High-throughput and intelligent design of potential GRK2 inhibitor candidates using deep learning and mathematical programming methods

doi:10.1016/j.cjche.2025.02.024

中国化学工程学报 ›› 2025, Vol. 84 ›› Issue (8): 11-22.DOI: 10.1016/j.cjche.2025.02.024

• Full Length Article • 上一篇下一篇

High-throughput and intelligent design of potential GRK2 inhibitor candidates using deep learning and mathematical programming methods

Yujing Zhao^1,2, Qilei Liu^2,3, Jian Du², Qingwei Meng^2,3, Liang Sun^4,5, Lei Zhang²

1. MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, China;
2. State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China;
3. Ningbo Institute of Dalian University of Technology, Ningbo 315016, China;
4. Shenzhen Shuli Tech Co., Ltd, Shenzhen 518126, China;
5. Department of Physics, City University of Hong Kong, Hong Kong, China

收稿日期:2024-10-24 修回日期:2025-02-13 接受日期:2025-02-19 出版日期:2025-08-28 发布日期:2025-03-26
通讯作者: Lei Zhang,E-mail:keleiz@dlut.edu.cn
基金资助:
This work is supported by the National Natural Science Foundation of China Excellent Young Scientist Fund (22422801), the National Natural Science Foundation of China General Project (22278053), the National Natural Science Foundation of China General Project (22078041), Dalian High-level Talents Innovation Support Program (2023RQ059).

High-throughput and intelligent design of potential GRK2 inhibitor candidates using deep learning and mathematical programming methods

Yujing Zhao^1,2, Qilei Liu^2,3, Jian Du², Qingwei Meng^2,3, Liang Sun^4,5, Lei Zhang²

1. MOE Key Laboratory of Bio-Intelligent Manufacturing, School of Bioengineering, Dalian University of Technology, Dalian 116024, China;
2. State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China;
3. Ningbo Institute of Dalian University of Technology, Ningbo 315016, China;
4. Shenzhen Shuli Tech Co., Ltd, Shenzhen 518126, China;
5. Department of Physics, City University of Hong Kong, Hong Kong, China

Received:2024-10-24 Revised:2025-02-13 Accepted:2025-02-19 Online:2025-08-28 Published:2025-03-26
Contact: Lei Zhang,E-mail:keleiz@dlut.edu.cn
Supported by:
This work is supported by the National Natural Science Foundation of China Excellent Young Scientist Fund (22422801), the National Natural Science Foundation of China General Project (22278053), the National Natural Science Foundation of China General Project (22078041), Dalian High-level Talents Innovation Support Program (2023RQ059).

摘要/Abstract

摘要： G protein coupled receptor kinase 2 (GRK2) is a kinase that regulates cardiac signaling activity. Inhibiting GRK2 is a promising mechanism for the treatment of heart failure (HF). Further development and optimization of inhibitors targeting GRK2 are highly meaningful. Therefore, in order to design GRK2 inhibitors with better performance, the most active molecule was selected as a reference compound from a data set containing 4-pyridylhydrazone derivatives and triazole derivatives, and its scaffold was extracted as the initial scaffold. Then, a powerful optimization-based framework for de novo drug design, guided by binding affinity, was used to generate a virtual molecular library targeting GRK2. The binding affinity of each virtual compound in this dataset was predicted by our developed deep learning model, and the designed potential compound with high binding affinity was selected for molecular docking and molecular dynamics simulation. It was found that the designed potential molecule binds to the ATP site of GRK2, which consists of key amino acids including Arg199, Gly200, Phe202, Val205, Lys220, Met274 and Asp335. The scaffold of the molecule is stabilized mainly by H-bonding and hydrophobic contacts. Concurrently, the reference compound in the dataset was also simulated by docking. It was found that this molecule also binds to the ATP site of GRK2. In addition, its scaffold is stabilized mainly by H-bonding and π-cation stacking interactions with Lys220, as well as hydrophobic contacts. The above results show that the designed potential molecule has similar binding modes to the reference compound, supporting the effectiveness of our framework for activity-focused molecular design. Finally, we summarized the interaction characteristics of general GRK2 inhibitors and gained insight into their molecule-target binding mechanisms, thereby facilitating the expansion of lead to hit compound.

关键词: Mathematical modeling, Optimal design, Product design, GRK2, Mathematical programming method, Binding affinity

Abstract: G protein coupled receptor kinase 2 (GRK2) is a kinase that regulates cardiac signaling activity. Inhibiting GRK2 is a promising mechanism for the treatment of heart failure (HF). Further development and optimization of inhibitors targeting GRK2 are highly meaningful. Therefore, in order to design GRK2 inhibitors with better performance, the most active molecule was selected as a reference compound from a data set containing 4-pyridylhydrazone derivatives and triazole derivatives, and its scaffold was extracted as the initial scaffold. Then, a powerful optimization-based framework for de novo drug design, guided by binding affinity, was used to generate a virtual molecular library targeting GRK2. The binding affinity of each virtual compound in this dataset was predicted by our developed deep learning model, and the designed potential compound with high binding affinity was selected for molecular docking and molecular dynamics simulation. It was found that the designed potential molecule binds to the ATP site of GRK2, which consists of key amino acids including Arg199, Gly200, Phe202, Val205, Lys220, Met274 and Asp335. The scaffold of the molecule is stabilized mainly by H-bonding and hydrophobic contacts. Concurrently, the reference compound in the dataset was also simulated by docking. It was found that this molecule also binds to the ATP site of GRK2. In addition, its scaffold is stabilized mainly by H-bonding and π-cation stacking interactions with Lys220, as well as hydrophobic contacts. The above results show that the designed potential molecule has similar binding modes to the reference compound, supporting the effectiveness of our framework for activity-focused molecular design. Finally, we summarized the interaction characteristics of general GRK2 inhibitors and gained insight into their molecule-target binding mechanisms, thereby facilitating the expansion of lead to hit compound.

Key words: Mathematical modeling, Optimal design, Product design, GRK2, Mathematical programming method, Binding affinity

Yujing Zhao, Qilei Liu, Jian Du, Qingwei Meng, Liang Sun, Lei Zhang. High-throughput and intelligent design of potential GRK2 inhibitor candidates using deep learning and mathematical programming methods[J]. 中国化学工程学报, 2025, 84(8): 11-22.

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High-throughput and intelligent design of potential GRK2 inhibitor candidates using deep learning and mathematical programming methods

High-throughput and intelligent design of potential GRK2 inhibitor candidates using deep learning and mathematical programming methods

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