Single Amino Acid Mutation change of Binding Energy (SAAMBE) method provides several algorithms and webservers to assess the effects of mutations on protein-protein interactions (PPIs). To address different types of investigations, here we provide several versions of SAAMBE, listed below.
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.
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SAAMBE-SEQ is a sequence-based machine learning algorithm to predict the binding energy changes upon single mutation in protein-protein complexes. Unlike other existing methods, SAAMBE-SEQ does not require a 3D complex structure as input. This method can be applied to protein complexes without known structure. The accuracy of SAAMBE-SEQ is comparable to existing structure-based methods. This method can be used to provide understanding the effect of mutation in protein complexes using sequence alone.
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Old SAAMBE method based on modified MM-PBSA protocol and optimized on old SKEMPI database.
Single Amino Acid Mutation related change of Binding Energy (SAAMBE) method addresses the demand for computational tools of predicting the effect of single amino acid substitution on the binding free energy of protein complexes. It is based on the fast (<< 1 minute) modified MM-PBSA protocol that is successfully tested and optimized for more than thousand experimental data points.