A gene-specific method for predicting hemophilia-causing point mutations

Nobuko Hamasaki-Katagiri, Raheleh Salari, Andrew Wu, Yini Qi, Tal Schiller, Amanda C. Filiberto, Enrique F. Schisterman, Anton A. Komar, Teresa M. Przytycka, Chava Kimchi-Sarfaty

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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 http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA-Predict/index.htm.

Original languageEnglish (US)
Pages (from-to)4023-4033
Number of pages11
JournalJournal of molecular biology
Issue number21
StatePublished - Nov 1 2013
Externally publishedYes


  • coagulation factor IX
  • coagulation factor VIII
  • gene/disease-specific prediction tool
  • hemophilia A/B
  • synonymous mutation

ASJC Scopus subject areas

  • Molecular Biology
  • Biophysics
  • Structural Biology


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