Physicochemical constraint violation by missense substitutions mediates impairment of protein function and disease severity

Abstract

We find that the degree of impairment of protein function by missense variants is predictable by comparative sequence analysis alone. The applicable range of impairment is not confined to binary predictions that distinguish normal from deleterious variants, but extends continuously from mild to severe effects. The accuracy of predictions is strongly dependent on sequence variation and is highest when diverse orthologs are available. High predictive accuracy is achieved by quantification of the physicochemical characteristics in each position of the protein, based on observed evolutionary variation. The strong relationship between physicochemical characteristics of a missense variant and impairment of protein function extends to human disease. By using four diverse proteins for which sufficient comparative sequence data are available, we show that grades of disease, or likelihood of developing cancer, correlate strongly with physicochemical constraint violation by causative amino acid variants.[Supplemental material is available online at www.genome.org. A Java executable of MAPP and documentation are freely available for download at http://mendel.stanford.edu/supplementarydata/stone_MAPP_2005.]Missense mutations that impair protein function may result in disease. For diseases caused by such deleterious mutations, a simple but plausible model presents itself: The type of disease is dependent on when and where the protein's function is required in the organism. Given the type of disease, its severity is likely determined by at least three parameters: (1) the degree to which the function of the protein is impaired by the missense mutation; (2) variants of other genes that modulate the effect of the major locus, also referred to as genetic background; and (3) the environment. We present here a predictive statistical framework for the first of these parameters, impairment of protein function by missense mutations.Our study was motivated by several observations relevant to protein structure, function, and evolution, and by previous studies that addressed the relationship between the nature of missense variants, impairment of protein function, and resulting disease ( In aggregate, these studies suggest that mutations in evolutionarily conserved sites tend to impair protein function and lead to disease. There is also weak evidence that protein impairment and disease severity are somewhat correlated with the physicochemical difference between the original amino acid and the missense variant. However, no transparent, quantitative relationship between evolutionary constraint, functional impairment, and disease severity has been described. We believe that there has been a methodological barrier to uncovering such a relationship.Our analysis rests on two complementary ideas: (1) that differences in standard physicochemical properties between the "wild-type" amino acid and the missense variant are the root cause of functional impairment; and (2) that evolutionary variation among orthologs in the affected position is a sample ...

A formal perturbation equation between genotype and phenotype determines the Evolutionary Action of protein-coding variations on fitness

“…In TP53 there is also a lag, but it is small and may reflect the experimental error of averaging small differences in transactivation. As a reference, the sensitivity and specificity of common alternative measures of mutational impact (Ng and Henikoff 2001;Stone and Sidow 2005;Adzhubei et al 2010) are lower on the same data sets (Fig. 3A).…”

“…Nevertheless, association studies explain only a fraction of disease susceptibility (McCarthy and Hirschhorn 2008), and the role of both private and common mutations remains unclear (Ng et al 2008). Computational approaches therefore aim to identify which coding variations cause disease (Ng and Henikoff 2001;Stone and Sidow 2005;Adzhubei et al 2010) within the limitations of biophysical, statistical, and machine-learning models of protein function (Chun and Fay 2009;Hicks et al 2011). In parallel, a large body of theory models the spread and fixation of mutations (Orr 2005), their distribution for various population sizes and fitness effects (Eyre-Walker and Keightley 2007), and whether selection or drift dominates their fate (Nei 2007).…”

Molecular evolution of the hyaluronan synthase 2 gene in mammals: implications for adaptations to the subterranean niche and cancer resistance

“…To test for signatures of selection acting along 519 codons of HAS2 (exons 2 and 4) across all 70 mammal species included in our study, we implemented site, branch-site and clade models with the codeml package in PAML v. 4.4 [11], using a mammal species tree topology based on published studies [12][13][14]. Amino acid polymorphisms were analysed using MAPP [15], which implements a predictive statistical framework to score the physico-chemical impact of substitutions in multiple alignments of orthologues. Novel sequences have been deposited in GenBank.…”

Inferring causality and functional significance of human coding DNA variants

“…For example, SIFT (62) and PolyPhen-2 (67) estimate the probability that the mutant amino acid would fit the amino acid position given the observed substitution pattern. MAPP (69) analyzes the conservation of physico-chemical properties of amino acids, LRT (70) and GERP (71) estimate selective constraint. The methods based on multiple features also differ in machine learning algorithms they employ.…”