Correlated mutations and residue contacts in proteins

By Ulrike Göbel, Christopher Sander, Reinhard Schneider, Alfonso Valencia. 1994

The maintenance of protein function and structure constrains the evolution of amino acid sequences. This fact can be exploited to interpret correlated mutations observed in a sequence family as an indication of probable physical contact in three dimensions. Here we present a simple and general method to analyze correlations in mutational behavior between different positions in a multiple sequence alignment. We then use these correlations to predict contact maps for each of 11 protein families and compare the result with the contacts determined by crystallography. For the most strongly correlated residue pairs predicted to be in contact, the prediction accuracy ranges from 37 to 68% and the improvement ratio relative to a random prediction from 1.4 to 5.1. Predicted contact maps can be used as input for the calculation of protein tertiary structure, either from sequence information alone or in combination with experimental information. © 1994 John Wiley & Sons, Inc.

Subfields: 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): “Out of the possible random perturbations to a sequence, evolution is biased toward selecting those that are consistent with fitness (Göbel et al., 1994).”
Evolutionary-scale prediction of atomic level protein structure with a language model (2022, background): “[3] Ulrike GÈ obel, Chris Sander, Reinhard Schneider, and Alfonso Valencia.”
Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, methodology): “This suggests that residue correlations could provide information about amino acid residues that are close in structure [4,5,6,7,8,9,10,11].”
Learning generative models for protein fold families (2011, background): “Much of the early work focused on detecting such pairs in order to predict contacts in a protein in the absence of a solved structure [27, 28] and to perform fold recognition.”
3D structures of membrane proteins from genomic sequencing (2012, neutral): “of Membrane Proteins from Genomic Sequencing”
Sequence co-evolution gives 3D contacts and structures of protein complexes (2014, methodology): “putational approach is to use evolutionary analysis of amino acid co-variation to identify close residue contacts across protein interactions, which was first used 20 years ago (Gobel et al., 1994; Pazos and Valencia, 2001), and subsequently used also to identify protein interactions (Pazos et al.”
Protein sectors: evolutionary units of three-dimensional structure (2009, background): “Indeed, several reports have proposed approaches for the calculation of residue covariance, but often with the goal of identifying the pattern of contacts in the three dimensional structure (Gobel et al., 1994; Hamilton et al., 2004; Larson et al., 2000; Neher, 1994; Olmea and Valencia, 1997; Ortiz ”
Fast and Accurate Multivariate Gaussian Modeling of Protein Families: Predicting Residue Contacts and Protein-Interaction Partners (2014, neutral): “= (x i) i=1;:::;N and the 14 empirical covariance matrix C(X;) = C(X;) ij i;j=1;:::;N for given mean = ( i) i=1;:::;N : x i = 1 M XM m=1 xm ; (1) C ij (X;) = 1 M XM m=1 (xm i i) xm j j : (2) The empirical covariance is thus C= C(X;x). Note that the entry x i, with i= (l 1)Q+ a, measures the frac”