Computational approaches to predict protein functional families and functional sites |
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| Affiliation: | 1. Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia;2. Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia;3. Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan;4. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan;5. Gordon Life Science Institute, Boston, MA 02478, USA;6. Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China;7. Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah 21589, Saudi Arabia |
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| Abstract: | Understanding the mechanisms of protein function is indispensable for many biological applications, such as protein engineering and drug design. However, experimental annotations are sparse, and therefore, theoretical strategies are needed to fill the gap. Here, we present the latest developments in building functional subclassifications of protein superfamilies and using evolutionary conservation to detect functional determinants, for example, catalytic-, binding- and specificity-determining residues important for delineating the functional families. We also briefly review other features exploited for functional site detection and new machine learning strategies for combining multiple features. |
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