Literature DB >> 31425654

Toward Atomistic Modeling of Irreversible Covalent Inhibitor Binding Kinetics.

Haoyu S Yu1, Cen Gao2, Dmitry Lupyan1, Yujie Wu1, Takayuki Kimura3, Chuanjie Wu1, Leif Jacobson1, Edward Harder1, Robert Abel1, Lingle Wang1.   

Abstract

Covalent inhibitors have emerged as an important drug class in recent years, largely due to their many unique advantages as compared to noncovalent inhibitors, including longer duration of action, lower prolonged systemic exposure, higher potency, and selectivity. However, the potential off-target toxicity of covalent inhibitors, particularly of irreversible covalent inhibitors, represents a great challenge in covalent drug development. Therefore, accurate calculation of protein covalent inhibitor reaction kinetics to guide the design of selective inhibitors would greatly benefit covalent drug discovery efforts. In the present paper, we present a computational method to calculate the relative reaction kinetics between congeneric irreversible covalent inhibitors and their protein receptors. The method combines density functional theory calculations of the transition state barrier height of the rate-limiting step for reaction between the warhead of the inhibitor and a single protein residue, and molecular-mechanics-based free energy calculations to account for the interactions between the ligand in the transition state and the protein environment. The method was tested on four pharmaceutically interesting irreversible covalent binding systems involving 28 ligands; the mean unsigned error (MUE) of the relative reaction rate for all pairs of ligands between the predictions and experimental results for these tested systems is 0.79 log unit. This is to our knowledge the first time where the reaction kinetics of protein irreversible covalent inhibition have been directly calculated with physics-based free energy calculation methods and transition state theory. We anticipate the outstanding accuracy demonstrated here across a broad range of target classes will have a strong impact on the design of selective covalent inhibitors.

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Year:  2019        PMID: 31425654     DOI: 10.1021/acs.jcim.9b00268

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  4 in total

Review 1.  Selective and Effective: Current Progress in Computational Structure-Based Drug Discovery of Targeted Covalent Inhibitors.

Authors:  Giulia Bianco; David S Goodsell; Stefano Forli
Journal:  Trends Pharmacol Sci       Date:  2020-11-02       Impact factor: 14.819

2.  Δ-Quantum machine-learning for medicinal chemistry.

Authors:  Kenneth Atz; Clemens Isert; Markus N A Böcker; José Jiménez-Luna; Gisbert Schneider
Journal:  Phys Chem Chem Phys       Date:  2022-05-11       Impact factor: 3.945

3.  Fast and Effective Prediction of the Absolute Binding Free Energies of Covalent Inhibitors of SARS-CoV-2 Main Protease and 20S Proteasome.

Authors:  Jiao Zhou; Arjun Saha; Ziwei Huang; Arieh Warshel
Journal:  J Am Chem Soc       Date:  2022-04-18       Impact factor: 16.383

Review 4.  The design and development of covalent protein-protein interaction inhibitors for cancer treatment.

Authors:  Sha-Sha Cheng; Guan-Jun Yang; Wanhe Wang; Chung-Hang Leung; Dik-Lung Ma
Journal:  J Hematol Oncol       Date:  2020-03-30       Impact factor: 17.388

  4 in total

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