A further leap of improvement in tertiary structure prediction in CASP13 prompts new routes for future assessments

By Luciano A. Abriata, Giorgio Tamò, M. dal Peraro. 2019

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We present our assessment of tertiary structure predictions for hard targets in Critical Assessment of Structure Prediction round 13 (CASP13). The analysis includes (a) assignment and discussion of best models through scores‐aided visual inspection of models for each evaluation unit (EU); (b) ranking of predictors resulting from this evaluation and from global scores; and (c) evaluation of progress, state of the art, and current limitations of protein structure prediction. We witness a sizable improvement in tertiary structure prediction building on the progress observed from CASP11 to CASP12, with (a) top models reaching backbone RMSD <3 å for several EUs of size <150 residues, contributed by many groups; (b) at least one model that roughly captures global topology for all EUs, probably unprecedented in this track of CASP; and (c) even quite good models for full, unsplit targets. Better structure predictions are brought about mainly by improved residue‐residue contact predictions, and since this CASP also by distance predictions, achieved through state‐of‐the‐art machine learning methods which also progressed to work with slightly shallower alignments compared to CASP12. As we reach a new realm of tertiary structure prediction quality, new directions are proposed and explored for future CASPs: (a) dropping splitting into EUs, (b) rethinking difficulty metrics probably in terms of contact and distance predictions, (c) assessing also side chains for models of high backbone accuracy, and (d) assessing residue‐wise and possibly residue‐residue quality estimates.

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

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Citations in the corpus

Improved protein structure prediction using predicted interresidue orientations (2019, background): “The ω dihedral measures rotation along the virtual axis connecting the Cβ atoms of the two residues, and θ12, φ12 (θ21, φ21) angles define the direction in which the Cβ atom of residue 2 (1) is seen from residue 1 (2).”
Introducing “best single template” models as reference baseline for the Continuous Automated Model Evaluation (CAMEO) (2019, methodology): “dent blind assessments in the form of the community experiment “Critical Assessment of Techniques in Structure Prediction” (CASP).(2,3) CASP is organized every 2 years, with experts assessing”
Target Classification in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14). (2021, methodology): “In fact, the substantial progress in state-of-the-art deep learning methods from the previous CASP13(18) called for a suggestion to cease splitting targets into EUs for the topology category.(39) At the same time, an inadvertent omission of splitting a target with a conformational change led to a no”
Deep learning methods in protein structure prediction (2020, background): “Progress in this field over the last few years has been substantial, even dramatic especially in the prediction of contact and distance maps [127,136], but the essential role of structural, evolutionary, and co-evolutionary information in this progress cannot be understated, with ab initio predictio”
Toward the solution of the protein structure prediction problem (2021, background): “However, recently the ﬁ eld has witnessed a remarkable achievement in that, for the ﬁ rst time, the gap between the TBM and FM accuracies has largely been bridged through the use of deep learning, in particular end-to-end learning, to build protein structure models (27, 28, 77, 78).”
Topology Evaluation of Models for Difficult Targets in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14) (2021, background): “4,5,11 The ranking formula also included an aggregated score (MolProbity) of prediction model quality that combined the clash, rotamer outlier, and Ramachandran favored components of experimental structure model evaluation.”