Evaluation of the template-based modeling in CASP12

By Andriy Kryshtafovych, Bohdan Monastyrskyy, Krzysztof Fidelis, John Moult, Torsten Schwede, Anna Tramontano. 2017

55 citations recorded by the source corpus.

The article describes results of numerical evaluation of CASP12 models submitted on targets for which structural templates could be identified and for which servers produced models of relatively high accuracy. The emphasis is on analysis of details of models, and how well the models compete with experimental structures. Performance of contributing research groups is measured in terms of backbone accuracy, all‐atom local geometry, and the ability to estimate local errors in models. Separate analyses for all participating groups and automatic servers were carried out. Compared with the last CASP, two years ago, there have been significant improvements in a number of areas, particularly the accuracy of protein backbone atoms, accuracy of sequence alignment between models and available structures, increased accuracy over that which can be obtained from simple copying of a closest template, and accuracy of modeling of sub‐structures not present in the closest template. These advancements are likely associated with more effective strategies to build non‐template regions of the targets ab initio, better algorithms to combine information from multiple templates, enhanced refinement methods, and better methods for estimating model accuracy.

Subfields: Hardware and Architecture, Materials Chemistry, Molecular Biology

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “An additional new feature is the provision of a link that allows users to download all of the ASE annotated models in PDB format (with the error estimates, in Angstroms, in place of temperature factor data) as a zipped archive.”
Critical Assessment of Methods of Protein Structure Prediction (CASP) – Round XII (2017, background): “Several factors contributed to this, including more accurate alignment of the target sequence to that of the most useful template, and improved accuracy of regions not covered by the principal template (see data in the template based modeling assessment article 15 ).”
End-to-end differentiable learning of protein structure (2018, background): “In general, small gains in accuracy at the top end are difficult, with only minimal gains obtained over a 10-year time span from CASP 6 to CASP 11 (Kryshtafovych et al., 2018).”
High‐accuracy protein structure prediction in CASP14 (2021, methodology): “In CASP13, Croll et al. 15 adopted, and built on, the same over-all ranking score used for TBM models in CASP12, 16 which was based on five metrics: (1) GDT_HA, the high-accuracy version of the Global Distance Test (GDT), rewarding parts of the target that could be reproduced with high precision 3…”
Protein Data Bank: the single global archive for 3D macromolecular structure data (2018, methodology): “Release of PDB data in two phases is intended to assist the computational biology community in operating various prediction challenges, including CASP (32), CAPRI (33), CAMEO (34) and D3R (35).”
Driven to Near-Experimental Accuracy by Refinement via Molecular Dynamics Simulations (2019, methodology): “During CASP13, the residue-wise errors estimated in this fashion showed good agreement in terms of the accuracy self estimation (ASE) measure.(55) The average ASE was 87.”
Evaluation of template-based modeling in CASP13 (2019, methodology): “In line with previous rounds including CASP12, we use here a linear combination of coverage by the best structural template and the sequence identity between the target and the best template.”
Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations (2021, methodology): “It is important to note that sequence-based contact-map predictions have been of little to no use for TBMuntil themost recent CASP experiments (Kryshtafovych et al., 2018; Zhang et al., 2018).”
Continuous Automated Model EvaluatiOn (CAMEO) complementing the critical assessment of structure prediction in CASP12 (2018, background): “For overall good models, the accuracy of binding site details does not necessarily correlate with overall model quality[45], and the assessment of binding site quality in models is therefore a relevant measure.”
Detecting distant-homology protein structures by aligning deep neural-network based contact maps (2019, background): “While various advanced approaches have been proposed over the last few decades [3–8], template-based modeling (TBM), which is designed to construct structural models using templates of similar folds collected from the Protein Data Bank (PDB), remains still the most accurate and reliable protein stru”