In silico analysis of missense mutations as a first step in functional studies: examples from two sphingolipidoses

Free PMC Article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6275066/Note: Where it reads BBB-BMD/6301, should be PTDC/BIM-MEC/4762/2014. Erro de edição, tendo sido solicitado correção.In order to delineate a better approach to functional studies, we have selected 23 missense mutations distributed in different domains of two lysosomal enzymes, to be studied by in silico analysis. In silico analysis of mutations relies on computational modeling to predict their effects. Various computational platforms are currently available to check the probable causality of mutations encountered in patients at the protein and at the RNA levels. In this work we used four different platforms freely available online (Protein Variation Effect Analyzer- PROVEAN, PolyPhen-2, Swiss-model Expert Protein Analysis System-ExPASy, and SNAP2) to check amino acid substitutions and their effect at the protein level. The existence of functional studies, regarding the amino acid substitutions, led to the selection of the distinct protein mutants. Functional data were used to compare the results obtained with different bioinformatics tools. With the advent of next-generation sequencing, it is not feasible to carry out functional tests in all the variants detected. In silico analysis seems to be useful for the delineation of which mutants are worth studying through functional studies. Therefore, prediction of the mutation impact at the protein level, applying computational analysis, confers the means to rapidly provide a prognosis value to genotyping results, making it potentially valuable for patient care as well as research purposes. The present work points to the need to carry out functional studies in mutations that might look neutral. Moreover, it should be noted that single nucleotide polymorphisms (SNPs), occurring in coding and non-coding regions, may lead to RNA alterations and should be systematically verified. Functional studies can gain from a preliminary multi-step approach, such as the one proposed here.Financial support was received from the Portuguese Foundation of Science and Technology (FCT), project PTDC/BIM-MEC/4762/2014 (PI-O.A.) Diogo Ribeiro and Luciana Moreira were grant recipients from FCT under the same project. Ana Duarte was a PhD student with grant a FCTSFRH/BD/118009/2016.