Structural Stability of Human Protein Tyrosine Phosphatase ρ Catalytic Domain: Effect of Point Mutations

Protein tyrosine phosphatase ρ (PTPρ) belongs to the classical receptor type IIB family of protein tyrosine phosphatase, the most frequently mutated tyrosine phosphatase in human cancer. There are evidences to suggest that PTPρ may act as a tumor suppressor gene and dysregulation of Tyr phosphorylation can be observed in diverse diseases, such as diabetes, immune deficiencies and cancer. PTPρ variants in the catalytic domain have been identified in cancer tissues. These natural variants are nonsynonymous single nucleotide polymorphisms, variations of a single nucleotide occurring in the coding region and leading to amino acid substitutions. In this study we investigated the effect of amino acid substitution on the structural stability and on the activity of the membrane-proximal catalytic domain of PTPρ. We expressed and purified as soluble recombinant proteins some of the mutants of the membrane-proximal catalytic domain of PTPρ identified in colorectal cancer and in the single nucleotide polymorphisms database. The mutants show a decreased thermal and thermodynamic stability and decreased activation energy relative to phosphatase activity, when compared to wild- type. All the variants show three-state equilibrium unfolding transitions similar to that of the wild- type, with the accumulation of a folding intermediate populated at ∼4.0 M urea

Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis. Current data-driven and bioinformatics approaches are, however, limited by the large number of new variations found in each newly sequenced genome, and often do not provide direct mechanistic insight. Here we demonstrate, for the first time, that saturation mutagenesis, biophysical modeling and co-variation analysis, performed in silico, can predict the abundance, metabolic stability, and function of proteins inside living cells. As a model system, we selected the human mismatch repair protein, MSH2, where missense variants are known to cause the hereditary cancer predisposition disease, known as Lynch syndrome. We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function, and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors. Thus, in conclusion, in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome, and perhaps other hereditary diseases