Semiclassical statistical mechanics of hard-body fluid mixtures

Kumar, Jyotish; Dey, Tarun Kumar; Sinha, S. K.

doi:10.1063/1.1917748

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…RBFEs were calculated using the same systems as in ABFE calculations, including membrane and water. The coordinates of the PIEZO1 open and closed systems were taken from ABFE simulations and used as starting points for RBFE calculations with thermodynamic integration (TI) ( 65 ) method in AMBER. The AMBER force field [ff19SB ( 43 ) for protein, GAFF2.1 ( 44 ) for ligand, and Lipid17 for lipids] was used for all the simulation systems.…”

Section: Methodsmentioning

confidence: 99%

Structural and thermodynamic framework for PIEZO1 modulation by small molecules

Jiang,

Wijerathne,

Zhang

et al. 2023

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

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Mechanosensitive PIEZO channels constitute potential pharmacological targets for multiple clinical conditions, spurring the search for potent chemical PIEZO modulators. Among them is Yoda1, a widely used synthetic small molecule PIEZO1 activator discovered through cell-based high-throughput screening. Yoda1 is thought to bind to PIEZO1’s mechanosensory arm domain, sandwiched between two transmembrane regions near the channel pore. However, how the binding of Yoda1 to this region promotes channel activation remains elusive. Here, we first demonstrate that cross-linking PIEZO1 repeats A and B with disulfide bridges reduces the effects of Yoda1 in a redox-dependent manner, suggesting that Yoda1 acts by perturbing the contact between these repeats. Using molecular dynamics–based absolute binding free energy simulations, we next show that Yoda1 preferentially occupies a deeper, amphipathic binding site with higher affinity in PIEZO1 open state. Using Yoda1’s binding poses in open and closed states, relative binding free energy simulations were conducted in the membrane environment, recapitulating structure–activity relationships of known Yoda1 analogs. Through virtual screening of an 8 million-compound library using computed fragment maps of the Yoda1 binding site, we subsequently identified two chemical scaffolds with agonist activity toward PIEZO1. This study supports a pharmacological model in which Yoda1 activates PIEZO1 by wedging repeats A and B, providing a structural and thermodynamic framework for the rational design of PIEZO1 modulators. Beyond PIEZO channels, the three orthogonal computational approaches employed here represent a promising path toward drug discovery in highly heterogeneous membrane protein systems.

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Section: Methodsmentioning

confidence: 99%

Structural and thermodynamic framework for PIEZO1 modulation by small molecules

Jiang,

Wijerathne,

Zhang

et al. 2023

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

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show abstract

“…The coordinates of the Piezo1 open and closed systems were taken from ABFE simulations and used as starting points for relative binding free energy (RBFE) calculations with thermodynamic integration (TI) 58 method in AMBER. The AMBER force field (ff19SB 59 for protein, GAFF2.1 60 for ligand, and Lipid17 for lipids) was used for all the simulation systems.…”

Section: Theory and Methodsmentioning

confidence: 99%

Binding Free Energies of Piezo1 Channel Agonists at Protein-Membrane Interface

Jiang

Zhang

Yichun-Lin

et al. 2022

Preprint

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Mechanosensitive Piezo channels convert mechanical stimuli into biological signals in vertebrates. Piezo1 chemical modulators are anticipated to yield many clinical benefits. To date, Yoda1 is the most potent and widely used Piezo1-selective agonist, yet how Yoda1 interacts with Piezo1 at the protein-membrane interface and stabilizes Piezo1’s open state remains elusive. Here, using a previously identified putative Yoda1 binding site and three molecular dynamics (MD)-based methods, we computed the binding free energies of Yoda1 and its analogs in a Piezo1 cryo-EM closed state and an in silico open state. Our computed absolute binding free energy of Yoda1 in the closed state agrees well with the experimental Kd in which Piezo1 is expected to be in a closed state. More importantly, Yoda1 binds the open state better than the closed state, in agreement with its agonist effects. All three methods predicted that Dooku1, a Yoda1 analog, binds the closed state stronger than Yoda1, but binds the open state weaker than Yoda1. These results are consistent with the fact that Dooku1 antagonizes the effects of Yoda1 but lacks the ability to activate Piezo1. The relative binding free energies of seven Yoda1 analogs recapitulate key experimental structure-activity-relationships (SAR). Based on the state-dependent binding free energies, we were able to predict whether a molecule is an agonist or inhibitor and whether a chemical modification will lead to a change in affinity or efficacy. These mechanistic insights and computational workflow designed for transmembrane binders open an avenue to structural-based screening and design of novel Piezo1 agonists and inhibitors.

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“…Ligand binding free energy calculations can be classified into absolute and relative ones, which are determined by the thermodynamic end states. 15 The protein−ligand relative binding free energy (ΔΔG bind ) calculations require less computational time than their absolute counterparts, 16−19 and can be estimated using thermodynamic integration (TI), 20 Bennett's acceptance ratio (BAR), 21 multistate-BAR (MBAR), 22 or unbinned weighted histogram analysis method (UWHAM). 23 The advances in ligand binding free energy calculations have been fostered by enhanced sampling algorithms, improved force fields (FFs), state-of-the-art high-performance computing architectures, and the tools to generate molecular systems reliably.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Ligand binding free energy calculations can be classified into absolute and relative ones, which are determined by the thermodynamic end states . The protein–ligand relative binding free energy (ΔΔ G bind ) calculations require less computational time than their absolute counterparts, − and can be estimated using thermodynamic integration (TI), Bennett’s acceptance ratio (BAR), multistate-BAR (MBAR), or unbinned weighted histogram analysis method (UWHAM) …”

Section: Introductionmentioning

confidence: 99%

Practical Guidance for Consensus Scoring and Force Field Selection in Protein–Ligand Binding Free Energy Simulations

Zhang

Kim

2022

J. Chem. Inf. Model.

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The advances in ligand binding affinity prediction have been fostered by system generation tools and improved force fields (FFs). CHARMM-GUI Free Energy Calculator provides input and postprocessing scripts for AMBER-TI free energy calculations with various FFs. In this study, we used 12 different FF combinations (ff14SB and ff19SB for protein, GAFF2.2 and OpenFF for ligand, and TIP3P, TIP4PEW, and OPC for water) to calculate relative binding free energies (ΔΔG bind) for 80 alchemical transformations (among the JACS benchmark set) with different numbers of λ windows (12 or 21) and simulation times (1, 5, or 10 ns). Our results show that 12 λ windows and 5 ns simulation time for each window are sufficient to obtain reliable ΔΔG bind with 4 independent runs for the current benchmark set. While there is no statistically noticeable performance difference among 12 different FF combinations compared to the experimental values, a combination of ff14SB + GAFF2.2 + TIP3P FFs appears to be best with a mean unsigned error of 0.87 [0.69, 1.07] kcal/mol, a root-mean-square error of 1.22 [0.94, 1.50] kcal/mol, a Pearson’s correlation of 0.64 [0.52, 0.76], a Spearman’s correlation of 0.73 [0.58, 0.83], and a Kendell’s correlation of 0.54 [0.42, 0.64]. This large-scale ΔΔG bind calculation study provides useful information about ΔΔG bind prediction with different AMBER FF combinations and presents valuable suggestions for FF selection in AMBER-TI ΔΔG bind calculations.

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Semiclassical statistical mechanics of hard-body fluid mixtures

Cited by 5 publications

References 13 publications

Structural and thermodynamic framework for PIEZO1 modulation by small molecules

Structural and thermodynamic framework for PIEZO1 modulation by small molecules

Binding Free Energies of Piezo1 Channel Agonists at Protein-Membrane Interface

Practical Guidance for Consensus Scoring and Force Field Selection in Protein–Ligand Binding Free Energy Simulations

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