Artificial intelligence based methods for hot spot prediction |
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| Institution: | 1. Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey;2. KUIS AI Center, Koc University, Istanbul, 34450, Turkey;3. Graduate School of Science and Engineering, Koc University, Istanbul, 34450, Turkey;4. Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey;5. School of Medicine, Koc University, Istanbul, 34450, Turkey;1. Departments of Molecular Physiology and Biomedical Engineering, University of Virginia, Box 800886, Charlottesville, VA, 22908, USA;2. Department of Cell and Molecular Biology, Uppsala University, Box 256, Uppsala 75105, Sweden;1. Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA;2. Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey;3. Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey;4. Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel;1. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, 94305, USA;2. Stanford ChEM-H, Stanford University, Stanford, CA, 94305, USA;3. Microsoft Research New England, Cambridge, MA, 02142, USA |
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| Abstract: | Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved in. Any abnormal interactions may cause diseases. Therefore, the identification of small molecules which modulate protein interactions through their interfaces has high therapeutic potential. However, discovering such molecules is challenging. Most protein–protein binding affinity is attributed to a small set of amino acids found in protein interfaces known as hot spots. Recent studies demonstrate that drug-like small molecules specifically may bind to hot spots. Therefore, hot spot prediction is crucial. As experimental data accumulates, artificial intelligence begins to be used for computational hot spot prediction. First, we review machine learning and deep learning for computational hot spot prediction and then explain the significance of hot spots toward drug design. |
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