Critical assessment of methods of protein structure prediction (CASP)-Round XII
TLDR
The most recent Critical Assessment of Structure Prediction (CASP12) as mentioned in this paper was held in 2016, and the state of the art in modeling protein structure from amino acid sequence.Abstract:
This article reports the outcome of the 12th round of Critical Assessment of Structure Prediction (CASP12), held in 2016. CASP is a community experiment to determine the state of the art in modeling protein structure from amino acid sequence. Participants are provided sequence information and in turn provide protein structure models and related information. Analysis of the submitted structures by independent assessors provides a comprehensive picture of the capabilities of current methods, and allows progress to be identified. This was again an exciting round of CASP, with significant advances in 4 areas: (i) The use of new methods for predicting three-dimensional contacts led to a two-fold improvement in contact accuracy. (ii) As a consequence, model accuracy for proteins where no template was available improved dramatically. (iii) Models based on a structural template showed overall improvement in accuracy. (iv) Methods for estimating the accuracy of a model continued to improve. CASP continued to develop new areas: (i) Assessing methods for building quaternary structure models, including an expansion of the collaboration between CASP and CAPRI. (ii) Modeling with the aid of experimental data was extended to include SAXS data, as well as again using chemical cross-linking information. (iii) A team of assessors evaluated the suitability of models for a range of applications, including mutation interpretation, analysis of ligand binding properties, and identification of interfaces. This article describes the experiment and summarizes the results. The rest of this special issue of PROTEINS contains papers describing CASP12 results and assessments in more detail.read more
Citations
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Posted ContentDOI
Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences
Alexander Rives,Siddharth Goyal,Joshua Meier,Demi Guo,Myle Ott,C. Lawrence Zitnick,Jerry Ma,Rob Fergus,Rob Fergus +8 more
TL;DR: This work uses unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity, enabling state-of-the-art supervised prediction of mutational effect and secondary structure, and improving state- of- the-art features for long-range contact prediction.
Journal ArticleDOI
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives,Alexander Rives,Joshua Meier,Tom Sercu,Siddharth Goyal,Zeming Lin,Jason Liu,Demi Guo,Myle Ott,C. Lawrence Zitnick,Jerry Ma,Jerry Ma,Rob Fergus +12 more
TL;DR: This paper used unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity, which contains information about biological properties in its representations.
Journal ArticleDOI
QMEANDisCo-distance constraints applied on model quality estimation.
Gabriel Studer,Gabriel Studer,Christine Rempfer,Christine Rempfer,Andrew Waterhouse,Andrew Waterhouse,Rafal Gumienny,Rafal Gumienny,Juergen Haas,Juergen Haas,Torsten Schwede,Torsten Schwede +11 more
TL;DR: The single model composite score QMEAN is extended by introducing a consensus-based distance constraint (DisCo) score, which combines the accuracy of consensus methods with the broad applicability of single model approaches and demonstrates that CASP models are not the ideal data source to train predictive methods for model quality estimation.
Journal ArticleDOI
Critical assessment of methods of protein structure prediction (CASP)-Round XIII
TL;DR: The most recent Critical Assessment of Structure Prediction (CASP13) as discussed by the authors assesses the state of the art in modeling protein structure from amino acid sequence, and the results showed dramatic improvements in three-dimensional structure accuracy.
Posted ContentDOI
Evaluating Protein Transfer Learning with TAPE
Roshan Rao,Nicholas Bhattacharya,Neil Thomas,Yan Duan,Xi Chen,John Canny,John Canny,Pieter Abbeel,Yun S. Song +8 more
TL;DR: TAPE as discussed by the authors is a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology, and it is designed to test biologically relevant generalization that transfers to real-life scenarios.
References
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Journal ArticleDOI
Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
Nevan J. Krogan,Gerard Cagney,Gerard Cagney,Haiyuan Yu,Gouqing Zhong,Xinghua Guo,Alexandr Ignatchenko,Joyce Li,Shuye Pu,Nira Datta,Aaron Tikuisis,Thanuja Punna,José M. Peregrín-Alvarez,Michael Shales,Xin Zhang,Michael Davey,Mark D. Robinson,Alberto Paccanaro,James E. Bray,Anthony Sheung,Bryan Beattie,Dawn Richards,Veronica Canadien,Atanas Iliev Lalev,Frank Mena,Peter D Wong,Andrei Starostine,Myra M. Canete,James Vlasblom,Samuel Wu,Chris Orsi,Sean R. Collins,Shamanta Chandran,Robin Haw,Jennifer J. Rilstone,Kiran Gandi,Natalie J. Thompson,Gabe Musso,Peter St Onge,Shaun Ghanny,Mandy H. Y. Lam,Gareth Butland,Amin M. Altaf-Ul,Shigehiko Kanaya,Ali Shilatifard,Erin K. O'Shea,Jonathan S. Weissman,C. James Ingles,Timothy P. Hughes,John Parkinson,Mark Gerstein,Shoshana J. Wodak,Andrew Emili,Jack Greenblatt +53 more
TL;DR: T tandem affinity purification was used to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae to identify protein–protein interactions, which will help future studies on individual proteins as well as functional genomics and systems biology.
Journal ArticleDOI
HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
TL;DR: An open-source, general-purpose tool that represents both query and database sequences by profile hidden Markov models (HMMs): 'HMM-HMM–based lightning-fast iterative sequence search' (HHblits; http://toolkit.genzentrum.lmu.de/hhblits/).
Journal ArticleDOI
The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases.
Sandra Orchard,Mais G. Ammari,Bruno Aranda,Lionel Breuza,Leonardo Briganti,Fiona Broackes-Carter,Nancy H. Campbell,Gayatri Chavali,Carol Chen,Noemi del-Toro,Margaret Duesbury,Marine Dumousseau,Eugenia Galeota,Ursula Hinz,Marta Iannuccelli,Sruthi Jagannathan,Rafael C. Jimenez,Jyoti Khadake,Astrid Lagreid,Luana Licata,Ruth C. Lovering,Birgit H M Meldal,Anna N. Melidoni,Mila Milagros,Daniele Peluso,Livia Perfetto,Pablo Porras,Arathi Raghunath,Sylvie Ricard-Blum,Bernd Roechert,Andre Stutz,Michael Tognolli,Kim Van Roey,Gianni Cesareni,Henning Hermjakob +34 more
TL;DR: All data manually curated by the MINT curators have been moved into the IntAct database at EMBL-EBI and are merged with the existing IntAct dataset.
Journal ArticleDOI
Protein Structure Prediction and Structural Genomics
David Baker,Andrej Sali +1 more
TL;DR: This Viewpoint begins by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes.
Journal ArticleDOI
The SWISS-MODEL Repository: new features and functionalities.
Jürgen Kopp,Torsten Schwede +1 more
TL;DR: The SWiss-MODEL Repository is a database of annotated 3D protein structure models generated by the SWISS- MODEL homology-modelling pipeline that reflects the current state of sequence and structure databases.
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