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A Gene-Specific Method for Predicting Hemophilia-Causing Point Mutations

Hamasaki-Katagiri, Nobuko, Salari, Raheleh, Wu, Andrew, Qi, Yini, Schiller, Tal, Filiberto, Amanda C., Schisterman, Enrique F., Komar, Anton A., Przytycka, Teresa M., Kimchi-Sarfaty, Chava
Journal of Molecular Biology 2013 v.425 pp. 4023-4033
amino acids, computer software, hemophilia, missense mutation, patients, point mutation, prediction, structural proteins
A fundamental goal of medical genetics is the accurate prediction of genotype–phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at