Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.Author summary: Genetic diseases often occur when mutations in proteins cause gain/loss of functions. Although several methods based on conservation and protein biochemistry exist to predict genetic mutations that may impact function, many disease-associated mutations remain unexplained by these metrics. In this study, we sought a mechanistic explanation for such disease-associated mutations. In order to function, important regions of a protein must be able to exhibit collective motion. Through computer simulations, we observed that mutation of even a single amino acid position within a protein can change the protein motion. We found that disease-associated mutations tend to alter the motion of regions critical to protein function, even though these mutations occur far from these critical regions. In addition, we examined the degree to which two amino acid positions within a protein may be “coupled,” i.e., the extent to which motion in one position affects the other. We found that positions highly coupled to the active site of a protein are more likely to result in disease when mutated, thereby offering a new tool for predicting pathogenesis of new mutations by incorporating internal protein dynamics.