Evaluation of template-based modeling in CASP13

By T. Croll, M. Sammito, Andriy Kryshtafovych, Randy J. Read. 2019

47 citations recorded by the source corpus.

Performance in the template‐based modeling (TBM) category of CASP13 is assessed here, using a variety of metrics. Performance of the predictor groups that participated is ranked using the primary ranking score that was developed by the assessors for CASP12. This reveals that the best results are obtained by groups that include contact predictions or inter‐residue distance predictions derived from deep multiple sequence alignments. In cases where there is a good homolog in the wwPDB (TBM‐easy category), the best results are obtained by modifying a template. However, for cases with poorer homologs (TBM‐hard), very good results can be obtained without using an explicit template, by deep learning algorithms trained on the wwPDB. Alternative metrics are introduced, to allow testing of aspects of structural models that are not addressed by traditional CASP metrics. These include comparisons to the main‐chain and side‐chain torsion angles of the target, and the utility of models for solving crystal structures by the molecular replacement method. The alternative metrics are poorly correlated with the traditional metrics, and it is proposed that modeling has reached a sufficient level of maturity that the best models should be expected to satisfy this wider range of criteria.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

High‐accuracy protein structure prediction in CASP14 (2021, methodology): “…other terms to evaluate the geometric fitting of models: the (1) backbone and (2) side-chain deviation scores, which measure the local difference of torsion angles in the model relative to the target 15 ; and (3) the MolProbity clashscore, which assesses the number of serious clashes in the model.”
Protein storytelling through physics (2020, methodology): “Over the past half-decade, physics-based methods have become fast enough to enter the protein modeling competitions ( 89 , 90 ).”
Target Classification in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14). (2021, methodology): “At the same time, an inadvertent omission of splitting a target with a conformational change led to a noted performance outlier in the CASP13 high accuracy modeling category.(17) For this round of CASP, sets of high-performing models existed that did not follow the general trend of the remaining pre”
Cryo-EM targets in CASP13: overview and evaluation of results (2019, result): “These results are in agreement with the results on all CASP13 targets reported by CASP13 TBMand FMassessors.(14,15)”