Critical Assessment of Methods of Protein Structure Prediction (CASP) – Progress and New directions in Round XI

By John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede, Anna Tramontano. 2016

224 citations recorded by the source corpus.

Modeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling. Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other areas. Proteins 2016; 84(Suppl 1):4–14. © 2016 Wiley Periodicals, Inc.

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

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, neutral): “The Test (59) and CASP11 (60) test sets evaluate with sequence identity holdout at 25%, the CASP12 (61) test set implements a temporal holdout with the structural training data, and the CASP13 (58) experiment implements a full temporal holdout of both the pretraining and training data.”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, methodology): “34,35 The area was further strengthened in CASP12 by introducing a separate category in which the accuracy of the predicted complexes for all nonmonomeric targets was evaluated by a dedicated team of assessors, 36 and a subset of targets meeting CAPRI criteria was separately assessed in…”
Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model (2016, methodology): “train this deep neural network using a subset of proteins with solved structures and then test its performance on public data including the CASP [26, 27] and CAMEO [28] targets as well as membrane proteins.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “Alignment Generation: Co-evolution analyses depend on multiple sequence alignments (MSAs).”
High‐accuracy protein structure prediction in CASP14 (2021, background): “9 Only in 2018, in CASP13, was a jump of accuracy seen due to the inclusion of deep-learning methods for contact prediction.”
A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments (2019, methodology): “to provide an objective evaluation of state-of-the-art methodologies in protein structure prediction.(1) Predictors submit models of target”
Evaluation of template-based modeling in CASP13 (2019, methodology): “Using this measure of difficulty, progress in improving overall fold accuracy as judged by GDT_TS had seemed to stall around the time of CASP11.(38) However, substantial improvements were seen again in CASP12.”
Computational protein structure refinement: Almost there, yet still so far to go (2017, background): “21 22 – 24 Predicted structural models generated with today ’ s methods can be impressively accurate, 25,26 but they still often do not reach experimental accuracy [considered to be 1 Å root-mean-square deviation (RMSD) for all heavy atoms].”
VoroMQA: Assessment of protein structure quality using interatomic contact areas (2017, methodology): “Having the derivation of pseudo-energy values defined using equations (1-7), let us describe how the derived values are used for scoring protein structures.”