High-accuracy protein structure prediction in CASP14.
CASP14
high-accuracy
molecular replacement
Journal
Proteins
ISSN: 1097-0134
Titre abrégé: Proteins
Pays: United States
ID NLM: 8700181
Informations de publication
Date de publication:
12 2021
12 2021
Historique:
revised:
16
06
2021
received:
05
05
2021
accepted:
23
06
2021
pubmed:
5
7
2021
medline:
26
2
2022
entrez:
4
7
2021
Statut:
ppublish
Résumé
The application of state-of-the-art deep-learning approaches to the protein modeling problem has expanded the "high-accuracy" category in CASP14 to encompass all targets. Building on the metrics used for high-accuracy assessment in previous CASPs, we evaluated the performance of all groups that submitted models for at least 10 targets across all difficulty classes, and judged the usefulness of those produced by AlphaFold2 (AF2) as molecular replacement search models with AMPLE. Driven by the qualitative diversity of the targets submitted to CASP, we also introduce DipDiff as a new measure for the improvement in backbone geometry provided by a model versus available templates. Although a large leap in high-accuracy is seen due to AF2, the second-best method in CASP14 out-performed the best in CASP13, illustrating the role of community-based benchmarking in the development and evolution of the protein structure prediction field.
Substances chimiques
Proteins
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1687-1699Subventions
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/S007105/1
Pays : United Kingdom
Informations de copyright
© 2021 The Authors. Proteins: Structure, Function, and Bioinformatics published by Wiley Periodicals LLC.
Références
Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP)-round XIII. Proteins. 2019;87:1011-1020.
Zemla A, Venclovas C, Moult J, Fidelis K. Processing and analysis of CASP3 protein structure predictions. Proteins. 1999;3:22-29.
Zemla A. LGA: a method for finding 3D similarities in protein structures. Nucleic Acids Res. 2003;31:3370-3374.
Moult J, Hubbard T, Fidelis K, Pedersen JT. Critical assessment of methods of protein structure prediction (CASP): round III. Proteins. 1999;37(Suppl 3):2-6.
Dunbrack RL Jr. Comparative modeling of CASP3 targets using PSI-BLAST and SCWRL. Proteins. 1999;3:81-87.
Moult J, Fidelis K, Zemla A, Hubbard T. Critical assessment of methods of protein structure prediction (CASP): round IV. Proteins. 2001;45(Suppl 5):2-7.
Koretke KK, Russell RB, Lupas AN. Fold recognition from sequence comparisons. Proteins. 2001;45(Suppl 5):68-75.
Kopp J, Bordoli L, Battey JND, Kiefer F, Schwede T. Assessment of CASP7 predictions for template-based modeling targets. Proteins. 2007;69(Suppl 8):38-56.
Moult J, Fidelis K, Kryshtafovych A, Schwede T, Tramontano A. Critical assessment of methods of protein structure prediction: Progress and new directions in round XI. Proteins. 2016;69(Suppl 1):4-14.
Monastyrskyy B, D'Andrea D, Fidelis K, Tramontano A, Kryshtafovych A. New encouraging developments in contact prediction: assessment of the CASP11 results. Proteins. 2016;84(Suppl 1):131-144.
Senior AW, Evans R. Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13). Proteins. 2019;87:1141-1148.
Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D. Improved protein structure prediction using predicted inter-residue orientations. Proc Natl Acad Sci USA. 2020;117:1496-1503. https://doi.org/10.1101/846279
Kinch LN, Pei J, Kryshtafovych A, Schaeffer RD, Grishin NV. Topology evaluation of models for difficult targets in the 14th round of the critical assessment of protein structure prediction (CASP14). Proteins: Structure, Function, and Bioinformatics. 2021;89(12):1673-1686. https://doi.org/10.1002/prot.26172.
Kryshtafovych A, Monastyrskyy B, Fidelis K. CASP11 statistics and the prediction center evaluation system. Proteins. 2016;84(Suppl 1):15-19.
Croll TI, Sammito MD, Kryshtafovych A, Read RJ. Evaluation of template-based modeling in CASP13. Proteins. 2019;87:1113-1127.
Kryshtafovych A, Monastyrskyy B. Evaluation of the template-based modeling in CASP12. Proteins. 2018;86(Suppl 1):321-334.
Mariani V, Biasini M, Barbato A, Schwede T. lDDT: a local superposition-free score for comparing protein structures and models using distance difference tests. Bioinformatics. 2013;29:2722-2728.
Olechnovič K, Kulberkytė E, Venclovas C. CAD-score: a new contact area difference-based function for evaluation of protein structural models. Proteins. 2013;81:149-162.
Kryshtafovych A, Monastyrskyy B, Fidelis K. CASP prediction center infrastructure and evaluation measures in CASP10 and CASP ROLL. Proteins. 2014;82(Suppl 2):7-13.
Pereira J, Lamzin VS. A distance geometry-based description and validation of protein main-chain conformation. IUCrJ. 2017;4:657-670.
Langer G, Cohen SX, Lamzin VS, Perrakis A. Automated macromolecular model building for X-ray crystallography using ARP/wARP version 7. Nat Protoc. 2008;3:1171-1179.
Weis F, Beckers M, von der Hocht I, Sachse C. Elucidation of the viral disassembly switch of tobacco mosaic virus. EMBO Rep. 2019;20:e48451.
Winn MD, Ballard CC. Overview of the CCP4 suite and current developments. Acta Crystallogr D Biol Crystallogr. 2011;67:235-242.
Söding J. Protein homology detection by HMM-HMM comparison. Bioinformatics. 2005;21:951-960.
Bibby J, Keegan RM, Mayans O, Winn MD, Rigden DJ. AMPLE: a cluster-and-truncate approach to solve the crystal structures of small proteins using rapidly computed ab initio models. Acta Crystallogr D Biol Crystallogr. 2012;68:1622-1631.
Rigden DJ, Thomas JMH. Ensembles generated from crystal structures of single distant homologues solve challenging molecular-replacement cases in AMPLE. Acta Crystallogr D Struct Biol. 2018;74:183-193.
Adams PD, Afonine PV. PHENIX: a comprehensive python-based system for macromolecular structure solution. Acta Crystallogr D Biol Crystallogr. 2010;66:213-221.
Rodrigues JPGLM, Teixeira JMC, Trellet M, Bonvin AMJ. pdb-tools: a swiss army knife for molecular structures. F1000Res. 2018;7:1961.
Shapovalov MV, Dunbrack RL Jr. A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. Structure. 2011;19:844-858.
Dunbrack RL, Karplus M. Backbone-dependent rotamer library for proteins application to side-chain prediction. J Mol Biol. 1993;230:543-574.
Uziela K, Menéndez Hurtado D, Shu N, Wallner B, Elofsson A. ProQ3D: improved model quality assessments using deep learning. Bioinformatics. 2017;33:1578-1580.
Igashov I, Olechnovič L, Kadukova M, Venclovas Č, Grudinin S. VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures. Bioinformatics. 2021. https://doi.org/10.1093/bioinformatics/btab118
Poupon A. Voronoi and Voronoi-related tessellations in studies of protein structure and interaction. Curr Opin Struct Biol. 2004;14:233-241.
McCoy AJ, Grosse-Kunstleve RW, Adams PD, Winn MD, Storoni LC, Read RJ. Phaser crystallographic software. J Appl Cryst. 2007;40:658-674.
McCoy AJ, Sammito MD, Read RJ. Possible implications of AlphaFold2 for crystallographic phasing by molecular replacement. bioRxiv. 2021. https://doi.org/10.1101/2021.05.18.444614
Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP)-round XIV. Proteins. 2021;89(12):1607-1617. https://doi.org/10.1002/prot.26237