Abstract
Single nucleotide polymorphisms (SNPs) are a major contributor to genetic and phenotypic variation within populations. Non-synonymous SNPs (nsSNPs) modify the sequence of proteins and can affect their folding or binding properties. Experimental analysis of all nsSNPs is currently unfeasible and therefore computational predictions of the molecular effect of nsSNPs are helpful to guide experimental investigations. While some nsSNPs can be accurately characterized, for instance if they fall into strongly conserved or well annotated regions, the molecular consequences of many others are more challenging to predict. In particular, nsSNPs affecting less structured, and often less conserved regions, are difficult to characterize. Binding sites that mediate protein-protein or other protein interactions are an important class of functional sites on proteins and can be used to help interpret nsSNPs. Binding sites targeted by the PDZ modular peptide recognition domain have recently been characterized. Here we use this data to show that it is possible to computationally identify nsSNPs in PDZ binding motifs that modify or prevent binding to the proteins containing the motifs. We confirm these predictions by experimentally validating a selected subset with ELISA. Our work also highlights the importance of better characterizing linear motifs in proteins as many of these can be affected by genetic variations.
Highlights
Genome sequencing projects have uncovered thousands of genetic variations in human populations [1]
Results non-synonymous Single Nucleotide Polymorphism (SNP) (nsSNPs) are over-represented in C-terminal sequences nsSNPs replacing one amino acid by another one can have different effects on proteins depending on which part of the amino acid sequence they affect
NsSNPs found in protein interaction interfaces will often result in weakening or disruption of the interaction
Summary
Genome sequencing projects have uncovered thousands of genetic variations in human populations [1]. It is important to understand and predict the functional and molecular consequences of nsSNPs and several computational approaches have been developed to address this issue [6,7,8,9,10,11,12,13] These algorithms typically use sequence conservation, domain annotation, structural environment, biochemical similarity between the wild type and the mutated residues, or manual annotations based on detailed biochemical studies to predict the functional impact of nsSNPs. despite the large panel of methods that have been proposed, the functional consequences of nsSNPs are still difficult to predict. Despite the large panel of methods that have been proposed, the functional consequences of nsSNPs are still difficult to predict This is especially true in regions displaying low conservation, no evident structure (e.g., disordered regions) or poor annotation, which represent a substantial portion of human protein sequences
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