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Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction

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  • Tatjana Braun
  • Julia Koehler Leman
  • Oliver F Lange

Abstract

Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.Author Summary: Recently, a breakthrough has been achieved in modeling the atomic 3D structures of proteins from their sequence alone without requiring any experimental work on the protein itself. To achieve this goal, a database of evolutionary related sequences is analyzed to find co-evolving residues, giving insight into which residues are in close proximity to each other. These residue-residue contacts can help to drive a computer simulation with an atomic-scale physical model of the protein structure from a random starting conformation to a native-like 3D conformation. Although much effort is being put into the improvement of residue-residue contact predictions, their accuracy will always be limited. Therefore, structure prediction protocols with a high tolerance against incorrect distance restraints are needed. Here, we present a structure prediction protocol that combines evolutionary information with the iterative sampling approach of the molecular modeling suite Rosetta, called RASREC. RASREC has been shown to converge faster to near-native models and to be more robust against incorrect distance restraints than standard prediction protocols. It is therefore perfectly suited for restraints obtained from predicted residue-residue contacts with limited accuracy. We show that our protocol outperforms other currently published structure prediction methods and is able to achieve accurate structures, even if the accuracy of predicted contacts is low.

Suggested Citation

  • Tatjana Braun & Julia Koehler Leman & Oliver F Lange, 2015. "Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-20, December.
  • Handle: RePEc:plo:pcbi00:1004661
    DOI: 10.1371/journal.pcbi.1004661
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    References listed on IDEAS

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    1. Tomasz Kosciolek & David T Jones, 2014. "De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-15, March.
    2. Marcin J Skwark & Daniele Raimondi & Mirco Michel & Arne Elofsson, 2014. "Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns," PLOS Computational Biology, Public Library of Science, vol. 10(11), pages 1-14, November.
    3. Lukas Burger & Erik van Nimwegen, 2010. "Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments," PLOS Computational Biology, Public Library of Science, vol. 6(1), pages 1-18, January.
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