Yu-He Yang, Jia-Shu Wang, Shi-Shi Yuan, Meng-Lu Liu, Wei Su, Hao Lin* and Zhao-Yue Zhang* Pages 789 - 806 ( 18 )
Protein-ligand interactions are necessary for majority protein functions. Adenosine- 5’-triphosphate (ATP) is one such ligand that plays vital role as a coenzyme in providing energy for cellular activities, catalyzing biological reaction and signaling. Knowing ATP binding residues of proteins is helpful for annotation of protein function and drug design. However, due to the huge amounts of protein sequences influx into databases in the post-genome era, experimentally identifying ATP binding residues is costineffective and time-consuming. To address this problem, computational methods have been developed to predict ATP binding residues. In this review, we briefly summarized the application of machine learning methods in detecting ATP binding residues of proteins. We expect this review will be helpful for further research.
Adenosine-5’-triphosphate (ATP), binding residues, prediction, machine learning, feature extraction, proteins.
Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054