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      Taming multiple binding poses in alchemical binding free energy prediction: the β-cyclodextrin host–guest SAMPL9 blinded challenge

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          Abstract

          The binding free energies of the multiple binding poses of the βCD/phenothiazine host–guest complexes are integrated to form SAMPL9 predictions.

          Abstract

          We apply the Alchemical Transfer Method (ATM) and a bespoke fixed partial charge force field to the SAMPL9 bCD host–guest binding free energy prediction challenge that comprises a combination of complexes formed between five phenothiazine guests and two cyclodextrin hosts. Multiple chemical forms, competing binding poses, and computational modeling challenges pose significant obstacles to obtaining reliable computational predictions for these systems. The phenothiazine guests exist in solution as racemic mixtures of enantiomers related by nitrogen inversions that bind the hosts in various binding poses, each requiring an individual free energy analysis. Due to the large size of the guests and the conformational reorganization of the hosts, which prevent a direct absolute binding free energy route, binding free energies are obtained by a series of absolute and relative binding alchemical steps for each chemical species in each binding pose. Metadynamics-accelerated conformational sampling was found to be necessary to address the poor convergence of some numerical estimates affected by conformational trapping. Despite these challenges, our blinded predictions quantitatively reproduced the experimental affinities for the β-cyclodextrin host and, to a lesser extent, those with a methylated derivative. The work illustrates the challenges of obtaining reliable free energy data in in silico drug design for even seemingly simple systems and introduces some of the technologies available to tackle them.

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          Automatic atom type and bond type perception in molecular mechanical calculations.

          In molecular mechanics (MM) studies, atom types and/or bond types of molecules are needed to determine prior to energy calculations. We present here an automatic algorithm of perceiving atom types that are defined in a description table, and an automatic algorithm of assigning bond types just based on atomic connectivity. The algorithms have been implemented in a new module of the AMBER packages. This auxiliary module, antechamber (roughly meaning "before AMBER"), can be applied to generate necessary inputs of leap-the AMBER program to generate topologies for minimization, molecular dynamics, etc., for most organic molecules. The algorithms behind the manipulations may be useful for other molecular mechanical packages as well as applications that need to designate atom types and bond types.
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            OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

            OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.
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              GFN2-xTB—An Accurate and Broadly Parametrized Self-Consistent Tight-Binding Quantum Chemical Method with Multipole Electrostatics and Density-Dependent Dispersion Contributions

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                Author and article information

                Contributors
                Journal
                PPCPFQ
                Physical Chemistry Chemical Physics
                Phys. Chem. Chem. Phys.
                Royal Society of Chemistry (RSC)
                1463-9076
                1463-9084
                September 20 2023
                2023
                : 25
                : 36
                : 24364-24376
                Affiliations
                [1 ]Department of Chemistry, Brooklyn College of the City University of New York, New York, USA
                [2 ]PhD Program in Biochemistry, Graduate Center of the City University of New York, USA
                [3 ]PhD Program in Chemistry, Graduate Center of the City University of New York, USA
                [4 ]Roivant Sciences, New York, NY, USA
                Article
                10.1039/D3CP02125D
                852b1bde-1526-400f-9a32-00c665270a2b
                © 2023

                http://creativecommons.org/licenses/by/3.0/

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