Coevolution-based inference of amino acid interactions underlying protein function
By Victor H Salinas, Rama Ranganathan. 2018
111 citations recorded by the source corpus.
Protein function arises from a poorly understood pattern of energetic interactions between amino acid residues. Sequence-based strategies for deducing this pattern have been proposed, but lack of benchmark data has limited experimental verification. Here, we extend deep-mutation technologies to enable measurement of many thousands of pairwise amino acid couplings in several homologs of a protein family – a deep coupling scan (DCS). The data show that cooperative interactions between residues are loaded in a sparse, evolutionarily conserved, spatially contiguous network of amino acids. The pattern of amino acid coupling is quantitatively captured in the coevolution of amino acid positions, especially as indicated by the statistical coupling analysis (SCA), providing experimental confirmation of the key tenets of this method. This work exposes the collective nature of physical constraints on protein function and clarifies its link with sequence analysis, enabling a general practical approach for understanding the structural basis for protein function.
Subfields: Genetics, Molecular Biology
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Citations in the corpus
- Direct coupling analysis of epistasis in allosteric materials (2018, unknown): “For example, considering the 199 pair of sites (1,2) and the pair (1,3), one finds |∆∆E13|/|∆∆E12| × |∆∆F12|/|∆∆F13| ≈ 0.”
- Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico (2019, methodology): “Sibe utilizes a combination of mathematical principles underlying SCA-and DCA-based methods for detecting patterns of structural contacts and functional couplings within sequence alignments to identify functional and physical interactions between amino acids [26].”