Correlating disease-related mutations to their effect on protein stability: a large-scale analysis of the human proteome

Abstract

Single residue mutations in proteins are known to affect protein stability and function. As a consequence, they can be disease associated. Available computational methods starting from protein sequence/structure can predict whether a mutated residue is or not disease associated and whether it is promoting instability of the protein-folded structure. However, the relationship among stability changes in proteins and their involvement in human diseases still needs to be fully exploited. Here, we try to rationalize in a nutshell the complexity of the question by generalizing over information already stored in public databases. For each single aminoacid polymorphysm (SAP) type, we derive the probability of being disease-related (Pd) and compute from thermodynamic data three indexes indicating the probability of decreasing (P-), increasing (P+), and perturbing the protein structure stability (Pp). Statistically validated analysis of the different P/Pd correlations indicate that Pd best correlates with Pp. Pp/Pd correlation values are as high as 0.49, and increase up to 0.67 when data variability is taken into consideration. This is indicative of a medium/good correlation among Pd and Pp and corroborates the assumption that protein stability changes can also be disease associated at the proteome level.