Revealing evolutionary constraints on proteins through sequence analysis

By Shou-Wen Wang, Anne-Florence Bitbol, Ned S. Wingreen. 2018

29 citations recorded by the source corpus.

Alignments of large numbers of protein sequences have revealed “sectors” of collectively coevolving amino acids in several protein families. Here, we show that selection acting on any relevant physical property of a protein, e.g. the elastic energy of an important conformational change, can give rise to such a sector. We demonstrate that the main signature of these physical sectors lies in the smalleigenvalue modes of the covariance matrix of the selected sequences. This simple, generic model leads us to propose a principled method to identify sectors, along with the magnitudes of mutational effects, from sequence data.

Subfields: Genetics, 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): “, 2017) as well as by contributions from phylogenetic bias (Gabaldon, 2007), experimental and selection biases (Wang et al., 2019; Overbaugh & Bangham, 2001), and sources of noise such as random genetic drift (Kondrashov et al.”
Inferring interaction partners from protein sequences using mutual information (2018, background): “t are in direct contact in the three-dimensional protein structure. However, correlations in protein sequences also have important collective modes [23, 24], which can arise from functional selection [25, 26], and additional correlations are due to phylogeny [23, 27, 28]. These contributions are del”
Direct coupling analysis of epistasis in allosteric materials (2018, unknown): “Consistently with this observation, statistical approaches based on principal components of the MSA covariance such as Sectors [44, 45] or Inverse Covariance Off-Diagonal (ICOD) [46] do not lead overall to better predictions of epistasis in our context, as we show in S1 Text, Sec.”