Critical Assessment of Methods of Protein Structure Prediction (CASP) - Round IX

By John Moult, Krzysztof Fidelis, Andriy Kryshtafovych, Anna Tramontano. 2011

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This article is an introduction to the special issue of the journal PROTEINS, dedicated to the ninth 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. Methods for modeling protein structure continue to advance, although at a more modest pace than in the early CASP experiments. CASP developments of note are indications of improvement in model accuracy for some classes of target, an improved ability to choose the most accurate of a set of generated models, and evidence of improvement in accuracy for short “new fold” models. In addition, a new analysis of regions of models not derivable from the most obvious template structure has revealed better performance than expected. Proteins 2011; © 2011 Wiley‐Liss, Inc.

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

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Citations in the corpus

DEEPCON: Protein Contact Prediction using Dilated Convolutional Neural Networks with Dropout (2019, methodology): “Following the standard in the field [4], as evaluation metrics of contact prediction accuracy, we use the precision of top L/5 predicted long-range contacts (PTOP-L/5-LR), where L is the length of the protein sequence.”
Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “CASP228: The CASP228 dataset consists of the 228 targets from the CASP 10 and 11 experiments (Moult et al., 2014; Moult et al., 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).”