AggreRATE-Pred: a mathematical model for the prediction of change in aggregation rate upon point mutation.
作者： Puneet Rawat , R Prabakaran , Sandeep Kumar , M Michael Gromiha
DOI: 10.1093/BIOINFORMATICS/BTZ764
关键词:
摘要: MOTIVATION Protein aggregation is a major unsolved problem in biochemistry with implications for several human diseases, biotechnology and biomaterial sciences. A majority of sequence-structural properties known their mechanistic roles protein do not correlate well the kinetics. This limits practical utility predictive algorithms. RESULTS We analyzed experimental data on 183 unique single point mutations that lead to change rates 23 polypeptides proteins. Our initial mathematical model obtained correlation coefficient 0.43 between predicted rate upon mutation (P-value <0.0001). However, when dataset was classified based length conformation at sites, average almost doubled 0.82 (range: 0.74-0.87; P-value observed distinct sequence structure-based determine kinetics each class. In conclusion, are impacted by local factors global ones, such as overall three-dimensional fold, or presence aggregation-prone regions. AVAILABILITY AND IMPLEMENTATION The web server available http://www.iitm.ac.in/bioinfo/aggrerate-pred/. SUPPLEMENTARY INFORMATION Supplementary Bioinformatics online.
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