Literature DB >> 34324227

Assessment of protein model structure accuracy estimation in CASP14: Old and new challenges.

Sohee Kwon1, Jonghun Won1,2, Andriy Kryshtafovych3, Chaok Seok1,2.   

Abstract

In CASP, blind testing of model accuracy estimation methods has been conducted on models submitted by tertiary structure prediction servers. In CASP14, model accuracy estimation results were evaluated in terms of both global and local structure accuracy, as in the previous CASPs. Unlike the previous CASPs that did not show pronounced improvements in performance, the best single-model method (from the Baker group) showed an improved performance in CASP14, particularly in evaluating global structure accuracy when compared to both the best single-model methods in previous CASPs and the best multi-model methods in the current CASP. Although the CASP14 experiment on model accuracy estimation did not deal with the structures generated by AlphaFold2, new challenges that have arisen due to the success of AlphaFold2 are discussed.
© 2021 Wiley Periodicals LLC.

Entities:  

Keywords:  CASP14 assessment; estimation of protein model accuracy; protein model quality assessment; protein structure prediction

Mesh:

Substances:

Year:  2021        PMID: 34324227      PMCID: PMC8616788          DOI: 10.1002/prot.26192

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


  15 in total

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Authors:  Adam Zemla
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

2.  GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  Biophys J       Date:  2011-10-19       Impact factor: 4.033

3.  Assessment of predictions in the model quality assessment category.

Authors:  Domenico Cozzetto; Andriy Kryshtafovych; Michele Ceriani; Anna Tramontano
Journal:  Proteins       Date:  2007

4.  CAD-score: a new contact area difference-based function for evaluation of protein structural models.

Authors:  Kliment Olechnovič; Eleonora Kulberkytė; Ceslovas Venclovas
Journal:  Proteins       Date:  2012-09-29

5.  Assessment of model accuracy estimations in CASP12.

Authors:  Andriy Kryshtafovych; Bohdan Monastyrskyy; Krzysztof Fidelis; Torsten Schwede; Anna Tramontano
Journal:  Proteins       Date:  2017-09-08

6.  Assessment of protein model structure accuracy estimation in CASP13: Challenges in the era of deep learning.

Authors:  Jonghun Won; Minkyung Baek; Bohdan Monastyrskyy; Andriy Kryshtafovych; Chaok Seok
Journal:  Proteins       Date:  2019-08-30

7.  CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL.

Authors:  Andriy Kryshtafovych; Bohdan Monastyrskyy; Krzysztof Fidelis
Journal:  Proteins       Date:  2013-10-18

8.  The CAD-score web server: contact area-based comparison of structures and interfaces of proteins, nucleic acids and their complexes.

Authors:  Kliment Olechnovič; Ceslovas Venclovas
Journal:  Nucleic Acids Res       Date:  2014-05-16       Impact factor: 16.971

9.  Assessment of the assessment: evaluation of the model quality estimates in CASP10.

Authors:  Andriy Kryshtafovych; Alessandro Barbato; Krzysztof Fidelis; Bohdan Monastyrskyy; Torsten Schwede; Anna Tramontano
Journal:  Proteins       Date:  2013-08-31

10.  Improved protein structure refinement guided by deep learning based accuracy estimation.

Authors:  Naozumi Hiranuma; Hahnbeom Park; Minkyung Baek; Ivan Anishchenko; Justas Dauparas; David Baker
Journal:  Nat Commun       Date:  2021-02-26       Impact factor: 14.919

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  10 in total

1.  Critical assessment of methods of protein structure prediction (CASP)-Round XIV.

Authors:  Andriy Kryshtafovych; Torsten Schwede; Maya Topf; Krzysztof Fidelis; John Moult
Journal:  Proteins       Date:  2021-10-07

2.  Modeling SARS-CoV-2 proteins in the CASP-commons experiment.

Authors:  Andriy Kryshtafovych; John Moult; Wendy M Billings; Dennis Della Corte; Krzysztof Fidelis; Sohee Kwon; Kliment Olechnovič; Chaok Seok; Česlovas Venclovas; Jonghun Won
Journal:  Proteins       Date:  2021-10-05

3.  High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.

Authors:  Mu Gao; Peik Lund-Andersen; Alex Morehead; Sajid Mahmud; Chen Chen; Xiao Chen; Nabin Giri; Raj S Roy; Farhan Quadir; T Chad Effler; Ryan Prout; Subil Abraham; Wael Elwasif; N Quentin Haas; Jeffrey Skolnick; Jianlin Cheng; Ada Sedova
Journal:  Workshop Mach Learn HPC Environ       Date:  2021-12-27

4.  Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes.

Authors:  Mark van Breugel; Ivan Rosa E Silva; Antonina Andreeva
Journal:  Commun Biol       Date:  2022-04-05

5.  A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

Authors:  Raj S Roy; Farhan Quadir; Elham Soltanikazemi; Jianlin Cheng
Journal:  Bioinformatics       Date:  2022-02-04       Impact factor: 6.937

6.  A-Prot: protein structure modeling using MSA transformer.

Authors:  Yiyu Hong; Juyong Lee; Junsu Ko
Journal:  BMC Bioinformatics       Date:  2022-03-16       Impact factor: 3.169

7.  Estimation of model accuracy by a unique set of features and tree-based regressor.

Authors:  Mor Bitton; Chen Keasar
Journal:  Sci Rep       Date:  2022-08-18       Impact factor: 4.996

8.  Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs.

Authors:  Neeladri Sen; Ivan Anishchenko; Nicola Bordin; Ian Sillitoe; Sameer Velankar; David Baker; Christine Orengo
Journal:  Brief Bioinform       Date:  2022-07-18       Impact factor: 13.994

9.  Protein Model Quality Estimation Using Molecular Dynamics Simulation.

Authors:  Jason Kurniawan; Takashi Ishida
Journal:  ACS Omega       Date:  2022-07-05

10.  A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models.

Authors:  Yuma Takei; Takashi Ishida
Journal:  Bioengineering (Basel)       Date:  2022-03-15
  10 in total

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