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Computational Biophysics & Bioinformatics - Dr. Emil Alexov Group
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SAAMBE Web Server

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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Mutation Image
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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Mutation Image

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