<scp>Site‐Identification</scp>by Ligand Competitive Saturation as a Paradigm of Co‐solvent<scp>MD</scp>Methods

Orr, Asuka A.; MacKerell Jr, Alexander D.

doi:10.1002/9783527840748.ch5

Computational Drug Discovery 2024

DOI: 10.1002/9783527840748.ch5

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Site‐Identificationby Ligand Competitive Saturation as a Paradigm of Co‐solventMDMethods

Asuka A. Orr,

Alexander D. MacKerell Jr

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2024

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Cited by 2 publications

References 160 publications

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Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities

Kumar,

Goel,

et al. 2024

J. Chem. Theory Comput.

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Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities

Kumar,

Goel,

et al. 2024

J. Chem. Theory Comput.

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Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

Zhao,

Yu,

MacKerell

2024

J. Phys. Chem. B

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In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand−protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D freeenergy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.

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Site‐Identificationby Ligand Competitive Saturation as a Paradigm of Co‐solventMDMethods

Cited by 2 publications

References 160 publications

Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities

Modeling Ligand Binding Site Water Networks with Site Identification by Ligand Competitive Saturation: Impact on Ligand Binding Orientations and Relative Binding Affinities

Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms

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