Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at
Threading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence‐based predicted contact or distance information is used. Contact‐assisted or distance‐assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query–template alignment. We present a new distance‐ and orientation‐based covariational threading method called DisCovER by effectively integrating information from inter‐residue distance and orientation along with the topological network neighborhood of a query–template alignment. Our method first selects a subset of templates using standard profile‐based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance‐ and orientation‐based query–template alignment using an iterative double dynamic programming framework. Multiple large‐scale benchmarking results on query proteins classified as weakly homologous from the continuous automated model evaluation experiment and from the current literature show that our method outperforms several existing state‐of‐the‐art threading approaches, and that the integration of the neighborhood effect with the inter‐residue distance and orientation information synergistically contributes to the improved performance of DisCovER. DisCovER is freely available at
- NSF-PAR ID:
- 10365098
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- Volume:
- 90
- Issue:
- 2
- ISSN:
- 0887-3585
- Page Range / eLocation ID:
- p. 579-588
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Availability and implementation http://raptorx.uchicago.edu/
Supplementary information Supplementary data are available at Bioinformatics online.
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