CASP9 results compared to those of previous CASP experiments

By Andriy Kryshtafovych, Krzysztof Fidelis, John Moult. 2011

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The quality of structure models submitted to CASP9 is analyzed in the context of previous CASPs. Comparison methods are similar to those used in previous articles in this series, with the addition of new methods looking at model quality in regions not covered by a single best structural template, alignment accuracy, and progress for template‐free models. Progress in this CASP was again modest and statistically hard to validate. Nevertheless, there are several positive trends. There is an indication of improvement in overall model quality for the midrange of template‐based modeling difficulty, methods for identifying the best model from a set generated have improved, and there are strong indications of progress in the quality of template‐free models of short proteins. In addition, the new examination of a model quality in regions of model not covered by the best available template reveals better performance than had previously been apparent. © Proteins 2011; © 2011 Wiley‐Liss, Inc.

Subfields: Genetics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Critical Assessment of Methods of Protein Structure Prediction (CASP) – Progress and New directions in Round XI (2016, background): “As noted in previous papers in this series,54,55 in template free (FM) modeling, over the last decade there has been substantial progress in accuracy for small ( 100 residues) targets, building on earlier methodological advances but the techniques used have not appeared to scale to larger…”
Protein structure prediction from sequence variation (2012, background): “…de novo predictions from sequence, when not a single structure in a protein family is known, have been hard to achieve, as the pioneering Critical Assessment of Techniques for Protein Structure Prediction (CASP) evaluation of blinded predictions has demonstrated over the past two decades 5,6 .”
Assessment of hard target modeling in CASP12 reveals an emerging role of alignment‐based contact prediction methods (2018, methodology): “The data underlying these plots are available at the official CASP Prediction Center website an analysis of prediction performance over time reported by CASP9 29 indicated there was a large stepwise improvement in prediction accuracy from CASP round 1 to 5, followed by only slight improvements after”
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction (2012, methodology): “The contact prediction performance is evaluated using the standard accuracy measure [15]: Acc = TP/(TP+FP), where TP and FP are the true positive and false positive predicted contacts, respectively.”
Assessment of CASP10 contact‐assisted predictions (2014, methodology): “12–18 But constructing an alignment for marginal or poor predictions can be problematic, and GDT can be less reliable for such predictions,12–14,19–21 which is why we also used QCS, a score that is not alignment dependent.”
Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks (2014, methodology): “As a test dataset in the reconstruction process we use 27 free-modelling and 112 comparative-modelling CASP9 targets [46].”