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Domenico Cozzetto

Bio: Domenico Cozzetto is an academic researcher from University College London. The author has contributed to research in topics: Protein function prediction & Immunology. The author has an hindex of 24, co-authored 37 publications receiving 4233 citations. Previous affiliations of Domenico Cozzetto include Francis Crick Institute & Guy's and St Thomas' NHS Foundation Trust.


Papers
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Journal ArticleDOI
Predrag Radivojac1, Wyatt T. Clark1, Tal Ronnen Oron2, Alexandra M. Schnoes3, Tobias Wittkop2, Artem Sokolov4, Artem Sokolov5, Kiley Graim5, Christopher S. Funk6, Karin Verspoor6, Asa Ben-Hur5, Gaurav Pandey7, Gaurav Pandey8, Jeffrey M. Yunes8, Ameet Talwalkar8, Susanna Repo9, Susanna Repo8, Michael L Souza8, Damiano Piovesan10, Rita Casadio10, Zheng Wang11, Jianlin Cheng11, Hai Fang, Julian Gough12, Patrik Koskinen13, Petri Törönen13, Jussi Nokso-Koivisto13, Liisa Holm13, Domenico Cozzetto14, Daniel W. A. Buchan14, Kevin Bryson14, David T. Jones14, Bhakti Limaye15, Harshal Inamdar15, Avik Datta15, Sunitha K Manjari15, Rajendra Joshi15, Meghana Chitale16, Daisuke Kihara16, Andreas Martin Lisewski17, Serkan Erdin17, Eric Venner17, Olivier Lichtarge17, Robert Rentzsch14, Haixuan Yang18, Alfonso E. Romero18, Prajwal Bhat18, Alberto Paccanaro18, Tobias Hamp19, Rebecca Kaßner19, Stefan Seemayer19, Esmeralda Vicedo19, Christian Schaefer19, Dominik Achten19, Florian Auer19, Ariane Boehm19, Tatjana Braun19, Maximilian Hecht19, Mark Heron19, Peter Hönigschmid19, Thomas A. Hopf19, Stefanie Kaufmann19, Michael Kiening19, Denis Krompass19, Cedric Landerer19, Yannick Mahlich19, Manfred Roos19, Jari Björne20, Tapio Salakoski20, Andrew Wong21, Hagit Shatkay22, Hagit Shatkay21, Fanny Gatzmann23, Ingolf Sommer23, Mark N. Wass24, Michael J.E. Sternberg24, Nives Škunca, Fran Supek, Matko Bošnjak, Panče Panov, Sašo Džeroski, Tomislav Šmuc, Yiannis A. I. Kourmpetis25, Yiannis A. I. Kourmpetis26, Aalt D. J. van Dijk25, Cajo J. F. ter Braak25, Yuanpeng Zhou27, Qingtian Gong27, Xinran Dong27, Weidong Tian27, Marco Falda28, Paolo Fontana, Enrico Lavezzo28, Barbara Di Camillo28, Stefano Toppo28, Liang Lan29, Nemanja Djuric29, Yuhong Guo29, Slobodan Vucetic29, Amos Marc Bairoch30, Amos Marc Bairoch31, Michal Linial32, Patricia C. Babbitt3, Steven E. Brenner8, Christine A. Orengo14, Burkhard Rost19, Sean D. Mooney2, Iddo Friedberg33 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
Abstract: Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

859 citations

Journal ArticleDOI
TL;DR: A novel method, PSICOV, is presented, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction and displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks.
Abstract: Motivation The accurate prediction of residue-residue contacts, critical for maintaining the native fold of a protein, remains an open problem in the field of structural bioinformatics. Interest in this long-standing problem has increased recently with algorithmic improvements and the rapid growth in the sizes of sequence families. Progress could have major impacts in both structure and function prediction to name but two benefits. Sequence-based contact predictions are usually made by identifying correlated mutations within multiple sequence alignments (MSAs), most commonly through the information-theoretic approach of calculating mutual information between pairs of sites in proteins. These predictions are often inaccurate because the true covariation signal in the MSA is often masked by biases from many ancillary indirect-coupling or phylogenetic effects. Here we present a novel method, PSICOV, which introduces the use of sparse inverse covariance estimation to the problem of protein contact prediction. Our method builds on work which had previously demonstrated corrections for phylogenetic and entropic correlation noise and allows accurate discrimination of direct from indirectly coupled mutation correlations in the MSA. Results PSICOV displays a mean precision substantially better than the best performing normalized mutual information approach and Bayesian networks. For 118 out of 150 targets, the L/5 (i.e. top-L/5 predictions for a protein of length L) precision for long-range contacts (sequence separation >23) was ≥ 0.5, which represents an improvement sufficient to be of significant benefit in protein structure prediction or model quality assessment. Availability The PSICOV source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/PSICOV.

778 citations

Journal ArticleDOI
TL;DR: This work describes DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them and shows that this predictor generates precise assignments of disordered protein binding regions and that it compares well with other publicly available tools.
Abstract: Motivation: A sizeable fraction of eukaryotic proteins contain intrinsically disordered regions (IDRs), which act in unfolded states or by undergoing transitions between structured and unstructured conformations. Over time, sequence-based classifiers of IDRs have become fairly accurate and currently a major challenge is linking IDRs to their biological roles from the molecular to the systems level. Results: We describe DISOPRED3, which extends its predecessor with new modules to predict IDRs and protein-binding sites within them. Based on recent CASP evaluation results, DISOPRED3 can be regarded as state of the art in the identification of IDRs, and our self-assessment shows that it significantly improves over DISOPRED2 because its predictions are more specific across the whole board and more sensitive to IDRs longer than 20 amino acids. Predicted IDRs are annotated as protein binding through a novel SVM based classifier, which uses profile data and additional sequence-derived features. Based on benchmarking experiments with full cross-validation, we show that this predictor generates precise assignments of disordered protein binding regions and that it compares well with other publicly available tools. Availability and implementation: http://bioinf.cs.ucl.ac.uk/disopred Contact: ku.ca.lcu@senoj.t.d Supplementary information: Supplementary data are available at Bioinformatics online.

691 citations

Journal ArticleDOI
Yuxiang Jiang1, Tal Ronnen Oron2, Wyatt T. Clark3, Asma R. Bankapur4  +153 moreInstitutions (59)
TL;DR: The second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function, was conducted by as mentioned in this paper. But the results of the CAFA2 assessment are limited.
Abstract: 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.

330 citations

Journal ArticleDOI
TL;DR: The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins to provide access to models published in the scientific literature, together with validating experimental data.
Abstract: The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins The database is designed to provide access to models published in the scientific literature, together with validating experimental data It is a relational database and it currently contains >74,000 models for approximately 240 proteins The system is accessible at http://wwwcaspurit/PMDB and allows predictors to submit models along with related supporting evidence and users to download them through a simple and intuitive interface Users can navigate in the database and retrieve models referring to the same target protein or to different regions of the same protein Each model is assigned a unique identifier that allows interested users to directly access the data

277 citations


Cited by
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Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations

Proceedings ArticleDOI
13 Aug 2016
TL;DR: Node2vec as mentioned in this paper learns a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes by using a biased random walk procedure.
Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

7,072 citations

Journal ArticleDOI
TL;DR: The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence- to-structure-to-function paradigm.
Abstract: The iterative threading assembly refinement (I-TASSER) server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional (3D) atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the confidence score of the modeling. This protocol provides new insights and guidelines for designing of online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.

5,792 citations

Journal ArticleDOI
Yang Zhang1
TL;DR: The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I- TASSER models.
Abstract: Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score) based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1]) of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 A for RMSD. The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available to the academic community at http://zhang.bioinformatics.ku.edu/I-TASSER .

4,754 citations

Journal ArticleDOI
TL;DR: The new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies less on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence.
Abstract: Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

3,902 citations