Straightforward incorporation of multiple ligand types into molecular dynamics simulations for efficient binding site detection and characterization

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Straightforward incorporation of multiple ligand types into molecular dynamics simulations for efficient binding site detection and characterization
Title:
Straightforward incorporation of multiple ligand types into molecular dynamics simulations for efficient binding site detection and characterization
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Journal of Chemical Theory and Computation
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Publication Date:
18 August 2020
Citation:
Tan YS, Verma CS. Straightforward Incorporation of Multiple Ligand Types into Molecular Dynamics Simulations for Efficient Binding Site Detection and Characterization. J Chem Theory Comput. 2020 Sep 10. doi: 10.1021/acs.jctc.0c00405. Epub ahead of print. PMID: 32810406.
Abstract:
Binding site identification and characterization is an important initial step in structure-based drug design. To account for the effects of protein flexibility and solvation, several cosolvent molecular dynamics (MD) simulation methods that incorporate small organic molecules into the protein’s solvent box to probe for binding sites have been developed. However, most of these methods are highly inefficient, as they allow for the use of only one probe type at a time, which means that multiple sets of simulations have to be performed to map different types of binding sites. The high probe concentrations used in some of these methods also necessitate the use of artificial repulsive forces to prevent the probes from aggregating. Here, we present multiple-ligand-mapping MD (mLMMD), a method that incorporates multiple types of probes for simultaneous and efficient mapping of different types of binding sites without the need for introduction of artificial forces that may cause unintended mapping artifacts. We validate the method on a diverse set of 10 proteins and show that the mLMMD probes are able to reliably identify hydrophobic, hydrogen-bonding, charged, and cryptic binding sites in all of the test cases. Our results also highlight the potential utility of mLMMD for virtual screening and rational drug design.
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Funding Info:
This work was supported by A*STAR's Career Development Award (Y.S.T.; 202D800022) and Industry Alignment Fund (C.S.V.; H17/01/a0/010 and H18/01/a0/015).
Description:
This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jctc.0c00405
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