Literature DB >> 22210962

Binding free energy calculation with QM/MM hybrid methods for Abl-Kinase inhibitor.

Kshatresh Dutta Dubey1, Rajendra Prasad Ojha.   

Abstract

We report a Quantum mechanics/Molecular Mechanics-Poisson-Boltzmann/ Surface Area (QM/MM-PB/SA) method to calculate the binding free energy of c-Abl human tyrosine kinase by combining the QM and MM principles where the ligand is treated quantum mechanically and the rest of the receptor by classical molecular mechanics. To study the role of entropy and the flexibility of the protein ligand complex in a solvated environment, molecular dynamics calculations are performed using a hybrid QM/MM approach. This work shows that the results of the QM/MM approach are strongly correlated with the binding affinity. The QM/MM interaction energy in our reported study confirms the importance of electronic and polarization contributions, which are often neglected in classical MM-PB/SA calculations. Moreover, a comparison of semi-empirical methods like DFTB-SCC, PM3, MNDO, MNDO-PDDG, and PDDG-PM3 is also performed. The results of the study show that the implementation of a DFTB-SCC semi-empirical Hamiltonian that is derived from DFT gives better results than other methods. We have performed such studies using the AMBER molecular dynamic package for the first time. The calculated binding free energy is also in agreement with the experimentally determined binding affinity for c-Abl tyrosine kinase complex with Imatinib.Electronic supplementary material The online version of this article (doi:10.1007/s10867-010-9199-z) contains supplementary material, which is available to authorized users.

Entities:  

Keywords:  Binding free energy; Molecular dynamics; QM/MM-PB/SA; Semi-empirical methods; Tyrosine kinase

Year:  2010        PMID: 22210962      PMCID: PMC3006460          DOI: 10.1007/s10867-010-9199-z

Source DB:  PubMed          Journal:  J Biol Phys        ISSN: 0092-0606            Impact factor:   1.365


  33 in total

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9.  Simulating protein-ligand binding with neural network potentials.

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