Protein stability is related to its functional activities, and effect on stability or misfolding could be one of the major disease-causing mechanisms of missense mutations. Here we developed a novel machine learning computational method PremPS, which predicts the effects of single mutations on protein stability by calculating the changes in unfolding Gibbs free energy. PremPS uses only ten evolutionary-and structure-based features and is parameterized on five thousand mutations.Our approach outperforms previous methods and shows a considerable improvement in estimating the effects of mutations increasing protein stability. In addition, PremPS presents an outstanding performance in predicting the pathogenicity of missense mutations using an experimental dataset composed of two thousand non-neutral and neutral mutations. PremPS can be applied to many tasks, including finding functionally important variants, revealing the molecular mechanisms of functional influences and protein design. It is freely available at https://lilab.jysw.suda.edu.cn/research/PremPS/. Key Points:• Considerable improvement in estimating the effects of mutations increasing protein stability;• Comprehensive comparison with other 25 computational methods on different test sets;• An outstanding performance in predicting the pathogenicity of missense mutations;• PremPS employs only ten distinct features belonging to six categories, and the most important feature describes evolutionary conservation of the site;• The webserver allows to do large-scale mutational scanning and takes about ten minutes to perform calculations for one thousand mutations from a normal size protein.