MUpro is a set of machine learning programs to predict how single-site amino acid mutation affects protein stability. We developed two machine learning methods: Support Vector Machines and Neural Networks. Both of them were trained on a large mutation dataset and show accuracy above 84% via 20 fold cross validation, which is better than other methods in the literature. One advantage of our methods is that they do not require tertiary structures to predict protein stability changes. Our experimental results show that the prediction accuracy using sequence information alone is comparable to that of using tertiary structures. So even you do not have protein tertiary structures available, you still can use this server to get rather accurate prediction. Of course, if you provide tertiary structures, our methods will take advantage of them and you might get slightly better predictions.
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J. Cheng, A. Randall, and P. Baldi.
Prediction of Protein Stability Changes for Single Site Mutations Using Support Vector Machines.
Proteins. 2006 Mar 1;62(4):1125-32.