Topological data analysis in an approach to use topological techniques for analysis of datasets. Persistent homology is one of the main tools of topological data analysis used for reducing the dimension and complexity of the data sets, and also to distinguish topological features and delete noises. Site directed mutagenesis is widely used to understand the structure and function of biomolecules. Computational prediction of protein mutation impacts offers a fast, economical and potentially accurate alternative to laboratory mutagenesis. In this talk, topology based mutation predictor -T-MP- is introduced to dramatically reduce the geometric complexity and number of degrees of freedom of proteins, while element specific persistent homology is proposed to retain essential biological information. The present approach is found to outperform other existing methods in globular protein mutation impact predictions.

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