|Title||Critical Assessment of Methods of Protein Structure Prediction (CASP) - Round XIV.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Kryshtafovych, A, Schwede, T, Topf, M, Fidelis, K, Moult, J|
|Date Published||2021 Sep 17|
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 rivalling 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.