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 () and Xuemei Pu ()
Additional contact information
Xin Chen: Sichuan University
Kexin Wang: Sichuan University
Jianfang Chen: Sichuan University
Chao Wu: Sichuan University
Jun Mao: Sichuan University
Yuanpeng Song: Sichuan University
Yijing Liu: Sichuan University
Zhenhua Shao: Sichuan University
Xuemei Pu: Sichuan University

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-52399-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52399-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-52399-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().