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SAMPDI-3D: Predicting protein-DNA binding free energy change upon mutations

Emil Alexov Group

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About SAMPDI-3D

SAMPDI-3D uses a gradient boosting decision tree machine learning algorithm with features as physicochemical properties, structure of mutation site and protein-DNA interactions to predict the change of binding free energy. SAMPDI-3D outperforms all existing state-of-the-art methods in both the Pearson correlation coefficient and root-mean-squared-error parameters for several independent datasets(available here). Users can also download SAMPDI-3D stand alone code (available here) and use it locally.

 
SAMPDI-3D-SCHEMA
 

 
Processing mode:
Protein's single residue mutation (single mode)

Protein-DNA complex structure


Protein mutation detail



   

Protein's multiple residue(s) mutation (batch mode)

Protein-DNA complex structure


Protein mutation list file


Upload a plain text file with one mutation per line, click here to view an example for reference


   

Processing mode:
DNA's single basepair mutation (single mode)

Protein-DNA complex structure


DNA mutation detail



   

DNA's multiple basepair(s) mutation (batch mode)

Protein-DNA complex structure


DNA mutation list file


Upload a plain text file with one mutation per line, click here to view an example for reference


   

 
Copyright © Computational Biophysics and Bioinformatics - Emil Alexov Group.