Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XIV

By Andriy Kryshtafovych, Torsten Schwede, Maya Topf, Krzysztof Fidelis, John Moult. 2021

442 citations recorded by the source corpus.

Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three‐dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14), deep‐learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein‐folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance. Here, we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter‐residue contacts and distances, are also described.

Subfields: Materials Chemistry, Molecular Biology, Evaluation & Benchmarks, Computational Biology & Bioinformatics

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Citations in the corpus

High-resolution de novo structure prediction from primary sequence (2022, methodology): “…models, which has been demonstrated to be able to extract biologically meaningful representations for various tasks such as protein contact prediction ( 11–14 ), protein function prediction ( 15,16 ), zero-shot mutation effect prediction ( 17 ), and viral evolution and escape prediction ( 18, 19 ).”
High‐accuracy protein structure prediction in CASP14 (2021, background): “1,11 Now, in CASP14 we saw a further leap in model accuracy, 36 with the best model for targets at any difficulty level reaching a GDT_TS above 90%, a range at the level of experimental accuracy.”
Predicting protein flexibility with AlphaFold (2023, methodology): “The optional parameter db_preset was set to be full_dbs to invoke all genetic (sequence) databases that AF2 used at CASP14 32 – 34 so that the multiple sequence alignment quality was optimized.”
Assessment of Prediction Methods for Protein Structures Determined by NMR in CASP14: Impact of AlphaFold2 (2021, unknown): “5 Å.(1) Buried sidechain conformations in these blind predictions of protein structure are also generally”
Topology Evaluation of Models for Difficult Targets in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14) (2021, methodology): “In fact, CASP14 servers provided topologically correct models that outperformed the top known templates for most target EUs in the assessment and outperformed all CASP13 groups 20 .”
How accurately can one predict drug binding modes using AlphaFold models? (2023, background): “…protein structure prediction competition—AlphaFold 2 (AF2) demonstrated an unprecedented ability to predict protein structures with atomic accuracy, sometimes rivaling the accuracy of certain experimental methods for structure determination ( Kryshtafovych et al., 2021 ; Tejero et al., 2022 ).”
Applying and improving AlphaFold at CASP14 (2021, background): “8 by the next best group.(5) The system predicted high-accuracy structures (GDT_TS(6) > 70, best of five) for 87 out of 92 domains, structures on par with experimental”
Rethinking Protein Drug Design with Highly Accurate Structure Prediction of Anti-CRISPR Proteins (2021, methodology): “We obtained the Zpd and TM-score for the CASP14 set of AlphaFold2 from the CASP14 assessment scores [41], whereas Zgt of CASP14 and Zgt and Zpd of Set A and Set C were calculated using DaliLite.”