Classification of Amino Acid Substitutions in Mismatch Repair Proteins Using PON-MMR2

Abstract

Variations in mismatch repair (MMR) system genes are causative of Lynch syndrome and other cancers. Thousands of variants have been identified in MMR genes, but the clinical relevance is known for only a small proportion. Recently, the InSiGHT group classified 2,360 MMR variants into five classes. One-third of variants, majority of which is nonsynonymous variants, remain to be of uncertain clinical relevance. Computational tools can be used to prioritize variants for disease relevance investigations. Previously, we classified 248 MMR variants as likely pathogenic and likely benign using PON-MMR. We have developed a novel tool, PON-MMR2, which is trained on a larger and more reliable dataset. In performance comparison, PON-MMR2 outperforms both generic tolerance prediction methods as well as methods optimized for MMR variants. It achieves accuracy and MCC of 0.89 and 0.78, respectively, in cross-validation and 0.86 and 0.69, respectively, on an independent test dataset. We classified 354 class 3 variants in InSiGHT database as well as all possible amino acid substitutions in four MMR proteins. Likely harmful variants mainly appear in the protein core, whereas likely benign variants are on the surface. PON-MMR2 is a highly reliable tool to prioritize variants for functional analysis. It is freely available at http://structure.bmc.lu.se/PON-MMR2/.

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

“…Recently, some computational studies have been published in which all possible amino acid substitutions or those caused by single‐nucleotide changes were analyzed. These include kinase domain variants in Bruton tyrosine kinase (BTK) [Väliaho et al., ], variants in four mismatch repair (MMR) proteins [Niroula and Vihinen, ], and predicted protein solubility affecting variations in human interleukin‐1 β [Yang et al., ]. In addition, similar studies have been performed for mitochondrial tRNA molecules [Kondrashov, ; Niroula and Vihinen, ].…”

“…The BTK kinase domain has the highest published ratio of harmful variants, with 67% of the studied cases [Väliaho et al., ]. In MMR proteins, the proportions of harmful amino acid substitutions varied from 14.6% (in PMS2) to 40.4% (in MSH2) [Niroula and Vihinen, ]. There are only a few experimental studies for massive amounts of amino acid substitutions in single proteins.…”

Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding

“…Genetic variations originate from subtle differences in DNA and it is well known that humans share 99.5% of DNA code and only the rest 0.5% results in the uniqueness of individuals. However, despite of low occurrence, common genetic variations may contribute significantly to human's susceptibility to common diseases . Thus, understanding common human genetic variations and associated functional impact is a very important part of any genetic study and shows great potential for direct clinical applications …”

“…Genetic differences can be manifested at different levels as a Single Nucleotide Polymorphism (SNPs), which is a genetic change of single nucleotide or as non‐synonymous SNP (nsSNP), which results in amino acid change in the corresponding transcribed product. In this work we focus on substitutions of single amino acid in the corresponding protein and following the literature such a change is termed single amino acid variation (SAV) . The SAV can affect the corresponding protein's function and thus may be associated with human diseases .…”

PON‐P and PON‐P2 predictor performance in CAGI challenges: Lessons learned

“…Our group has a long experience and interest in investigating variants and their effects and includes protein engineering experiments to improve enzyme properties (Nera, Brockmann, Vihinen, Smith, & Mattsson, ; Rasila, Vihinen, Paulin, Haapa‐Paananen, & Savilahti, ; Vihinen et al., ; Vihinen & Mäntsälä, ; Vihinen, Helin, & Mäntsälä, ; Vihinen, Peltonen, Iitia, Suominen, & Mäntsälä, ), variant collection and distribution on locus‐specific variation databases (LSDBs) (Piirilä, Väliaho, & Vihinen, ; Väliaho, Smith, & Vihinen, ; Vihinen et al., ), interpretation of variants and their effects (Lee et al., ; Väliaho, Faisal, Ortutay, Smith, & Vihinen, ; Vihinen et al., ), and the development of recommendations and standards for variation data (Celli, Dalgleish, Vihinen, Taschner, & den Dunnen, ; Vihinen et al., ; Vihinen, den Dunnen, Dalgleish, and Cotton, ) as well as the development of various prediction tools to filter and interpret harmful variants (Ali, Olatubosun, & Vihinen, ; Niroula & Vihinen, ; Niroula & Vihinen, ; Niroula, Urolagin, & Vihinen, ; Olatubosun, Väliaho, Härkönen, Thusberg, & Vihinen, ; Yang, Niroula, Shen, & Vihinen, ). In addition, we have promoted the importance of systematic performance assessments (Khan & Vihinen, ; Thusberg et al., ), systematic measures and reporting of prediction methods (Vihinen, ; Vihinen, ), and the need for benchmark datasets (Nair & Vihinen, ; Schaafsma & Vihinen, ) and for systematics and nomenclature for describing variants (Byrne et al., ; Vihinen, ; Vihinen, ; Vihinen, ).…”