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A Computational Binding Affinity Estimation Protocol with Maximum Utilization of Experimental Data : A Case Study for Adenosine Receptor
preprintrevised on 22.12.2020, 06:29 and posted on 23.12.2020, 07:01 by Il Kwon Cho, Sung Hyun Moon, Kwang-Hui Cho
Estimating binding affinity between a target protein and the ligand is a crucial step in the drug discovery process. In computer aided drug design (CADD), the problem can be divided into two steps, finding the correct binding pose and estimating binding free energy. In this study, a new binding affinity estimation protocol, which uses molecular docking and binding affinity estimation with Molecular Dynamics (MD) simulation and maximizes the use of available experimental data, is suggested. Docking with a custom scoring function was used to find a better initial binding pose and Linear Interaction Energy (LIE) method with an optimized coefficient was used to estimate the binding affinity. The protocol has been validated with an external validation set and applied to five modafinil and its derivatives to set the order of binding affinity to Adenosine A2A receptors (ADORA2A, A2aR), which is a membrane protein, for a case study. This protocol could be time efficient and useful for computational drug discovery where limited experimental data is available.