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In Journal of chemical information and modeling

Nonsynonymous Single Nucleotide Polymorphisms often result in altered protein stability while playing crucial roles both in the evolution process and in the development of human diseases. Prediction of change in thermodynamic stability due to such missense mutations will help in protein engineering endeavors and will contribute to a better understanding of different disease conditions. Here, we develop a machine learning-based framework viz., ProTSPoM to estimate the change in protein thermodynamic stability arising out of single point mutations (SPMs). ProTSPoM outperforms existing methods on the S2698 and S1925 databases and reports a Pearson correlation coefficient of 0.82 (0.88) and a root-mean-squared-error of 0.92 (1.06) kcal/mol between the predicted and experimental ∆∆G values for the long-established S350 (tumor suppressor p53 protein) dataset. Further, we estimate the change in thermodynamic stability for all possible SPMs in the DNA binding domain of the p53 protein. We identify SNPs in p53 which are plausibly detrimental to its structural integrity and interaction affinity with the DNA molecule. ProTSPoM with its reliable estimates and time-efficient prediction is well suited to be integrated with existing protein engineering techniques. The ProTSPoM web server is accessible at

Banerjee Anupam, Mitra Pralay