Estimating Change in Protein Thermodynamic Stability owing to Single Point Mutation

Developed for protein structures, ProTSPoM uses a combination of Random Forest Regressors (RFR) and Gradient Boosted Regressors (GBR) along with residue properties, fold level attributes, environmental compatibility, and evolutionary information to predict the change in Gibbs free energy originating out of single point missense mutations. ProTSPoM outperforms all existing state-of-the-art methods in both the Pearson correlation coefficient and root-mean-squared-error parameters for the S2648, S350, S1925 and p53 databases (even if we do not include evolutionary information).

The ProTSPoM web service use the RFR and GBR models trained on the 2298 instances from the S2648 dataset and tested on the most widely benchmarked S350 dataset to predict ΔΔG for new SPM instances.

Upload file (PDB format) (<2MB): (Sample)
Chain id:
Mutate at position:

Mutate by amino acid:

Additional features: exclude GRAVY, aliphatic index, isoelectric point

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Currently, ProTSPoM web service does not include evolutionary information. Please download ProTSPoM and run locally for all functionalities (including evolutionary information).

How to cite:
1. Anupam Banerjee, Pralay Mitra (2020) Estimating the Effect of Single Point Mutations on Protein Thermodynamic Stability and Analyzing the Mutation Landscape of the p53 Protein. Journal of Chemical Information and Modeling 60(6):3315-3323 [DOI] [PubMed].

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