Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR
Xin Chen, Kexin Wang, Jianfang Chen, Chao Wu, Jun Mao, Yuanpeng Song, Yijing Liu, Zhenhua Shao, Xuemei Pu
Abstract
Allosteric drugs offer a new avenue for modern drug design. However, the identification of cryptic allosteric sites presents a formidable challenge. Following the allostery nature of residue-driven conformation transition, we propose a state-of-the-art computational pipeline by developing a residue-intuitive hybrid machine learning (RHML) model coupled with molecular dynamics (MD) simulation, through which we can efficiently identify the allosteric site and allosteric modulator as well as reveal their regulation mechanism. For the clinical target β2-adrenoceptor (β2AR), we discover an additional allosteric site located around residues D792.50, F2826.44, N3187.45 and S3197.46 and one putative allosteric modulator ZINC5042. Using Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) and protein structure network (PSN), the allosteric potency and regulation mechanism are probed to further improve identification accuracy. Benefiting from sufficient computational evidence, the experimental assays then validate our predicted allosteric site, negative allosteric potency and regulation pathway, showcasing the effectiveness of the identification pipeline in practice. We expect that it will be applicable to other target proteins. The identification of cryptic allosteric sites presents a formidable challenge. Here, the authors develop a residue-intuitive hybrid ML model coupled with MD simulation to successfully identify a novel allosteric site of β2AR and a negative allosteric modulator.