SAAMBE-3D is a newly developed machine learning algorithm to predict the effects of single amino acid mutation on PPIs. It allows addressing two types of questions: (1) prediction of binding free energy change caused by a mutation and (b) prediction if mutation disrupts or not PPIs. We also provide downloadable stand-alone code. Both codes are very fast, providing out in a fraction of second and thus can be used for genome-scale investigations. The accuracy of the predicting binding tree energy change was tested against SKEMPI-2 database in 5 fold test, resulting in pearson correlation coefficient (PCC) of 0.8.

The accuracy of predicting disruptive/non-disruptive mutations was tested against INstruct database achieving AUC=1.0
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