TY - JOUR
T1 - Topology evaluation of models for difficult targets in the 14th round of the critical assessment of protein structure prediction (CASP14)
AU - Kinch, Lisa N.
AU - Pei, Jimin
AU - Kryshtafovych, Andriy
AU - Schaeffer, R. Dustin
AU - Grishin, Nick V.
N1 - Funding Information:
We thank the CASP organizers for their invitation for us to participate in CASP14, the protein crystallographers, NMR spectroscopists and cryo‐EM scientists that contributed targets and fellow assessors for helpful discussion about topology modeling performance. This research was supported by the US National Institute of General Medical Sciences (NIGMS/NIH) grants R01GM100482 (AK) and R35GM127390 (NVG) and Welch Foundation grant I‐1505 (NVG).
Publisher Copyright:
© 2021 Wiley Periodicals LLC.
PY - 2021/12
Y1 - 2021/12
N2 - This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.
AB - This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies.
KW - CASP14
KW - free modeling
KW - homology modeling
KW - machine learning
KW - protein structure prediction
KW - structural bioinformatics
KW - topology structure modeling evaluation
UR - http://www.scopus.com/inward/record.url?scp=85109835297&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85109835297&partnerID=8YFLogxK
U2 - 10.1002/prot.26172
DO - 10.1002/prot.26172
M3 - Article
C2 - 34240477
AN - SCOPUS:85109835297
SN - 0887-3585
VL - 89
SP - 1673
EP - 1686
JO - Proteins: Structure, Function and Bioinformatics
JF - Proteins: Structure, Function and Bioinformatics
IS - 12
ER -