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2. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Author

Naihui Zhou, Yuxiang Jiang, Timothy R. Bergquist, Alexandra J. Lee, Balint Z. Kacsoh, Alex W. Crocker, Kimberley A. Lewis, George Georghiou, Huy N. Nguyen, Md Nafiz Hamid, Larry Davis, Tunca Dogan, Volkan Atalay, Ahmet S. Rifaioglu, Alperen Dalkıran, Rengul Cetin Atalay, Chengxin Zhang, Rebecca L. Hurto, Peter L. Freddolino, Yang Zhang, Prajwal Bhat, Fran Supek, José M. Fernández, Branislava Gemovic, Vladimir R. Perovic, Radoslav S. Davidović, Neven Sumonja, Nevena Veljkovic, Ehsaneddin Asgari, Mohammad R.K. Mofrad, Giuseppe Profiti, Castrense Savojardo, Pier Luigi Martelli, Rita Casadio, Florian Boecker, Heiko Schoof, Indika Kahanda, Natalie Thurlby, Alice C. McHardy, Alexandre Renaux, Rabie Saidi, Julian Gough, Alex A. Freitas, Magdalena Antczak, Fabio Fabris, Mark N. Wass, Jie Hou, Jianlin Cheng, Zheng Wang, Alfonso E. Romero, Alberto Paccanaro, Haixuan Yang, Tatyana Goldberg, Chenguang Zhao, Liisa Holm, Petri Törönen, Alan J. Medlar, Elaine Zosa, Itamar Borukhov, Ilya Novikov, Angela Wilkins, Olivier Lichtarge, Po-Han Chi, Wei-Cheng Tseng, Michal Linial, Peter W. Rose, Christophe Dessimoz, Vedrana Vidulin, Saso Dzeroski, Ian Sillitoe, Sayoni Das, Jonathan Gill Lees, David T. Jones, Cen Wan, Domenico Cozzetto, Rui Fa, Mateo Torres, Alex Warwick Vesztrocy, Jose Manuel Rodriguez, Michael L. Tress, Marco Frasca, Marco Notaro, Giuliano Grossi, Alessandro Petrini, Matteo Re, Giorgio Valentini, Marco Mesiti, Daniel B. Roche, Jonas Reeb, David W. Ritchie, Sabeur Aridhi, Seyed Ziaeddin Alborzi, Marie-Dominique Devignes, Da Chen Emily Koo, Richard Bonneau, Vladimir Gligorijević, Meet Barot, Hai Fang, Stefano Toppo, Enrico Lavezzo, Marco Falda, Michele Berselli, Silvio C.E. Tosatto, Marco Carraro, Damiano Piovesan, Hafeez Ur Rehman, Qizhong Mao, Shanshan Zhang, Slobodan Vucetic, Gage S. Black, Dane Jo, Erica Suh, Jonathan B. Dayton, Dallas J. Larsen, Ashton R. Omdahl, Liam J. McGuffin, Danielle A. Brackenridge, Patricia C. Babbitt, Jeffrey M. Yunes, Paolo Fontana, Feng Zhang, Shanfeng Zhu, Ronghui You, Zihan Zhang, Suyang Dai, Shuwei Yao, Weidong Tian, Renzhi Cao, Caleb Chandler, Miguel Amezola, Devon Johnson, Jia-Ming Chang, Wen-Hung Liao, Yi-Wei Liu, Stefano Pascarelli, Yotam Frank, Robert Hoehndorf, Maxat Kulmanov, Imane Boudellioua, Gianfranco Politano, Stefano Di Carlo, Alfredo Benso, Kai Hakala, Filip Ginter, Farrokh Mehryary, Suwisa Kaewphan, Jari Björne, Hans Moen, Martti E.E. Tolvanen, Tapio Salakoski, Daisuke Kihara, Aashish Jain, Tomislav Šmuc, Adrian Altenhoff, Asa Ben-Hur, Burkhard Rost, Steven E. Brenner, Christine A. Orengo, Constance J. Jeffery, Giovanni Bosco, Deborah A. Hogan, Maria J. Martin, Claire O’Donovan, Sean D. Mooney, Casey S. Greene, Predrag Radivojac, and Iddo Friedberg

Subjects
Protein function prediction, Long-term memory, Biofilm, Critical assessment, Community challenge, Biology (General), QH301-705.5, Genetics, QH426-470
Abstract

Abstract Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

Published
2019
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3. Assessing predictors for new post translational modification sites: A case study on hydroxylation.

Author

Damiano Piovesan, Andras Hatos, Giovanni Minervini, Federica Quaglia, Alexander Miguel Monzon, and Silvio C E Tosatto

Subjects
Biology (General), QH301-705.5
Abstract

Post-translational modification (PTM) sites have become popular for predictor development. However, with the exception of phosphorylation and a handful of other examples, PTMs suffer from a limited number of available training examples and sparsity in protein sequences. Here, proline hydroxylation is taken as an example to compare different methods and evaluate their performance on new experimentally determined sites. As a guide for effective experimental design, predictors require both high specificity and sensitivity. However, the self-reported performance may often not be indicative of prediction quality and detection of new sites is not guaranteed. We have benchmarked seven published hydroxylation site predictors on two newly constructed independent datasets. The self-reported performance is found to widely overestimate the real accuracy measured on independent datasets. No predictor performs better than random on new examples, indicating the refined models do not sufficiently generalize to detect new sites. The number of false positives is high and precision low, in particular for non-collagen proteins whose motifs are not conserved. As hydroxylation site predictors do not generalize for new data, caution is advised when using PTM predictors in the absence of independent evaluations, in particular for highly specific sites involved in signalling.

Published
2020
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4. Molecular Determinants of Selectivity in Disordered Complexes May Shed Light on Specificity in Protein Condensates

Author

Alexander Miguel Monzon, Damiano Piovesan, and Monika Fuxreiter

Subjects
biomolecular condensates, protein interactions, disordered complexes, fuzzy interactions, Microbiology, QR1-502
Abstract

Biomolecular condensates challenge the classical concepts of molecular recognition. The variable composition and heterogeneous conformations of liquid-like protein droplets are bottlenecks for high-resolution structural studies. To obtain atomistic insights into the organization of these assemblies, here we have characterized the conformational ensembles of specific disordered complexes, including those of droplet-driving proteins. First, we found that these specific complexes exhibit a high degree of conformational heterogeneity. Second, we found that residues forming contacts at the interface also sample many conformations. Third, we found that different patterns of contacting residues form the specific interface. In addition, we observed a wide range of sequence motifs mediating disordered interactions, including charged, hydrophobic and polar contacts. These results demonstrate that selective recognition can be realized by variable patterns of weakly defined interaction motifs in many different binding configurations. We propose that these principles also play roles in determining the selectivity of biomolecular condensates.

Published
2022
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5. An intrinsically disordered proteins community for ELIXIR [version 1; peer review: 2 approved]

Author

Norman E. Davey, M. Madan Babu, Martin Blackledge, Alan Bridge, Salvador Capella-Gutierrez, Zsuzsanna Dosztanyi, Rachel Drysdale, Richard J. Edwards, Arne Elofsson, Isabella C. Felli, Toby J. Gibson, Aleksandras Gutmanas, John M. Hancock, Jen Harrow, Desmond Higgins, Cy M. Jeffries, Philippe Le Mercier, Balint Mészáros, Marco Necci, Cedric Notredame, Sandra Orchard, Christos A. Ouzounis, Rita Pancsa, Elena Papaleo, Roberta Pierattelli, Damiano Piovesan, Vasilis J. Promponas, Patrick Ruch, Gabriella Rustici, Pedro Romero, Sirarat Sarntivijai, Gary Saunders, Benjamin Schuler, Malvika Sharan, Denis C. Shields, Joel L. Sussman, Jonathan A. Tedds, Peter Tompa, Michael Turewicz, Jiri Vondrasek, Wim F. Vranken, Bonnie Ann Wallace, Kanin Wichapong, and Silvio C. E. Tosatto

Subjects
Medicine, Science
Abstract

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are now recognised as major determinants in cellular regulation. This white paper presents a roadmap for future e-infrastructure developments in the field of IDP research within the ELIXIR framework. The goal of these developments is to drive the creation of high-quality tools and resources to support the identification, analysis and functional characterisation of IDPs. The roadmap is the result of a workshop titled “An intrinsically disordered protein user community proposal for ELIXIR” held at the University of Padua. The workshop, and further consultation with the members of the wider IDP community, identified the key priority areas for the roadmap including the development of standards for data annotation, storage and dissemination; integration of IDP data into the ELIXIR Core Data Resources; and the creation of benchmarking criteria for IDP-related software. Here, we discuss these areas of priority, how they can be implemented in cooperation with the ELIXIR platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for an IDP Community in ELIXIR and is an appeal to identify and involve new stakeholders.

Published
2019
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6. A community proposal to integrate proteomics activities in ELIXIR [version 1; referees: 2 approved]

Author

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

Subjects
Protein Chemistry & Proteomics, Science & Medical Policies, Medicine, Science
Abstract

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

Published
2017
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7. FFPred 2.0: improved homology-independent prediction of gene ontology terms for eukaryotic protein sequences.

Author

Federico Minneci, Damiano Piovesan, Domenico Cozzetto, and David T Jones

Subjects
Medicine, Science
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.

Published
2013
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9. CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins.

Author

Alessio Del Conte, Adel Bouhraoua, Mahta Mehdiabadi, Damiano Clementel, Alexander Miguel Monzon, Alex S. Holehouse, Daniel A. Griffith, Ryan J. Emenecker, Ashwini Patil, Ronesh Sharma, Tatsuhiko Tsunoda, Alok Sharma, Yi Jun Tang, Bin Liu, Claudio Mirabello, Björn Wallner, Burkhard Rost, Dagmar Ilzhöfer, Maria Littmann, Michael Heinzinger, Lea I M. Krautheimer, Michael Bernhofer, Liam J. McGuffin, Isabelle Callebaut, Tristan Bitard Feildel, Jian Liu, Jianlin Cheng, Zhiye Guo, Jinbo Xu, Sheng Wang, Nawar Malhis, Jörg Gsponer, Chol-Song Kim, Kun-Sop Han, Myong-Chol Ma, Lukasz A. Kurgan, Sina Ghadermarzi, Akila Katuwawala, Bi Zhao, Zhenling Peng, Zhonghua Wu, Gang Hu, Kui Wang, Md. Tamjidul Hoque, Md Wasi Ul Kabir, Michele Vendruscolo, Pietro Sormanni, Min Li 0007, Fuhao Zhang, Pengzhen Jia, Yida Wang, Michail Yu. Lobanov, Oxana V. Galzitskaya, Wim F. Vranken, Adrián Díaz, Thomas Litfin, Yaoqi Zhou, Jack Hanson, Kuldip K. Paliwal, Zsuzsanna Dosztányi, Gábor Erdös 0002, Silvio C. E. Tosatto, and Damiano Piovesan

Published
2023
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12. PDBe-KB: collaboratively defining the biological context of structural data.

Author

Mihaly Varadi, Stephen Anyango, David R. Armstrong, John M. Berrisford, Preeti Choudhary, Mandar S. Deshpande, Nurul Nadzirin, Sreenath Nair, Lukás Pravda, Ahsan Tanweer, Bissan Al-Lazikani, Claudia Andreini, Geoffrey J. Barton, David Bednar, Karel Berka, Tom L. Blundell, Kelly Brock, José María Carazo, Jirí Damborský, Alessia David, Sucharita Dey, Roland L. Dunbrack Jr., Juan Fernández-Recio, Franca Fraternali, Toby J. Gibson, Manuela Helmer-Citterich, David Hoksza, Thomas A. Hopf, David Jakubec, Natarajan Kannan, Radoslav Krivák, Manjeet Kumar, Emmanuel D. Levy, Nir London, José Ramón Macías, Mallur Srivatsan Madhusudhan, Debora S. Marks, Lennart Martens, Stuart A McGowan, Jake E. McGreig, Vivek Modi, R. Gonzalo Parra, Gerardo Pepe, Damiano Piovesan, Jaime Prilusky, Valeria Putignano, Leandro G. Radusky, Pathmanaban Ramasamy, Atilio O. Rausch, Nathalie Reuter, Luis A. Rodriguez, Nathan J. Rollins, Antonio Rosato, Pawel Rubach, Luis Serrano, Gulzar Singh, Petr Skoda 0001, Carlos Oscar Sánchez Sorzano, Jan Stourac, Joanna I. Sulkowska, Radka Svobodová Vareková, Natalia Tichshenko, Silvio C. E. Tosatto, Wim F. Vranken, Mark N. Wass, Dandan Xue, Daniel Zaidman, Janet M. Thornton, Michael J. E. Sternberg, Christine A. Orengo, and Sameer Velankar

Published
2022
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14. DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation.

Author

Federica Quaglia, Bálint Mészáros, Edoardo Salladini, András Hatos, Rita Pancsa, Lucía B. Chemes, Mátyás Pajkos, Tamas Lazar, Samuel Peña-Díaz, Jaime Santos, Veronika ács, Nazanin Farahi, Erzsébet Fichó, Maria Cristina Aspromonte, Claudio Bassot, Anastasia Chasapi, Norman E. Davey, Radoslav Davidovic, Laszlo Dobson, Arne Elofsson, Gábor Erdös 0002, Pascale Gaudet, Michelle G. Giglio, Juliana Glavina, Javier Iserte, Valentín Iglesias, Zsófia L. Kálmán, Matteo Lambrughi, Emanuela Leonardi, Sonia Longhi, Sandra Macedo-Ribeiro, Emiliano Maiani, Julia Marchetti, Cristina Marino Buslje, Attila Meszaros, Alexander Miguel Monzon, Giovanni Minervini, Suvarna Nadendla, Juliet F. Nilsson, Marián Novotný, Christos A. Ouzounis, Nicolas Palopoli, Elena Papaleo, Pedro J. Barbosa Pereira, Gabriele Pozzati, Vasilis J. Promponas, Jordi Pujols, Alma Carolina Sanchez Rocha, Martin Salas, Luciana Rodriguez Sawicki, éva Schád, Aditi Shenoy, Tamás Szaniszló, Konstantinos D. Tsirigos, Nevena Veljkovic, Gustavo D. Parisi, Salvador Ventura, Zsuzsanna Dosztányi, Peter Tompa, Silvio C. E. Tosatto, and Damiano Piovesan

Published
2022
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17. RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures.

Author

Lisanna Paladin, Martina Bevilacqua, Sara Errigo, Damiano Piovesan, Ivan Micetic, Marco Necci, Alexander Miguel Monzon, Maria Laura Fabre, José Luis López, Juliet F. Nilsson, Javier Ríos, Pablo Lorenzano Menna, Maia Cabrera, Martin Gonzalez Buitron, Mariane Gonçalves Kulik, Sebastian Fernandez Alberti, María Silvina Fornasari, Gustavo D. Parisi, Antonio Lagares, Layla Hirsh, Miguel A. Andrade-Navarro, Andrey V. Kajava, and Silvio C. E. Tosatto

Published
2021
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18. MobiDB: intrinsically disordered proteins in 2021.

Author

Damiano Piovesan, Marco Necci, Nahuel Escobedo, Alexander Miguel Monzon, András Hatos, Ivan Micetic, Federica Quaglia, Lisanna Paladin, Pathmanaban Ramasamy, Zsuzsanna Dosztányi, Wim F. Vranken, Norman E. Davey, Gustavo D. Parisi, Mónika Fuxreiter, and Silvio C. E. Tosatto

Published
2021
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19. DisProt: intrinsic protein disorder annotation in 2020.

Author

András Hatos, Borbála Hajdu-Soltész, Alexander Miguel Monzon, Nicolas Palopoli, Lucía álvarez, Burcu Aykaç Fas, Claudio Bassot, Guillermo I. Benítez, Martina Bevilacqua, Anastasia Chasapi, Lucía B. Chemes, Norman E. Davey, Radoslav Davidovic, A. Keith Dunker, Arne Elofsson, Julien Gobeill, Nicolás S. González Foutel, Govindarajan Sudha, Mainak Guharoy, Tamás Horváth 0005, Valentin Iglesias, Andrey V. Kajava, Orsolya P. Kovács, John Lamb, Matteo Lambrughi, Tamas Lazar, Jeremy Y. Leclercq, Emanuela Leonardi, Sandra Macedo-Ribeiro, Mauricio Macossay-Castillo, Emiliano Maiani, José A. Manso, Cristina Marino Buslje, Elizabeth Martínez-Pérez, Bálint Mészáros, Ivan Micetic, Giovanni Minervini, Nikoletta Murvai, Marco Necci, Christos A. Ouzounis, Mátyás Pajkos, Lisanna Paladin, Rita Pancsa, Elena Papaleo, Gustavo D. Parisi, Emilie Pasche, Pedro J. Barbosa Pereira, Vasilis J. Promponas, Jordi Pujols, Federica Quaglia, Patrick Ruch, Marco Salvatore, éva Schád, Beáta Szabó, Tamás Szaniszló, Stella Tamana, ágnes Tantos, Nevena Veljkovic, Salvador Ventura, Wim F. Vranken, Zsuzsanna Dosztányi, Peter Tompa, Silvio C. E. Tosatto, and Damiano Piovesan

Published
2020
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23. The Pfam protein families database in 2019.

Author

Sara El-Gebali, Jaina Mistry, Alex Bateman, Sean R. Eddy, Aurelien Luciani, Simon C. Potter, Matloob Qureshi, Lorna J. Richardson, Gustavo A. Salazar, Alfredo Smart, Erik L. L. Sonnhammer, Layla Hirsh, Lisanna Paladin, Damiano Piovesan, Silvio C. E. Tosatto, and Robert D. Finn

Published
2019
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29. DisProt 7.0: a major update of the database of disordered proteins.

Author

Damiano Piovesan, Francesco Tabaro, Ivan Micetic, Marco Necci, Federica Quaglia, Christopher J. Oldfield, Maria Cristina Aspromonte, Norman E. Davey, Radoslav Davidovic, Zsuzsanna Dosztányi, Arne Elofsson, Alessandra Gasparini, András Hatos, Andrey V. Kajava, Lajos Kalmár, Emanuela Leonardi, Tamas Lazar, Sandra Macedo-Ribeiro, Mauricio Macossay-Castillo, Attila Meszaros, Giovanni Minervini, Nikoletta Murvai, Jordi Pujols, Daniel B. Roche, Edoardo Salladini, éva Schád, Antoine Schramm, Beáta Szabó, ágnes Tantos, Fiorella Tonello, Konstantinos D. Tsirigos, Nevena Veljkovic, Salvador Ventura, Wim F. Vranken, Per Warholm, Vladimir N. Uversky, A. Keith Dunker, Sonia Longhi, Peter Tompa, and Silvio C. E. Tosatto

Published
2017
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32. InterPro in 2017 - beyond protein family and domain annotations.

Author

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

Published
2017
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40. Intrinsic protein disorder and conditional folding in AlphaFoldDB

Author

Damiano Piovesan, Alexander Miguel Monzon, and Silvio C. E. Tosatto

Subjects
Intrinsically Disordered Proteins, Protein Folding, machine learning, CAID, Protein Conformation, critical assessment, structural bioinformatics, protein structure, Molecular Biology, Biochemistry, intrinsically disordered proteins
Abstract

Intrinsically disordered regions (IDRs) defying the traditional protein structure-function paradigm have been difficult to analyze. The availability of accurate structure predictions on a large scale in AlphaFoldDB offers a fresh perspective on IDR prediction. Here, we establish three baselines for IDR prediction from AlphaFoldDB models based on the recent CAID dataset. Surprisingly, AlphaFoldDB is highly competitive for predicting both IDRs and conditionally folded binding regions, demonstrating the plasticity of the disorder to structure continuum.

Published
2022

45. PDB ProtVista: A reusable and open-source sequence feature viewer

Author

Silvio Tosatto, Mihaly Varadi, Typhaine Paysan-Lafosse, Aleksandras Gutmanas, Saqib Mir, Sameer Velankar, Sreenath Nair, Damiano Piovesan, and Mandar Deshpande

Abstract

SummaryThe PDB ProtVista is a reusable and customisable sequence feature viewer that provides intuitive and detailed 2D visualisation of residue-level annotations while also supporting interactive communication with 3D viewers. The Protein Data Bank in Europe (PDBe) team develops and maintains PDB ProtVista. Several web services use it to display structural and functional annotations such as macromolecular interaction interfaces, intrinsic disorder predictions, sequence variants, and sequence conservation.Availability and ImplementationThe PDB ProtVista is a native JavaScript (ES6) application that is freely available from https://github.com/PDBeurope/protvista-pdb. The above URL also provides extensive documentation and step-by-step user guides on integrating PDB ProtVista with existing web applications and 3D molecular viewers. It also offers an example of how to display custom data of the user and functional annotations for PDB and UniProt entries powered by a rich set of PDBe API endpoints.

Published
2022
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46. MIADE metadata guidelines: Minimum Information About a Disorder Experiment

Author

Bálint Mészáros, András Hatos, Nicolas Palopoli, Federica Quaglia, Edoardo Salladini, Kim Van Roey, Haribabu Arthanari, Zsuzsanna Dosztányi, Isabella C. Felli, Patrick D Fischer, Jeffrey C. Hoch, Cy M Jeffries, Sonia Longhi, Emiliano Maiani, Sandra Orchard, Rita Pancsa, Elena Papaleo, Roberta Pierattelli, Damiano Piovesan, Iva Pritisanac, Thibault Viennet, Peter Tompa, Wim Vranken, Silvio CE Tosatto, and Norman E Davey

Abstract

An unambiguous description of an experimental setup and analysis, and the subsequent biological observation is vital for accurate data interpretation and reproducible results. Consequently, experimental analyses should be described in a concise, unequivocal, and digestible manner. The aim of minimum information guidelines is to define the fundamental complement of data that can support an unambiguous conclusion on experimental observations. In this document, we present the Minimum Information About Disorder Experiments (MIADE) guidelines to define the minimal fundamental parameters required for non-experts to understand the key findings of an experiment studying intrinsically disordered proteins (IDPs) or intrinsically disordered protein regions (IDRs). MIADE guidelines provide recommendations for data producers to describe the results of their experiments at source, for curators to annotate experimental data to community resources and for database developers maintaining community resources to disseminate the data. We give examples of the application of these guidelines in common use cases and describe the implementation of an update to the DisProt IDP database to allow MIADE-compliant annotation. The MIADE guidelines will improve the interpretability of experimental results for data consumers, facilitate direct data submission, simplify data curation, improve data exchange among repositories and standardise the dissemination of the key metadata on an IDP experiment by IDP data sources.

Published
2022
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49. RepeatsDB in 2021: improved data and extended classification for protein tandem repeat structures

Author

Pablo Lorenzano Menna, Martina Bevilacqua, Mariane Gonçalves Kulik, Alexander Miguel Monzon, Lisanna Paladin, José Luis López, Martin Gonzalez Buitron, Javier Rios, Marco Necci, Sara Errigo, Layla Hirsh, Ivan Mičetić, Juliet F. Nilsson, Andrey V. Kajava, María Silvina Fornasari, Antonio Lagares, Damiano Piovesan, Sebastian Fernandez-Alberti, Maia Diana Eliana Cabrera, Gustavo Parisi, María Laura Fabre, Miguel A. Andrade-Navarro, Silvio C. E. Tosatto, Centre de recherche en Biologie Cellulaire (CRBM), and Université Montpellier 2 - Sciences et Techniques (UM2)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Université Montpellier 1 (UM1)

Subjects
Repetitive Sequences, Amino Acid, AcademicSubjects/SCI00010, Biología, Statistics as Topic, Protein Data Bank (RCSB PDB), Computational biology, Biology, Repetitive Sequences, Gene Ontology, HEK293 Cells, HeLa Cells, Humans, Proteins, Reproducibility of Results, User-Computer Interface, Databases, Protein, Tandem Repeat Sequences, Databases, 03 medical and health sciences, Annotation, Protein structure, Similarity (network science), Tandem repeat, Genetics, Database Issue, Ciencias Exactas, database, 030304 developmental biology, 0303 health sciences, Hierarchy (mathematics), Protein, 030302 biochemistry & molecular biology, computer.file_format, Protein Data Bank, Class (biology), proteins, Amino Acid, ComputingMethodologies_PATTERNRECOGNITION, classification, protein tandem repeat structures, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], computer
Abstract

The RepeatsDB database (URL: https://repeatsdb.org/) provides annotations and classification for protein tandem repeat structures from the Protein Data Bank (PDB). Protein tandem repeats are ubiquitous in all branches of the tree of life. The accumulation of solved repeat structures provides new possibilities for classification and detection, but also increasing the need for annotation. Here we present RepeatsDB 3.0, which addresses these challenges and presents an extended classification scheme. The major conceptual change compared to the previous version is the hierarchical classification combining top levels based solely on structural similarity (Class > Topology > Fold) with two new levels (Clan > Family) requiring sequence similarity and describing repeat motifs in collaboration with Pfam. Data growth has been addressed with improved mechanisms for browsing the classification hierarchy. A new UniProt-centric view unifies the increasingly frequent annotation of structures from identical or similar sequences. This update of RepeatsDB aligns with our commitment to develop a resource that extracts, organizes and distributes specialized information on tandem repeat protein structures., Facultad de Ciencias Exactas, Instituto de Biotecnologia y Biologia Molecular

Published
2020
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50. PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins

Author

Marcin Grynberg, John M. Hancock, Pablo Mier, Silvio C. E. Tosatto, Vasilis J. Promponas, Marco Necci, Aleksandra Gruca, László Dobson, Damiano Piovesan, Zsuzsanna Dosztányi, Patryk Jarnot, Joanna Ziemska-Legiecka, Miguel A. Andrade-Navarro, Matthew Merski, and Lisanna Paladin

Subjects
Sequence analysis, AcademicSubjects/SCI00010, Protein domain, Computational biology, Biology, Domain (software engineering), Computer graphics, 03 medical and health sciences, Annotation, Protein Domains, Sequence Analysis, Protein, Genetics, Computer Graphics, Humans, Amino Acids, 030304 developmental biology, 0303 health sciences, Intersection (set theory), 030302 biochemistry & molecular biology, Membrane Proteins, Proteins, Molecular Sequence Annotation, Visualization, Web Server Issue, Software
Abstract

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity—a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

Published
2020

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