Critical assessment of methods of protein structure prediction (CASP) — round x

By John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Torsten Schwede, Anna Tramontano. 2013

460 citations recorded by the source corpus.

This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three‐dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi‐domain and multi‐subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non‐template regions. Proteins 2014; 82(Suppl 2):1–6. © 2013 Wiley Periodicals, Inc.

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

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Citations in the corpus

I-TASSER server: new development for protein structure and function predictions (2015, positive): “Since CASP9, however, a new ab initio structure prediction approach, QUARK (25), has been introduced to the Zhang-Server pipeline to recognize and sort templates for the hard free modeling (FM) targets (26,27).”
BERTology Meets Biology: Interpreting Attention in Protein Language Models (2020, methodology): “We use two protein sequence datasets from the TAPE repository for the analysis: the ProteinNet dataset [3, 10, 25, 50] and the Secondary Structure dataset [10, 36, 50, 58].”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “In parallel, given the many obstacles to experimental structure determination, computational prediction of protein structure remains one of the longest-standing challenges in computational biology (Moult et al., 2014, 2016).”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “105 proteins from the CASP9 dataset and 93 proteins from the CASP10 dataset were selected according to the availability of crystal structure[43, 44].”
Folding membrane proteins by deep transfer learning (2017, background): “CMAPpro (Di Lena et al., 2012) uses a deep architecture, but its performance was worse than MetaPSICOV in CASP11 (Monastyrskyy et al., 2015).”
A Web Resource for Standardized Benchmark Datasets, Metrics, and Rosetta Protocols for Macromolecular Modeling and Design (2015, background): “Organizations such as CASP [11] and CAPRI [12] create blind prediction tests for problems in protein structure prediction, protein-protein docking, and other applications, but many questions in the field of macromolecular modeling and design could also benefit from canonical benchmarks such as those”
Intrinsically Disordered Proteins Studied by NMR Spectroscopy (2015, methodology): “In the case of structure prediction, this community-wide strategy has been implemented and optimised in the series of Critical Assessment of Protein Structure Prediction (CASP)1 exercises, which have run every 2 years since 1994 (Moult et al. 2014).”
Tools for Predicting the Functional Impact of Nonsynonymous Genetic Variation (2016, background): “…ﬁ eld could bene ﬁ t greatly from the assessment of truly blinded predictions at regular intervals, similarly to the Critical Assessment of Structure Prediction experiments (Moult et al. 2014) that proved their utility in the ﬁ eld of protein structure prediction from primary amino acid sequence.”