Reproducing ensemble averaged electrostatics with Super-Gaussian-based smooth dielectric function: application to electrostatic component of binding energy of protein complexes

Panday, Shailesh Kumar; Shashikala, Mihiri H. B.; Chakravorty, Arghya; Zhao, Shan; Alexov, Emil

doi:10.4310/cis.2019.v19.n4.a4

Cited by 6 publications

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“…However, we have shown in several works that energy minimized structures provide solvation energy which is very similar to the solvation energy obtained over an ensemble of MD snapshots. 51,54,55 This is true for both traditional two-dielectric PB and Gaussian-based PB. 50−52 We will use this observation in the current protocol and will model the enthalpy using a single frame, the energy minimized X-ray structure.…”

Section: Introductionmentioning

confidence: 90%

Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Panday

Alexov

2022

ACS Omega

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Here, we present a Gaussian-based method for estimation of protein–protein binding entropy to augment the molecular mechanics Poisson–Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (Δ G ). The method is termed f5-MM/PBSA/E, where “E” stands for entropy and f5 for five adjustable parameters. The enthalpy components of Δ G (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein–protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein–protein binding cases with experimentally determined dissociation constant K d values. This data set has been used for benchmarking in recently published protein–protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein–protein binding affinity prediction studies.

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Section: Introductionmentioning

confidence: 90%

Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Panday

Alexov

2022

ACS Omega

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On delivering polar solvation free energy of proteins from energy minimized structures using a regularized super‐Gaussian Poisson–Boltzmann model

Panday,

Chakravorty,

Zhao

et al. 2024

J Comput Chem

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The biomolecules interact with their partners in an aqueous media; thus, their solvation energy is an important thermodynamics quantity. In previous works (J. Chem. Theory Comput. 14(2): 1020–1032), we demonstrated that the Poisson–Boltzmann (PB) approach reproduces solvation energy calculated via thermodynamic integration (TI) protocol if the structures of proteins are kept rigid. However, proteins are not rigid bodies and computing their solvation energy must account for their flexibility. Typically, in the framework of PB calculations, this is done by collecting snapshots from molecular dynamics (MD) simulations, computing their solvation energies, and averaging to obtain the ensemble‐averaged solvation energy, which is computationally demanding. To reduce the computational cost, we have proposed Gaussian/super‐Gaussian‐based methods for the dielectric function that use the atomic packing to deliver smooth dielectric function for the entire computational space, the protein and water phase, which allows the ensemble‐averaged solvation energy to be computed from a single structure. One of the technical difficulties associated with the smooth dielectric function presentation with respect to polar solvation energy is the absence of a dielectric border between the protein and water where induced charges should be positioned. This motivated the present work, where we report a super‐Gaussian regularized Poisson–Boltzmann method and use it for computing the polar solvation energy from single energy minimized structures and assess its ability to reproduce the ensemble‐averaged polar solvation on a dataset of 74 high‐resolution monomeric proteins.

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Convergence of a diffuse interface Poisson-Boltzmann (PB) model to the sharp interface PB model: A unified regularization formulation

Shao

McGowan

Wang

et al. 2023

Applied Mathematics and Computation

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Reproducing ensemble averaged electrostatics with Super-Gaussian-based smooth dielectric function: application to electrostatic component of binding energy of protein complexes

Cited by 6 publications

References 0 publications

Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

On delivering polar solvation free energy of proteins from energy minimized structures using a regularized super‐Gaussian Poisson–Boltzmann model

Convergence of a diffuse interface Poisson-Boltzmann (PB) model to the sharp interface PB model: A unified regularization formulation

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