Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms
(nsSNPs) in the human and other genomes. In order to understand their impacts on the structure and function of the proteome,
as well as to guide protein engineering, robust in silico methodologies are required to study and predict the effects
of nsSNPs on protein stability. Despite the diversity of available computational methods in the literature, none has proven
robust and dependable on its own under all scenarios where mutation analysis is required.
Here we present DUET, a web server for an integrated computational approach for studying missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimised predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods.