Protein 3D Structure Computed from Evolutionary Sequence Variation

By Debora Marks, Lucy J. Colwell, Robert P. Sheridan, Thomas A. Hopf, Andrea Pagnani, Riccardo Zecchina, Christopher Sander. 2011

1,167 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, background): “Contacts can be inferred through evolutionary correlations between residues in a sequence family (Marks et al., 2011; Anishchenko et al., 2017).”
CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction (2020, methodology): “To extract the direct correlations, a variety of direct coupling analysis (DCA) methods have been proposed using precision matrix (the inverse of covariance matrix) or Potts model [11–15].”
Generating functional protein variants with variational autoencoders (2020, background): “304 Markov random field models with pairwise couplings between residues are the basis of the most 305 successful unsupervised contact prediction methods [42, 43].”
Evolutionary context-integrated deep sequence modeling for protein engineering (2020, background): “It has been shown that the magnitudes of e ij terms can accurately predict protein contacts [10] and 3D structures [45] .”
Distance-based protein folding powered by deep learning (2018, background): “In Eqn.(1), ?̅?, ?̅? are the mean, σ1, σ2 are the variance and ρ is the correlation.”
CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations (2014, methodology): “Two classes of such methods exist, either relying on regularized inversion of the covariance matrix or on pseudo-likelihood maximization (PLM).”