Missing Fragments: Detecting Cooperative Binding in Fragment-Based Drug Design

Nair, Pramod C.; Malde, Alpeshkumar K.; Drinkwater, N.; Mark, Alan E.

doi:10.1021/ml300015u

Cited by 28 publications

(25 citation statements)

References 17 publications

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“…96–98 These ligands can in fact be of relatively similar size and rigidity to toluene in some cases. 3 Thus, prediction of binding modes of small rigid fragments is in fact of considerable interest. Additionally, even when structural data is available for the binding of fragments, the X-ray crystal structures obtained from FBDD campaigns sometimes have ambiguous electron density for the ligand, making the binding mode difficult to determine.…”

Section: Discussionmentioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

et al. 2018

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Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

show abstract

Section: Discussionmentioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

et al. 2018

View full text Add to dashboard Cite

show abstract

“…[96][97][98] These ligands can in fact be of relatively similar size and rigidity to toluene in some cases. 3 Thus, prediction of binding modes of small rigid fragments is in fact of considerable interest. Additionally, even when structural data is available for the binding of fragments, the X-ray crystal structures obtained from FBDD campaigns sometimes have ambiguous electron density for the ligand, making the binding mode difficult to determine.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in the case of fragment-based drug discovery, small, relatively rigid ligands can often have some ambiguity in their binding modes because of internal pseudosymmetry, or other issues. 2,3 Additionally, methods to make X-ray diffraction data easier to collectsuch as cryocooling crystals-potentially stabilize binding modes that are not observed under the conditions of interest. 4 Multiple binding modes may also contribute substantially to a ligand's affinity, [5][6][7][8][9] therefore knowledge of a single experimental binding mode may be misleading or provide an incomplete picture.…”

Section: Ligand Binding Modes Are Important But Difficult To Predictmentioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Gill¹,

Lim²,

Grinaway³

et al. 2018

Preprint

View full text Add to dashboard Cite

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragmentlike ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

show abstract

“…[72][73][74] These ligands can in fact be of relatively similar size and rigidity to toluene in some cases. 3 Thus, prediction of binding modes of small rigid fragments is in fact of considerable interest. Additionally, even when structural data is available for the binding of fragments, the X-ray crystal structures obtained from FBDD campaigns sometimes have ambiguous electron density for the ligand, making the binding mode difficult to determine.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in the case of fragment-based drug discovery, small, relatively rigid ligands can often have some ambiguity in their binding modes because of internal pseudosymmetry, or other issues. 2,3 Additionally, methods to make crystallography data easier to collect, such as cryocooling crystal structures, potentially stabilize binding modes that are not observed under the conditions of interest. 4 In addition, multiple binding modes may contribute substantially to a ligand's affinity, [5][6][7][8][9] so knowledge of a single experimental binding mode may be misleading or provide an incomplete picture.…”

Section: Introductionmentioning

confidence: 99%

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Gill¹,

Lim²,

Grinaway³

et al. 2017

Preprint

View full text Add to dashboard Cite

Accurately predicting protein-ligand binding is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

show abstract

Missing Fragments: Detecting Cooperative Binding in Fragment-Based Drug Design

Cited by 28 publications

References 17 publications

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

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