovesan and Silvio C. E Tosatto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197788039\&doi=10.1093%2fnar%2fgkae337\&partnerID=40\&md5=bca70d16fbb39f5466a3957673ef9eef},

doi = {10.1093/nar/gkae337},

issn = {03051048},

year  = {2024},

date = {2024-01-01},

abstract = {Residue interaction networks (RINs) are a valuable approach for representing contacts in protein structures. RINs have been widely used in various research areas, including the analysis of mutation effects, domain-domain communication, catalytic activity, and molecular dynamics simulations. The RING server is a powerful tool to calculate non-covalent molecular interactions based on geometrical parameters, providing high-quality and reliable results. Here, we introduce RING 4.0, which includes significant enhancements for identifying both covalent and non-covalent bonds in protein structures. It now encompasses seven different interaction types, with the addition of π-hydrogen, halogen bonds and metal ion coordination sites. The definitions of all available bond types have also been refined and RING can now process the complete PDB chemical component dictionary (over 35000 different molecules) which provides atom names and covalent connectivity information for all known ligands. Optimization of the software has improved execution time by an order of magnitude. The RING web server has been redesigned to provide a more engaging and interactive user experience, incorporating new visualization tools. Users can now visualize all types of interactions simultaneously in the structure viewer and network component. The web server, including extensive help and tutorials, is available from URL: https://ring.biocomputingup.it/. © 2024 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aspromonte2024D434,

title = {DisProt in 2024: improving function annotation of intrinsically disordered proteins},

author = { Maria Cristina Aspromonte and Maria Victoria Nugnes and Federica Quaglia and Adel Bouharoua and Silvio C.E. Tosatto and Damiano Piovesan and Vasileios Sagris and Vasilis J. Promponas and Anastasia Chasapi and Erzs\'{e}bet Fich\'{o} and Galo E. Balatti and Gustavo Parisi and Mart\'{i}n Gonz\'{a}lez Buitr\'{o}n and Gabor Erdos and Matyas Pajkos and Zsuzsanna Doszt\'{a}nyi and Laszlo Dobson and Alessio Del Conte and Damiano Clementel and Edoardo Salladini and Emanuela Leonardi and Fatemeh Kordevani and Hamidreza Ghafouri and Luiggi G. Tenorio Ku and Alexander Miguel Monzon and Carlo Ferrari and Zs\'{o}fia K\'{a}lm\'{a}n and Juliet F. Nilsson and Jaime Santos and Carlos Pintado-Grima and Salvador Ventura and Veronika \'{A}cs and Rita Pancsa and Mariane Goncalves Kulik and Miguel A. Andrade-Navarro and Pedro Jos\'{e} Barbosa Pereira and Sonia Longhi and Philippe Le Mercier and Julian Bergier and Peter Tompa and Tamas Lazar},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176208048\&doi=10.1093%2fnar%2fgkad928\&partnerID=40\&md5=fc34ce08667ff42029fdb54d5142c08f},

doi = {10.1093/nar/gkad928},

issn = {03051048},

year  = {2024},

date = {2024-01-01},

abstract = {DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt’s curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications. © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Ghafouri2024D536,

title = {PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins},

author = { Hamidreza Ghafouri and Tamas Lazar and Alessio Del Conte and Luiggi G. Tenorio Ku and Peter Tompa and Silvio C.E. Tosatto and Alexander Miguel Monzon and Maria C. Aspromonte and Pau Bernad\'{o} and Bel\'{e}n Chaves-Arquero and Lucia Beatriz Chemes and Damiano Clementel and Tiago N. Cordeiro and Carlos A. Elena-Real and Michael Feig and Isabella C. Felli and Carlo Ferrari and Julie D. Forman-Kay and Tiago Gomes and Frank Gondelaud and Claudiu C. Gradinaru and T\^{a}p Ha-Duong and Teresa Head-Gordon and P\'{e}tur O. Heidarsson and Giacomo Janson and Gunnar Jeschke and Emanuela Leonardi and Zi Hao Liu and Sonia Longhi and Xamuel L. Lund and Maria J. Macias and Pau Martin-Malpartida and Davide Mercadante and Assia Mouhand and Gabor Nagy and Mar\'{i}a Victoria Nugnes and Jos\'{e} Manuel P\'{e}rez-Ca\~{n}adillas and Giulia Pesce and Roberta Pierattelli and Damiano Piovesan and Federica Quaglia and Sylvie Ricard-Blum and Paul Robustelli and Amin Sagar and Edoardo Salladini and Lucile S\'{e}nicourt and Nathalie Sibille and Jo\~{a}o M.C. Teixeira and Thomas E. Tsangaris and Mihaly Varadi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181761325\&doi=10.1093%2fnar%2fgkad947\&partnerID=40\&md5=0ad51562357f3e5f603d744e02f8729a},

doi = {10.1093/nar/gkad947},

issn = {03051048},

year  = {2024},

date = {2024-01-01},

abstract = {The Protein Ensemble Database (PED) (URL: https://proteinensemble.org) is the primary resource for depositing structural ensembles of intrinsically disordered proteins. This updated version of PED reflects advancements in the field, denoting a continual expansion with a total of 461 entries and 538 ensembles, including those generated without explicit experimental data through novel machine learning (ML) techniques. With this significant increment in the number of ensembles, a few yet-unprecedented new entries entered the database, including those also determined or refined by electron paramagnetic resonance or circular dichroism data. In addition, PED was enriched with several new features, including a novel deposition service, improved user interface, new database cross-referencing options and integration with the 3D-Beacons network\textemdashall representing efforts to improve the FAIRness of the database. Foreseeably, PED will keep growing in size and expanding with new types of ensembles generated by accurate and fast ML-based generative models and coarse-grained simulations. Therefore, among future efforts, priority will be given to further develop the database to be compatible with ensembles modeled at a coarse-grained level. © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{MacDonagh2024,

title = {Structured Tandem Repeats in Protein Interactions},

author = { Juan Mac Donagh and Abril Marchesini and Agostina Spiga and Maximiliano Jos\'{e} Fallico and Paula Nazarena Arr\'{i}as and Alexander Miguel Monzon and Aimilia-Christina Vagiona and Mariane Gon\c{c}alves-Kulik and Pablo Mier and Miguel A. Andrade-Navarro},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187783312\&doi=10.3390%2fijms25052994\&partnerID=40\&md5=36bf2bfc0d625c28b50aab098d0d94e7},

doi = {10.3390/ijms25052994},

issn = {16616596},

year  = {2024},

date = {2024-01-01},

abstract = {Tandem repeats (TRs) in protein sequences are consecutive, highly similar sequence motifs. Some types of TRs fold into structural units that pack together in ensembles, forming either an (open) elongated domain or a (closed) propeller, where the last unit of the ensemble packs against the first one. Here, we examine TR proteins (TRPs) to see how their sequence, structure, and evolutionary properties favor them for a function as mediators of protein interactions. Our observations suggest that TRPs bind other proteins using large, structured surfaces like globular domains; in particular, open-structured TR ensembles are favored by flexible termini and the possibility to tightly coil against their targets. While, intuitively, open ensembles of TRs seem prone to evolve due to their potential to accommodate insertions and deletions of units, these evolutionary events are unexpectedly rare, suggesting that they are advantageous for the emergence of the ancestral sequence but are early fixed. We hypothesize that their flexibility makes it easier for further proteins to adapt to interact with them, which would explain their large number of protein interactions. We provide insight into the properties of open TR ensembles, which make them scaffolds for alternative protein complexes to organize genes, RNA and proteins. © 2024 by the authors.},

note = {Cited by: 1; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Piovesan2024,

title = {CAFA-evaluator: a Python tool for benchmarking ontological classification methods},

author = { Damiano Piovesan and Davide Zago and Parnal Joshi and M. Clara De Paolis Kaluza and Mahta Mehdiabadi and Rashika Ramola and Alexander Miguel Monzon and Walter Reade and Iddo Friedberg and Predrag Radivojac and Silvio C. E. Tosatto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85188993912\&doi=10.1093%2fbioadv%2fvbae043\&partnerID=40\&md5=b6e09ea188a60708097f5bc31ba115dd},

doi = {10.1093/bioadv/vbae043},

issn = {26350041},

year  = {2024},

date = {2024-01-01},

abstract = {We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes multi-label evaluation to modern ontologies where the prediction targets are drawn from a directed acyclic graph and achieves high efficiency by leveraging matrix computation and topological sorting. The program requirements include a small number of standard Python libraries, making CAFA-evaluator easy to maintain. The code replicates the Critical Assessment of protein Function Annotation (CAFA) benchmarking, which evaluates predictions of the consistent subgraphs in Gene Ontology. Owing to its reliability and accuracy, the organizers have selected CAFA-evaluator as the official CAFA evaluation software. © 2024 The Author(s). Published by Oxford University Press.},

note = {Cited by: 2; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Conte20231925,

title = {Critical assessment of protein intrinsic disorder prediction (CAID) - Results of round 2},

author = { Alessio Del Conte and Mahta Mehdiabadi and Adel Bouhraoua and Alexander Miguel Monzon and Silvio C. E. Tosatto and Damiano Piovesan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169117009\&doi=10.1002%2fprot.26582\&partnerID=40\&md5=20d7d0ec6f796c0901f2a365becf7fa6},

doi = {10.1002/prot.26582},

issn = {08873585},

year  = {2023},

date = {2023-01-01},

abstract = {Protein intrinsic disorder (ID) is a complex and context-dependent phenomenon that covers a continuum between fully disordered states and folded states with long dynamic regions. The lack of a ground truth that fits all ID flavors and the potential for order-to-disorder transitions depending on specific conditions makes ID prediction challenging. The CAID2 challenge aimed to evaluate the performance of different prediction methods across different benchmarks, leveraging the annotation provided by the DisProt database, which stores the coordinates of ID regions when there is experimental evidence in the literature. The CAID2 challenge demonstrated varying performance of different prediction methods across different benchmarks, highlighting the need for continued development of more versatile and efficient prediction software. Depending on the application, researchers may need to balance performance with execution time when selecting a predictor. Methods based on AlphaFold2 seem to be good ID predictors but they are better at detecting absence of order rather than ID regions as defined in DisProt. The CAID2 predictors can be freely used through the CAID Prediction Portal, and CAID has been integrated into OpenEBench, which will become the official platform for running future CAID challenges. © 2023 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Piovesan2023D438,

title = {MobiDB: 10 years of intrinsically disordered proteins},

author = { Damiano Piovesan and Alessio Del Conte and Damiano Clementel and Alexander Miguel Monzon and Martina Bevilacqua and Maria Cristina Aspromonte and Javier A Iserte and Fernando E Orti and Cristina Marino-Buslje and Silvio C. E Tosatto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145966264\&doi=10.1093%2fnar%2fgkac1065\&partnerID=40\&md5=8547b341a7698891915b9e425ab793e7},

doi = {10.1093/nar/gkac1065},

issn = {03051048},

year  = {2023},

date = {2023-01-01},

abstract = {The MobiDB database (URL: https://mobidb.org/) is a knowledge base of intrinsically disordered proteins. MobiDB aggregates disorder annotations derived from the literature and from experimental evidence along with predictions for all known protein sequences. MobiDB generates new knowledge and captures the functional significance of disordered regions by processing and combining complementary sources of information. Since its first release 10 years ago, the MobiDB database has evolved in order to improve the quality and coverage of protein disorder annotations and its accessibility. MobiDB has now reached its maturity in terms of data standardization and visualization. Here, we present a new release which focuses on the optimization of user experience and database content. The major advances compared to the previous version are the integration of AlphaFoldDB predictions and the re-implementation of the homology transfer pipeline, which expands manually curated annotations by two orders of magnitude. Finally, the entry page has been restyled in order to provide an overview of the available annotations along with two separate views that highlight structural disorder evidence and functions associated with different binding modes. © 2023 The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.},

note = {Cited by: 57; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Escobedo2023,

title = {Combining Protein Conformational Diversity and Phylogenetic Information Using CoDNaS and CoDNaS-Q},

author = { Nahuel Escobedo and Alexander Miguel Monzon and Mar\'{i}a Silvina Fornasari and Nicolas Palopoli and Gustavo Parisi},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159198884\&doi=10.1002%2fcpz1.764\&partnerID=40\&md5=01fcae4cfec9f15a0d39dfdc6d05c8ab},

doi = {10.1002/cpz1.764},

issn = {26911299},

year  = {2023},

date = {2023-01-01},

abstract = {CoDNaS (http://ufq.unq.edu.ar/codnas/) and CoDNaS-Q (http://ufq.unq.edu.ar/codnasq) are repositories of proteins with different degrees of conformational diversity. Following the ensemble nature of the native state, conformational diversity represents the structural differences between the conformers in the ensemble. Each entry in CoDNaS and CoDNaS-Q contains a redundant collection of experimentally determined conformers obtained under different conditions. These conformers represent snapshots of the protein dynamism. While CoDNaS contains examples of conformational diversity at the tertiary level, a recent development, CoDNaS-Q, contains examples at the quaternary level. In the emerging age of accurate protein structure prediction by machine learning approaches, many questions remain open regarding the characterization of protein dynamism. In this context, most bioinformatics resources take advantage of distinct features derived from protein alignments, however, the complexity and heterogeneity of information makes it difficult to recover reliable biological signatures. Here we present five protocols to explore tertiary and quaternary conformational diversity at the individual protein level as well as for the characterization of the distribution of conformational diversity at the protein family level in a phylogenetic context. These protocols can provide curated protein families with experimentally known conformational diversity, facilitating the exploration of sequence determinants of protein dynamism. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Assessing conformational diversity with CoDNaS. Alternate Protocol 1: Assessing conformational diversity at the quaternary level with CoDNaS-Q. Basic Protocol 2: Exploring conformational diversity in a protein family. Alternate Protocol 2: Exploring quaternary conformational diversity in a protein family. Basic Protocol 3: Representing conformational diversity in a phylogenetic context. © 2023 Wiley Periodicals LLC.},

note = {Cited by: 0; All Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Arr\'{i}as2023,

title = {The repetitive structure of DNA clamps: An overlooked protein tandem repeat},

author = { Paula Nazarena Arr\'{i}as and Alexander Miguel Monzon and Damiano Clementel and Soroush Mozaffari and Damiano Piovesan and Andrey V. Kajava and Silvio C.E. Tosatto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165364940\&doi=10.1016%2fj.jsb.2023.108001\&partnerID=40\&md5=a32d9f45912f167f7d63dd4ecf4c4758},

doi = {10.1016/j.jsb.2023.108001},

issn = {10478477},

year  = {2023},

date = {2023-01-01},

abstract = {Structured tandem repeats proteins (STRPs) are a specific kind of tandem repeat proteins characterized by a modular and repetitive three-dimensional structure arrangement. The majority of STRPs adopt solenoid structures, but with the increasing availability of experimental structures and high-quality predicted structural models, more STRP folds can be characterized. Here, we describe “Box repeats”, an overlooked STRP fold present in the DNA sliding clamp processivity factors, which has eluded classification although structural data has been available since the late 1990s. Each Box repeat is a β⍺βββ module of about 60 residues, which forms a class V “beads-on-a-string” type STRP. The number of repeats present in processivity factors is organism dependent. Monomers of PCNA proteins in both Archaea and Eukarya have 4 repeats, while the monomers of bacterial beta-sliding clamps have 6 repeats. This new repeat fold has been added to the RepeatsDB database, which now provides structural annotation for 66 Box repeat proteins belonging to different organisms, including viruses. © 2023},

note = {Cited by: 2; All Open Access, Hybrid Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DelConte2023W62,

title = {CAID prediction portal: A comprehensive service for predicting intrinsic disorder and binding regions in proteins},

author = { Alessio Del Conte and Adel Bouhraoua and Mahta Mehdiabadi and Damiano Clementel and Alexander Miguel Monzon and Silvio C.E. Tosatto and Damiano Piovesan and Alex S. Holehouse and Daniel Griffith and Ryan J. Emenecker and Ashwini Patil and Ronesh Sharma and Tatsuhiko Tsunoda and Alok Sharma and Yi Jun Tang and Bin Liu and Claudio Mirabello and Bj\"{o}rn Wallner and Burkhard Rost and Dagmar Ilzh\"{o}fer and Maria Littmann and Michael Heinzinger and Lea I.M. Krautheimer and Michael Bernhofer and Liam J. McGuffin and Isabelle Callebaut and Tristan Bitard Feildel and Jian Liu and Jianlin Cheng and Zhiye Guo and Jinbo Xu and Sheng Wang and Nawar Malhis and J\"{o}rg Gsponer and Chol-Song Kim and Kun-Sop Han and Myong-Chol Ma and Lukasz Kurgan and Sina Ghadermarzi and Akila Katuwawala and Bi Zhao and Zhenling Peng and Zhonghua Wu and Gang Hu and Kui Wang and Md Tamjidul Hoque and Md Wasi Ul Kabir and Michele Vendruscolo and Pietro Sormanni and Min Li and Fuhao Zhang and Pengzhen Jia and Yida Wang and Michail Yu Lobanov and Oxana V. Galzitskaya and Wim Vranken and Adri\'{a}n D\'{i}az and Thomas Litfin and Yaoqi Zhou and Jack Hanson and Kuldip Paliwal and Zsuzsanna Doszt\'{a}nyi and G\'{a}bor Erd\H{o}s},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163958831\&doi=10.1093%2fnar%2fgkad430\&partnerID=40\&md5=a458ba56037429c6a042f7610f3cb4bf},

doi = {10.1093/nar/gkad430},

issn = {03051048},

year  = {2023},

date = {2023-01-01},

abstract = {Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: https://caid.idpcentral.org. © 2023 The Author(s). Published by Oxford University Press on behalf of Nucleic Acids Research.},

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

keywords = {},

pubstate = {published},

tppubtype = {article}

}