Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design

Cole, D. J. A.; Tirado‐Rives, Julian; Jorgensen, William L.

doi:10.1016/j.bbagen.2014.08.018

“…For signal transduction, the importance of such conformational changes has long been recognized (8), and has been emphasized by recent experimental (9) and computational studies (10). Accordingly, molecular dynamics simulations of protein-ligand complexes are now widely considered for ligand design (11)(12)(13)(14)(15)(16).…”

mentioning

confidence: 99%

Homologous ligands accommodated by discrete conformations of a buried cavity

Merski

¹

,

Fischer

²

,

Balius

³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Conformational change in protein–ligand complexes is widely modeled, but the protein accommodation expected on binding a congeneric series of ligands has received less attention. Given their use in medicinal chemistry, there are surprisingly few substantial series of congeneric ligand complexes in the Protein Data Bank (PDB). Here we determine the structures of eight alkyl benzenes, in single-methylene increases from benzene to n-hexylbenzene, bound to an enclosed cavity in T4 lysozyme. The volume of the apo cavity suffices to accommodate benzene but, even with toluene, larger cavity conformations become observable in the electron density, and over the series two other major conformations are observed. These involve discrete changes in main-chain conformation, expanding the site; few continuous changes in the site are observed. In most structures, two discrete protein conformations are observed simultaneously, and energetic considerations suggest that these conformations are low in energy relative to the ground state. An analysis of 121 lysozyme cavity structures in the PDB finds that these three conformations dominate the previously determined structures, largely modeled in a single conformation. An investigation of the few congeneric series in the PDB suggests that discrete changes are common adaptations to a series of growing ligands. The discrete, but relatively few, conformational states observed here, and their energetic accessibility, may have implications for anticipating protein conformational change in ligand design.

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“…The present review focuses on a class of methods in which free energy differences are computed with simulations that sample Boltzmann distributions of molecular configurations. These samples are usually generated by molecular dynamics (MD) simulations [88], with the system effectively coupled to a heat bath at constant temperature, but Monte Carlo methods may also be used [29,113,114]. In either case, the energy of a given configuration is provided by a potential function, or force field, which estimates the potential energy of a system of solute and solvent molecules as a function of the coordinates of all of its atoms.…”

Section: Overview Of Free Energy Calculationsmentioning

confidence: 99%

Predicting binding free energies: Frontiers and benchmarks

Mobley

¹

,

Gilson

²

2016

Preprint

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Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions between its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early stage drug discovery. However, many challenges remain to make them a robust and reliable tool. Here, we highlight key challenges facing these calculations, describe known examples of these challenges, and call for the designation of standard community benchmark test systems that will help the research community generate and evaluate progress. In our view, progress will require careful assessment and evaluation of new methods, force fields, and modeling innovations on well-characterized benchmark systems, and we lay out our vision for how this can be achieved.

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“…These samples are usually generated by molecular dynamics (MD) simulations (59), with the system effectively coupled to a heat bath at constant temperature, but Monte Carlo methods may also be used (75, 76, 21). In either case, the energy of a given configuration is provided by a potential function, or force field, which estimates the potential energy of a system of solute and solvent molecules as a function of the coordinates of all of its atoms.…”

Section: Introductionmentioning

confidence: 99%

Predicting Binding Free Energies: Frontiers and Benchmarks

Mobley

¹

,

Gilson

²

2017

Annu. Rev. Biophys.

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Binding free energy calculations based on molecular simulations provide predicted affinities for biomolecular complexes. These calculations begin with a detailed description of a system, including its chemical composition and the interactions between its components. Simulations of the system are then used to compute thermodynamic information, such as binding affinities. Because of their promise for guiding molecular design, these calculations have recently begun to see widespread applications in early stage drug discovery. However, many challenges remain to make them a robust and reliable tool. Here, we highlight key challenges facing these calculations, describe known examples of these challenges, and call for the designation of standard community benchmark test systems that will help the research community generate and evaluate progress. In our view, progress will require careful assessment and evaluation of new methods, force fields, and modeling innovations on well-characterized benchmark systems, and we lay out our vision for how this can be achieved.

show abstract

Molecular dynamics and Monte Carlo simulations for protein–ligand binding and inhibitor design

Cited by 53 publications

References 37 publications

Homologous ligands accommodated by discrete conformations of a buried cavity

Homologous ligands accommodated by discrete conformations of a buried cavity

Predicting binding free energies: Frontiers and benchmarks

Predicting Binding Free Energies: Frontiers and Benchmarks

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