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共价抑制的动力学建模:快速波动中间态的影响

Kinetic Modeling of Covalent Inhibition: Effects of Rapidly Fluctuating Intermediate States.

作者信息

Ghaby Kyle, Roux Benoît

机构信息

Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL 60637.

出版信息

bioRxiv. 2025 May 29:2025.05.28.656658. doi: 10.1101/2025.05.28.656658.


DOI:10.1101/2025.05.28.656658
PMID:40502015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12154943/
Abstract

There is increasing interest in the discovery of small-molecule inhibitors that form covalent bonds with their targets for therapeutic applications. Nevertheless, identifying clear rational design principles remains challenging because the action of these molecules cannot be understood as common noncovalent inhibitors. Conventional kinetic models often reduce the binding of covalent inhibitors to a two-step irreversible process, overlooking rapid complex dynamics of the associated unlinked inhibitor before the formation of the covalent bond with its target. In the present analysis, we expand the intermediate state into two conformations-reactive (E.I) and nonreactive (E..I). To illustrate the consequences of such simplification, the expanded kinetic model can be reduced to an effective two-step scheme expressed in terms of the equilibrium probability of the unlinked inhibitor to form either conformation. A mass-action-based numerical workflow is implemented to simulate time-dependent kinetics, overcoming the common limitations of empirical models. The numerical workflow helps relate microscopic states observed in molecular dynamics simulations to macroscopic observables like and the apparent rate of covalent inhibition, showing the impact of transient intermediates on dissociation rates and potency. The proposed framework refines the interpretation of dose-response data, aiding medicinal chemists in optimizing covalent inhibitors and provides a quantitative platform for relating molecular conformational distributions to empirical parameters.

摘要

人们对发现能够与其靶点形成共价键用于治疗应用的小分子抑制剂的兴趣日益浓厚。然而,确定明确的合理设计原则仍然具有挑战性,因为这些分子的作用不能像常见的非共价抑制剂那样被理解。传统的动力学模型通常将共价抑制剂的结合简化为两步不可逆过程,忽略了在与靶点形成共价键之前相关未连接抑制剂的快速复杂动力学。在本分析中,我们将中间状态扩展为两种构象——反应性(E.I)和非反应性(E..I)。为了说明这种简化的后果,扩展的动力学模型可以简化为一个有效的两步方案,该方案用未连接抑制剂形成任一构象的平衡概率来表示。实施了基于质量作用的数值工作流程来模拟时间相关的动力学,克服了经验模型的常见局限性。该数值工作流程有助于将分子动力学模拟中观察到的微观状态与诸如共价抑制的表观速率等宏观可观测量联系起来,显示了瞬态中间体对解离速率和效力的影响。所提出的框架完善了剂量反应数据的解释,有助于药物化学家优化共价抑制剂,并为将分子构象分布与经验参数联系起来提供了一个定量平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/fbbd1823fd62/nihpp-2025.05.28.656658v1-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/2bab28797c63/nihpp-2025.05.28.656658v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/5a988777c3a3/nihpp-2025.05.28.656658v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/0edcd3a61721/nihpp-2025.05.28.656658v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/d2a5a4f7b5dd/nihpp-2025.05.28.656658v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/fbbd1823fd62/nihpp-2025.05.28.656658v1-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/2bab28797c63/nihpp-2025.05.28.656658v1-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/5a988777c3a3/nihpp-2025.05.28.656658v1-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/0edcd3a61721/nihpp-2025.05.28.656658v1-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/d2a5a4f7b5dd/nihpp-2025.05.28.656658v1-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b1fc/12154943/fbbd1823fd62/nihpp-2025.05.28.656658v1-f0006.jpg

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本文引用的文献

[1]
Dynamics Playing a Key Role in the Covalent Binding of Inhibitors to Focal Adhesion Kinase.

J Chem Inf Model. 2024-8-12

[2]
Covalent Inhibitors: To Infinity and Beyond.

J Med Chem. 2024-7-11

[3]
Fitting of and Values from Endpoint Pre-incubation IC Data.

ACS Med Chem Lett. 2024-4-4

[4]
Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib.

J Chem Inf Model. 2024-4-22

[5]
Landscape of Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery.

J Phys Chem B. 2023-11-16

[6]
An update on the discovery and development of reversible covalent inhibitors.

Med Chem Res. 2023

[7]
Recent Advances in Covalent Drug Discovery.

Pharmaceuticals (Basel). 2023-4-28

[8]
Rational Design of Covalent Kinase Inhibitors by an Integrated Computational Workflow (Kin-Cov).

J Med Chem. 2023-6-8

[9]
MD-Based Assessment of Covalent Inhibitors in Noncovalent Association Complexes: Learning from Cathepsin K as a Test Case.

J Chem Inf Model. 2023-5-22

[10]
Mechanistic Modeling of Lys745 Sulfonylation in EGFR C797S Reveals Chemical Determinants for Inhibitor Activity and Discriminates Reversible from Irreversible Agents.

J Chem Inf Model. 2023-2-27

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