Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta
By Sergey Ovchinnikov, David E. Kim, R. Wang, Yuan Liu, Frank DiMaio, David Baker. 2016
108 citations recorded by the source corpus.
We describe CASP11 de novo blind structure predictions made using the Rosetta structure prediction methodology with both automatic and human assisted protocols. Model accuracy was generally improved using coevolution derived residue–residue contact information as restraints during Rosetta conformational sampling and refinement, particularly when the number of sequences in the family was more than three times the length of the protein. The highlight was the human assisted prediction of T0806, a large and topologically complex target with no homologs of known structure, which had unprecedented accuracy—<3.0 Å root‐mean‐square deviation (RMSD) from the crystal structure over 223 residues. For this target, we increased the amount of conformational sampling over our fully automated method by employing an iterative hybridization protocol. Our results clearly demonstrate, in a blind prediction scenario, that coevolution derived contacts can considerably increase the accuracy of template‐free structure modeling. Proteins 2016; 84(Suppl 1):67–75. © 2015 Wiley Periodicals, Inc.
Subfields: Materials Chemistry, Molecular Biology
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In Materials Chemistry
- Yang Zhang
- David Baker
- Mohammed Alquraishi
- Sergey Ovchinnikov
- Hahnbeom Park
- David E. Kim
- Frank DiMaio
- Robin Pearce
In Molecular Biology
- Yang Zhang
- David Baker
- Sergey Ovchinnikov
- Mohammed Alquraishi
- Robin Pearce
- Hahnbeom Park
- Jinbo Xu
- David E. Kim
Citations in the corpus
- End-to-end differentiable learning of protein structure (2018, methodology): “On all CASPs, RGNs had the best performance, even compared to servers that use coevolution data (in CASP 11 (Kryshtafovych et al., 2016; Ovchinnikov et al., 2016) and CASP 12 (Schaarschmidt et al.”
- Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “Several tools (including Rosetta) have successfully incorporated evolutionary couplings into their pipelines as distance restraints to significantly improve predictions, particularly for proteins that have proven challenging using traditional approaches (Ovchinnikov et al., 2015, 2017).”
- Transformer protein language models are unsupervised structure learners (2020, methodology): “Senior et al. (2019), use features from a Potts model fit with pseudolikelihood maximization to predict pairwise distances with a deep residual network and optimize the final structure using Rosetta.”
- Protein structure prediction using Rosetta in CASP12 (2017, methodology): “The AMBER12SB force field 5,17 is used for the simulation.”
- Protein homology model refinement by large-scale energy optimization (2018, methodology): “For CASP targets, we selected as starting models the best models submitted by any server group [analogous to the CASP refinement category (3)]; for CAMEO targets we selected the best Robetta server (19, 20) model.”
- Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12 (2017, methodology): “4 8 Although the TBM and FM methods have been primarily developed for modeling different categories of protein targets, based on templates or ab initio folding, a recent trend shows that the integration of their complementarity can be useful in improving the structure modeling accuracy for both…”