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

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, t...

Full description

Saved in:
Bibliographic Details
Published in:PLoS computational biology 2015-12, Vol.11 (12), p.e1004661-e1004661
Main Authors: Braun, Tatjana, Koehler Leman, Julia, Lange, Oliver F
Format: Article
Language:English
Subjects:
Accuracy
Amino Acid Sequence
Computational Biology - methods
Computer simulation
Databases, Protein
Evolution, Molecular
Methods
Models, Molecular
Mutation
Protein Conformation
Proteins
Proteins - chemistry
Protocol
Sequence Analysis, Protein
Software
Structure
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1004661