Use of estimated evolutionary strength at the codon level improves the prediction of disease related protein mutations in humans

Predicting the functional impact of a protein variation is one of the most challenging problems in Bioinformatics. A rapidly growing number of genome-scale studies provide large amounts of experimental data allowing the application of rigorous statistical approaches for predicting if a given single point mutation has or not an impact on human health. Up until now, existing methods have limited their source data to either protein or gene information. Novel in this work, we take advantage of both and focus on protein evolutionary information by using estimated selective pressures at the codon level. Here we introduce a new method (SeqProfCod) to predict the likeliness that a given protein variant is associated or not with a human disease. Our method relies in a Support Vector Machine classifier trained using three sources of information: protein sequence, multiple protein sequence alignments and the estimation of selective pressure at the codon level. SeqProfCod has been benchmarked with a large dataset of 8,987 single point mutations from 1,434 human proteins from SWISS-PROT. It achieves 82% overall accuracy and a correlation coefficient of 0.59 indicating that the estimation of the selective pressure helps in predicting the functional impact of single-point mutations. Moreover, this study demonstrates the synergic effect of combining two sources of information for predicting the functional effects of protein variants: protein sequence/profile-based information and the evolutionary estimation of the selective pressures at codon level. The results of large-scale application of SeqProfCod over all annotated point mutations in SWISS-PROT, which are available for download at http://bioinfo.cipf.es/sgu/services/SeqProfCod/, could be used to support clinical studies.