Protein 3D Structure Computed from Evolutionary Sequence Variation
Debora S. Marks1#*, Lucy J. Colwell2#, Robert Sheridan3, Thomas A. Hopf1, Andrea Pagnani4,Riccardo Zecchina4,5, Chris Sander3
1 Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America, 2 MRC Laboratory of Molecular Biology, Hills Road, Cambridge, United Kingdom, 3 Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America, 4 Human Genetics Foundation, Torino, Italy, 5 Politecnico di Torino, Torino, Italy
The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing.
In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.
We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 Å Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.
Citation: Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, et al. (2011) Protein 3D Structure Computed from Evolutionary Sequence Variation. PLoS ONE 6(12): e28766. doi:10.1371/journal.pone.0028766
Editor: Andrej Sali, University of California San Francisco, United States of America
Received: November 10, 2011; Accepted: November 14, 2011; Published: December 7, 2011
Copyright: © 2011 Marks et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: CS and RS have support from the Dana Farber Cancer Institute-Memorial Sloan-Kettering Cancer Center Physical Sciences Oncology Center (NIH U54-CA143798). LC is supported by an Engineering and Physical Sciences Research Council fellowship (EP/H028064/1). TH has support from the German National Academic Foundation. RZ has support from European Community grant 267915. No other financial support was received for the research. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
# These authors contributed equally to this work.