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An atomistic model for simulations of nilotinib and nilotinib/kinase binding

Valeyev, Najl V., Aleksandrov, Alexey (2011) An atomistic model for simulations of nilotinib and nilotinib/kinase binding. Theoretical Chemistry Accounts: Theory, Computation, and Modeling, 129 (6). pp. 747-756. ISSN 1432-881X. (doi:10.1007/s00214-011-0931-y) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:29215)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1007/s00214-011-0931-y

Abstract

Nilotinib is a novel anticancer drug, which specifically binds to the Abl kinase and blocks its signaling activity. In order to model the nilotinib/protein interactions, we have developed a molecular mechanics force field for nilotinib, consistent with the CHARMM force field for proteins and nucleic acids. Atomic charges were derived by utilizing a supermolecule ab initio approach. We considered the ab initio energies and geometries of a probe water molecule that interacts with nilotinib fragments at six different positions. We investigated both neutral and protonated states of nilotinib. The final rms deviation between the ab initio and the force field energies, averaged over both forms, was equal 0.2 kcal/mol. The model reproduces the ab initio geometry and flexibility of nilotinib. To apply the force field to nilotinib/Abl simulations, it is also necessary to determine the most likely protein and nilotinib protonation state when it binds to Abl. This task was carried out using molecular dynamics free energy simulations. The simulations indicate that nilotinib can interact with Abl in protonated and deprotonated forms, with the protonated form more favoured for the interaction. In the course of our calculations, we established that the His361, a titratable amino acid residue that mediates the interaction, prefers to be neutral. These insights and models should be of interest for drug design.

Item Type: Article
DOI/Identification number: 10.1007/s00214-011-0931-y
Uncontrolled keywords: Molecular recognition – Computer simulation – AMN107 – Drug design – CHARMM program
Subjects: Q Science
Divisions: Divisions > Division of Natural Sciences > Biosciences
Depositing User: Susan Davies
Date Deposited: 27 Mar 2012 15:35 UTC
Last Modified: 16 Nov 2021 10:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/29215 (The current URI for this page, for reference purposes)

University of Kent Author Information

Valeyev, Najl V..

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