Kinase Inhibitors can Change Protonation or Tautomeric State upon Binding
Abstract The binding affinity of a ligand to a protein is influenced by the protonation and tautomeric states of both partners. However, this relationship remains under-investigated due to the limited availability of computational tools capable of treating charge and tautomer states together in a scalable manner to study clinically relevant systems. Here, we use multi-conformation continuum electrostatics (MCCE) to calculate the protonation and tautomer distributions of nine kinase domains bound to 18 FDA-approved inhibitors across 37 cocrystal structures, while considering their Boltzmann-ensemble. The full pipeline was repeated in triplicate, so that every reported value carries a measured run-to-run uncertainty. Our simulations show that protein net charge and proton distribution remain largely stable even upon binding charged inhibitors. Our results find that individual inhibitor charges are dynamic, frequently increasing or decreasing upon binding a specific protein target. We observe that kinase-inhibitor binding shifts the relative probabilities of low-energy states (ΔG < 2.8 kcal/mol), but does not increase the probability of higher-energy states in the bound form. By considering all low-energy charge states and tautomers, we identify when two solution-state tautomers of nearly equal population resolve upon binding into a single state, creating free energy differences of up to 3.5 kcal/mol between tautomers, and in some complexes differences beyond the resolution of the sampling. In turn, we find that minority species can become the dominant component in the bound state, emphasizing the necessity of considering ensemble-wide protonation and tautomer states to accurately predict protein–ligand binding energetics.
Authors
- Ariën S. Rustenburg (ORCID: https://orcid.org/0000-0001-7616-3701)
- Gehan Ranepura (ORCID: https://orcid.org/0000-0001-9558-1204)
- Junjun Mao (ORCID: https://orcid.org/0000-0002-3106-3018)
- Sukrit Singh (ORCID: https://orcid.org/0000-0003-1914-4955)
- Raquel López-Ríos de Castro (ORCID: https://orcid.org/0000-0003-2668-7405)
- Elizabeth Rosenzweig (ORCID: https://orcid.org/0000-0001-8464-8813)
- John D. Chodera (ORCID: https://orcid.org/0000-0003-0542-119X)
- Saqibul Islam Chowdhury
- M. R. Gunner
Institutions
- City College of New York (US)
- City University of New York (US)
- Institute of Biophysics (BG)
- Computational Physics (United States) (US)
- Institute of Molecular Biology and Biophysics (RU)
- Freie Universität Berlin (DE)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2026-10-16
- DOI
- https://doi.org/10.1021/acs.jcim.6c02792
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Damon Runyon Cancer Research Foundation
- Horizon 2020 Framework Programme
- National Cancer Institute
- National Institute of General Medical Sciences
- Division of Molecular and Cellular Biosciences