News from the protein mutability landscape

Abstract

Some mutations of protein residues matter more than others, and these are often conserved evolutionarily. The explosion of deep sequencing and genotyping increasingly requires the distinction between effect and neutral variants. The simplest approach predicts all mutations of conserved residues to have an effect; however, this works poorly, at best. Many computational tools that are optimized to predict the impact of point mutations provide more detail. Here, we expand the perspective from the view of single variants to the level of sketching the entire mutability landscape. This landscape is defined by the impact of substituting every residue at each position in a protein by each of the 19 non-native amino acids. We review some of the powerful conclusions about protein function, stability and their robustness to mutation that can be drawn from such an analysis. Large-scale experimental and computational mutagenesis experiments are increasingly furthering our understanding of protein function and of the genotype-phenotype associations. We also discuss how these can be used to improve predictions of protein function and pathogenicity of missense variants.

Keywords: 3D; G-protein-coupled receptor; GPCR; PDB; Protein Data Bank; SAAS; SIFT; SNAP; SNP; SNP effects; alanine scanning; complete single mutagenesis; exome-wide mutagenesis; hMC4R; human melanocortin 4 receptor; in silico mutagenesis; non-synonymous SNP; nsSNP; screening for non-acceptable polymorphisms; single nucleotide polymorphism; single-amino-acid substitution; sorting intolerant from tolerant; three-dimensional.