Including Explicit Water Molecules as Part of the Protein Structure in MM/PBSA Calculations

Zhu, Yongliang; Beroza, Paul; Artis, Dean R.

doi:10.1021/ci4001794

Cited by 44 publications

(47 citation statements)

References 21 publications

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“…The reason for the improved performance with diverse datasets is presumably due to the capture of interactions between the ligand and the closest of the four conserved water molecules also found in the binding site. This observation is in line with other work in which system dependent numbers of water molecules were found to improve rankings [43][44][45] and the broader phenomenon of the impact of crucial water molecules on SAR landscapes. Incorporation of the water molecules was highly effective in differentiating the ligands with diverse binding modes but less effective in the set of related THQ-scaffold based compounds.…”

Section: Discussionsupporting

confidence: 92%

Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

Wright¹,

Wan²,

Meyer³

et al. 2018

Preprint

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As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis.Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell.

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Section: Discussionsupporting

confidence: 92%

Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

Wright¹,

Wan²,

Meyer³

et al. 2018

Preprint

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“…CSMs have been fairly successful at predicting many quantities, such as Δ G , and the salt dependencies of various free energies, 10–14 but they have not been uniformly successful at predicting some other quantities, such as ΔΔ G . 15–21 …”

Section: Introductionmentioning

confidence: 99%

Examining the Assumptions Underlying Continuum-Solvent Models

Harris

Pettitt²

2015

J. Chem. Theory Comput.

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Continuum-solvent models (CSMs) have successfully predicted many quantities, including the solvation-free energies (ΔG) of small molecules, but they have not consistently succeeded at reproducing experimental binding free energies (ΔΔG), especially for protein–protein complexes. Several CSMs break ΔG into the free energy (ΔGvdw) of inserting an uncharged molecule into solution and the free energy (ΔGel) gained from charging. Some further divide ΔGvdw into the free energy (ΔGrep) of inserting a nearly hard cavity into solution and the free energy (ΔGatt) gained from turning on dispersive interactions between the solute and solvent. We show that for 9 protein–protein complexes neither ΔGrep nor ΔGvdw was linear in the solvent-accessible area A, as assumed in many CSMs, and the corresponding components of ΔΔG were not linear in changes in A. We show that linear response theory (LRT) yielded good estimates of ΔGatt and ΔΔGatt, but estimates of ΔΔGatt obtained from either the initial or final configurations of the solvent were not consistent with those from LRT. The LRT estimates of ΔGel differed by more than 100 kcal/mol from the explicit solvent model’s (ESM’s) predictions, and its estimates of the corresponding component (ΔΔGel) of ΔΔG differed by more than 10 kcal/mol. Finally, the Poisson–Boltzmann equation produced estimates of ΔGel that were correlated with those from the ESM, but its estimates of ΔΔGel were much less so. These findings may help explain why many CSMs have not been consistently successful at predicting ΔΔG for many complexes, including protein–protein complexes.

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“…Model‐3 . As previously examined for the MM/PBSA method, the incorporation of the interaction of explicit water molecules with a receptor successfully improved the correlation. Therefore, in this model, the m water molecules were defined as the part of a protein (RmW) as shown below:

RmW + L \to RmW - L

…”

Section: Theories and Resultsmentioning

confidence: 76%

“…Several reports have already examined the explicit water molecules that interact with a protein or a ligand using the endpoint method. In these reports, water molecules were dealt with in various ways, for instance as part of a protein, hydration shell, and cavity‐water displacement …”

Section: Theories and Resultsmentioning

confidence: 99%

“…The dataset chosen was avidin and its inhibitors (biotin and six derivatives) (Supporting Information Figure S1). This dataset was a congeneric series of ligands and has been used as a benchmark dataset in numerous studies . The conformational samples for the avidin‐inhibitor complexes were prepared in the same manner as that of the α‐thrombin‐inhibitor complex.…”

Section: Theories and Resultsmentioning

confidence: 99%

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An attempt to incorporate effect of direct interaction between a ligand and explicit water molecules into MM/3D‐RISM

Gohda

2018

Chem Biol Drug Des

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Endpoint methods using continuum-solvent models are widely used to estimate protein-ligand affinity. A recently developed method, MM/3D-RISM, estimates the solvation energy using statistical mechanics by 3D-RISM. This method is theoretically expected to accurately describe solvation effects and to also be less dependent on protein-ligand systems. In this study, we examined the performance of MM/3D-RISM for a set of α-thrombin inhibitors with a non-congeneric series of ligands, containing three diverse chemical scaffolds. The standard MM/3D-RISM showed a weak correlation (R = 0.191) but correctly estimated affinity for two of the three scaffolds. However, the simplest inhibitor, benzamidine, was not ranked appropriately. From visual inspection of inhibitor-binding modes, an attempt was made to incorporate the direct interaction between a ligand and water molecules into MM/3D-RISM. A model (Model-1) dealing with directly interacting water molecules (Wat) as an independent component of a protein (R)-ligand (L) complex-formation, that is, R + L + Wat → R-L-Wat, showed a better linearity (R = 0.422) than that of the standard MM/3D-RISM model and achieved a good ranking of all three scaffolds of α-thrombin inhibitors. Additionally, an attempt was made to model avidin-biotin system with a congeneric series of inhibitors, and results showed that both the standard MM/3D-RISM model (R = 0.839) and Model-1 (R = 0.695) satisfactorily estimated the affinity.

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Including Explicit Water Molecules as Part of the Protein Structure in MM/PBSA Calculations

Cited by 44 publications

References 21 publications

Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

Examining the Assumptions Underlying Continuum-Solvent Models

An attempt to incorporate effect of direct interaction between a ligand and explicit water molecules into MM/3D‐RISM

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