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Evaluation of computational tools for thermodynamics and structural analysis of protein stability upon point mutation prediction
* , * , *
1  Federal University of Rio Grande


In Bioinformatics, review of the state of the art about computational tools, including the interpretation of generated outputs and the restrictions of each software, contributes for choosing the best application to a specific problem. This way, an important research topic is the study of the impact of mutations in the treatment of complex diseases. The mutations play a major role in the cells for presenting advantages and disadvantages by the fact that its can affect protein stability. Actually, researchers need accurate computational tools for prediction of how single point amino acid mutations affect the stability of a protein structure. For the analysis of single mutation points effects the tools generally use machine learning techniques. Recent works show significant advances in predicting stability upon point mutation. This paper presents an evaluation of computational tools for thermodynamics and structural analysis of protein stability upon point mutation prediction. We choose to evaluate for thermodynamic analysis the software CUPSAT (Cologne University Protein Stability Analysis Tool) and mCSM (mutation Cutoff Scanning Matrix), and for structural analysis the software FoldX and Modeller. It was chosen these software since, according to literature, they are commonly used in these kind of analyzes. In our proposed evaluation we verify the software outputs and verify the proximity to experimental results. As a case study we selected a set of 50 proteins extracted from: (i) MutaProt, which analyses pairs of PDB files whose members differ in one, or two, amino acids; (ii) ProTherm, database that contain experimentally determined thermodynamic parameters of protein stability. Each mutation in the datasets has attributes, as: PDB code, mutation, solvent accessibility, pH value, temperature and energy change (ddG). A stability prediction model was successfully created, and the majority of the point mutations were predicted successfully having a high correlation and low standard error.

Keywords: Point mutation, protein stability, computational tools