Abstract
The hot regions of protein interactions refer to the activity scope where hot spots are found to be buried and tightly packing with other residues. The discovery and understanding of hot region is an important way to uncover protein functional activities, such as cell metabolism and signaling pathway, immune recognition and DNA replication, protein synthesis. In this study, machine learning method is used to discover the three aspects features of hot region from sequence conservation, structure conservation and energy conservation, which create conservation scoring algorithm though multiple sequence alignment, module substitute matrix, structural similarity and molecular dynamics simulation. This study has important theoretical and practical significance on promoting hot region research, which also provides a useful way to deeply investigate the functional activities of proteins.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61502356).
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Hu, J., Lin, X., Zhang, X. (2017). Protein Hot Regions Feature Research Based on Evolutionary Conservation. In: Huang, DS., Jo, KH., Figueroa-GarcÃa, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_23
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DOI: https://doi.org/10.1007/978-3-319-63312-1_23
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