Literature DB >> 31587357

Assessing the accuracy of contact predictions in CASP13.

Rojan Shrestha1, Eduardo Fajardo1, Nelson Gil1, Krzysztof Fidelis2, Andriy Kryshtafovych2, Bohdan Monastyrskyy2, Andras Fiser1.   

Abstract

The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  CASP13; contact prediction; protein structure modeling

Mesh:

Substances:

Year:  2019        PMID: 31587357      PMCID: PMC6851495          DOI: 10.1002/prot.25819

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  65 in total

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Authors:  P Fariselli; P Riccobelli; R Casadio
Journal:  Proteins       Date:  1999-08-15

2.  The Protein Information Resource: an integrated public resource of functional annotation of proteins.

Authors:  Cathy H Wu; Hongzhan Huang; Leslie Arminski; Jorge Castro-Alvear; Yongxing Chen; Zhang-Zhi Hu; Robert S Ledley; Kali C Lewis; Hans-Werner Mewes; Bruce C Orcutt; Baris E Suzek; Akira Tsugita; C R Vinayaka; Lai-Su L Yeh; Jian Zhang; Winona C Barker
Journal:  Nucleic Acids Res       Date:  2002-01-01       Impact factor: 16.971

3.  Reliability of assessment of protein structure prediction methods.

Authors:  Marc A Marti-Renom; M S Madhusudhan; András Fiser; Burkhard Rost; Andrej Sali
Journal:  Structure       Date:  2002-03       Impact factor: 5.006

4.  PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments.

Authors:  David T Jones; Daniel W A Buchan; Domenico Cozzetto; Massimiliano Pontil
Journal:  Bioinformatics       Date:  2011-11-17       Impact factor: 6.937

5.  Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era.

Authors:  Hetunandan Kamisetty; Sergey Ovchinnikov; David Baker
Journal:  Proc Natl Acad Sci U S A       Date:  2013-09-05       Impact factor: 11.205

6.  The choice of sequence homologs included in multiple sequence alignments has a dramatic impact on evolutionary conservation analysis.

Authors:  Nelson Gil; Andras Fiser
Journal:  Bioinformatics       Date:  2019-01-01       Impact factor: 6.937

7.  A modular perspective of protein structures: application to fragment based loop modeling.

Authors:  Narcis Fernandez-Fuentes; Andras Fiser
Journal:  Methods Mol Biol       Date:  2013

8.  Different sequence environments of amino acid residues involved and not involved in long-range interactions in proteins.

Authors:  E Tüdös; A Fiser; I Simon
Journal:  Int J Pept Protein Res       Date:  1994-02

9.  MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

Authors:  David T Jones; Tanya Singh; Tomasz Kosciolek; Stuart Tetchner
Journal:  Bioinformatics       Date:  2014-11-26       Impact factor: 6.937

10.  Disentangling direct from indirect co-evolution of residues in protein alignments.

Authors:  Lukas Burger; Erik van Nimwegen
Journal:  PLoS Comput Biol       Date:  2010-01-01       Impact factor: 4.475

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  17 in total

1.  Fold recognition by scoring protein maps using the congruence coefficient.

Authors:  Pietro Di Lena; Pierre Baldi
Journal:  Bioinformatics       Date:  2021-05-01       Impact factor: 6.937

2.  Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.

Authors:  Yang Li; Chengxin Zhang; Eric W Bell; Wei Zheng; Xiaogen Zhou; Dong-Jun Yu; Yang Zhang
Journal:  PLoS Comput Biol       Date:  2021-03-26       Impact factor: 4.475

3.  Assessing the accuracy of contact and distance predictions in CASP14.

Authors:  Victoria Ruiz-Serra; Camila Pontes; Edoardo Milanetti; Andriy Kryshtafovych; Rosalba Lepore; Alfonso Valencia
Journal:  Proteins       Date:  2021-10-03

4.  Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).

Authors:  S Geethu; E R Vimina
Journal:  Protein J       Date:  2022-08-25       Impact factor: 4.000

5.  Assessment of protein assembly prediction in CASP13.

Authors:  Dmytro Guzenko; Aleix Lafita; Bohdan Monastyrskyy; Andriy Kryshtafovych; Jose M Duarte
Journal:  Proteins       Date:  2019-08-27

6.  Study of Real-Valued Distance Prediction for Protein Structure Prediction with Deep Learning.

Authors:  Jin Li; Jinbo Xu
Journal:  Bioinformatics       Date:  2021-05-07       Impact factor: 6.937

7.  A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.

Authors:  Bian Li; Jeffrey Mendenhall; John A Capra; Jens Meiler
Journal:  J Proteome Res       Date:  2021-07-08       Impact factor: 5.370

8.  Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.

Authors:  Pengshuo Yang; Wei Zheng; Kang Ning; Yang Zhang
Journal:  Proc Natl Acad Sci U S A       Date:  2021-12-07       Impact factor: 12.779

Review 9.  Deep learning methods in protein structure prediction.

Authors:  Mirko Torrisi; Gianluca Pollastri; Quan Le
Journal:  Comput Struct Biotechnol J       Date:  2020-01-22       Impact factor: 7.271

10.  Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation.

Authors:  Lim Heo; Collin F Arbour; Giacomo Janson; Michael Feig
Journal:  J Chem Theory Comput       Date:  2021-02-09       Impact factor: 6.006

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