A Novel Computational Framework to Predict the Impact of a Point Mutation on PDZ Domain Classification

Abstract

PDZ domains represent one of the most common protein homology regions playing key roles in several diseases. Point mutations (PM) in amino acid primary sequence of PDZ domains can alter domain functions by affecting for example, downstream phosphorylation, a pivotal process in biology. Our goal in this present study was to introduce a novel approach to investigate how point mutations within the Class 1, Class 2 and Class 1-2 PDZ domains could affect the changes in binding with their partner ligands and hence affect their classification. We focused on features in PDZ domains of various species including human, rat and mouse. However, our work represents a generic computational framework that could be used to analyze PM in any given PDZ sequence. We have adopted two different approaches to investigate the impact of PM. In the first approach, we have developed a statistical model using bigram frequencies of amino acids and employed six different similarity measures to contrast the bigram frequency distribution of a wild type sequence relevant to its point mutants. In the next approach, we developed a statistical method that incorporates the impact of bigram frequency history associated with each mutational site that we call history weighted conditional change in probabilities. In this PM study, we observed that the history weighted method performs best when compared to all other methods studied in terms of picking up sites in PDZ domain where a PM could flip the class. We anticipate that this method will present a step forward towards computational techniques unveiling PDZ domain point mutants that largely affect the protein-ligand binding, specificity and affinity. We hope that this and future studies could aid therapy in which PDZ mutations have been implicated as the main disease drivers such as the Usher syndrome.