Improved QM/MM Linear-Interaction Energy Model for Substrate Recognition in Zinc-Containing Metalloenzymes

Williams E. Miranda; Van A. Ngo; Pedro A. Valiente; Sergei Yu Noskov

doi:10.1021/acs.jpcb.6b05628

Improved QM/MM Linear-Interaction Energy Model for Substrate Recognition in Zinc-Containing Metalloenzymes

Williams E. Miranda, Van A. Ngo, Pedro A. Valiente, Sergei Yu Noskov

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

One of the essential challenges in the description of receptor-drug interactions in the presence of various polyvalent cations (such as zinc, magnesium, or iron) is the accurate assessment of the electronic effects due to cofactor binding. The effects can range from partial electronic polarization of the proximal atoms in a receptor and bound substrate to long-range effects related to partial charge transfer and electronic delocalization effects between the cofactor and the drug. Here, we examine the role of the explicit account for electronic effects for a panel of small-molecule inhibitors binding to the zinc-aminopeptidase PfA-M1, an essential target for antimalarial drug development. Our study on PfA-M1:inhibitor interactions at the QM level reveals that the partial charge and proton transfer due to bound zinc ion are important mechanisms in the inhibitors' recognition and catalysis. The combination of classical MD simulations with a posteriori QM/MM corrections with novel DFTB parameters for the zinc cation and the linear-interaction energy (LIE) approach offers by far the most accurate estimates for the PfA-M1:inhibitor binding affinities, opening the door for future inhibitor design.

Original language	English
Pages (from-to)	7824-7835
Number of pages	12
Journal	Journal of Physical Chemistry B
Volume	120
Issue number	32
DOIs	https://doi.org/10.1021/acs.jpcb.6b05628
State	Published - Aug 18 2016
Externally published	Yes

Funding

This work was supported by grants from the Natural Sciences and Engineering Research Council (Canada) to S.Yu.N. (RGPIN-315019) and the Alberta Innovates Technical Futures Strategic Chair in (Bio)Molecular Simulations. W.E.M. was also supported by the Emerging Leaders in the Americas Program (ELAP). All of the computations were performed on the West- Grid/Compute Canada facilities, and the University of Calgary TNK cluster acquired with direct support by the Canada Foundation for Innovation. V.A.N. is supported by Alberta Innovates Health Solutions and Canadian Institute for Health Research postdoctoral fellowships.

Funders	Funder number
Canadian Institute for Health Research
University of Calgary
Natural Sciences and Engineering Research Council of Canada	RGPIN-315019
Alberta Innovates - Health Solutions
Canada Foundation for Innovation
Alberta Innovates

UN SDGs

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10.1021/acs.jpcb.6b05628

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abstract = "One of the essential challenges in the description of receptor-drug interactions in the presence of various polyvalent cations (such as zinc, magnesium, or iron) is the accurate assessment of the electronic effects due to cofactor binding. The effects can range from partial electronic polarization of the proximal atoms in a receptor and bound substrate to long-range effects related to partial charge transfer and electronic delocalization effects between the cofactor and the drug. Here, we examine the role of the explicit account for electronic effects for a panel of small-molecule inhibitors binding to the zinc-aminopeptidase PfA-M1, an essential target for antimalarial drug development. Our study on PfA-M1:inhibitor interactions at the QM level reveals that the partial charge and proton transfer due to bound zinc ion are important mechanisms in the inhibitors' recognition and catalysis. The combination of classical MD simulations with a posteriori QM/MM corrections with novel DFTB parameters for the zinc cation and the linear-interaction energy (LIE) approach offers by far the most accurate estimates for the PfA-M1:inhibitor binding affinities, opening the door for future inhibitor design.",

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Improved QM/MM Linear-Interaction Energy Model for Substrate Recognition in Zinc-Containing Metalloenzymes

Abstract

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UN SDGs

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