A Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding

2020 ◽  
Author(s):  
Edward Miller ◽  
Robert Murphy ◽  
Daniel Sindhikara ◽  
Ken Borrelli ◽  
Matthew Grisewood ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Structure Prediction ◽  
Heavy Atom ◽  
Accurate Solution ◽  
Binding Modes ◽  
Induced Fit Docking ◽  
Induced Fit ◽  
Test Set ◽  
Cross Docking ◽  
Public Data

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.

A Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding

2020 ◽  
Author(s):  
Edward Miller ◽  
Robert Murphy ◽  
Daniel Sindhikara ◽  
Ken Borrelli ◽  
Matthew Grisewood ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Structure Prediction ◽  
Heavy Atom ◽  
Accurate Solution ◽  
Binding Modes ◽  
Induced Fit Docking ◽  
Induced Fit ◽  
Test Set ◽  
Cross Docking ◽  
Public Data

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.

scholarly journals A Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding

2020 ◽  
Author(s):  
Edward Miller ◽  
Robert Murphy ◽  
Daniel Sindhikara ◽  
Ken Borrelli ◽  
Matthew Grisewood ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Structure Prediction ◽  
Heavy Atom ◽  
Accurate Solution ◽  
Binding Modes ◽  
Induced Fit Docking ◽  
Induced Fit ◽  
Test Set ◽  
Cross Docking ◽  
Public Data

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.

scholarly journals Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein–Ligand Binding

2021 ◽  
Vol 17 (4) ◽  
pp. 2630-2639
Author(s):  
Edward B. Miller ◽  
Robert B. Murphy ◽  
Daniel Sindhikara ◽  
Kenneth W. Borrelli ◽  
Matthew J. Grisewood ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Accurate Solution ◽  
Induced Fit Docking ◽  
Induced Fit

Prediction of Protein–Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations

2016 ◽  
Vol 12 (6) ◽  
pp. 2990-2998 ◽  
Author(s):  
Anthony J. Clark ◽  
Pratyush Tiwary ◽  
Ken Borrelli ◽  
Shulu Feng ◽  
Edward B. Miller ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Induced Fit Docking ◽  
Induced Fit

scholarly journals Structure prediction of Bestrophin for the induced - fit docking of anthocyanins

2012 ◽  
Vol 8 (16) ◽  
pp. 742-748 ◽  
Author(s):  
Sathyan Sri Lavvanya Priya ◽  
Ponnuswamy Renuka Devi ◽  
Palanisami Eganathan ◽  
Nishith Saurav Topno
Keyword(s):  
Structure Prediction ◽  
Induced Fit Docking ◽  
Induced Fit

Reversibly Sampling Conformations and Binding Modes Using Molecular Darting

2020 ◽  
Author(s):  
Samuel C. Gill ◽  
David Mobley
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Sites ◽  
Degrees Of Freedom ◽  
Dynamics Simulation ◽  
Hiv Integrase ◽  
Binding Modes ◽  
System A ◽  
Multiple Binding ◽  
Internal Degrees Of Freedom

<div>Sampling multiple binding modes of a ligand in a single molecular dynamics simulation is difficult. A given ligand may have many internal degrees of freedom, along with many different ways it might orient itself a binding site or across several binding sites, all of which might be separated by large energy barriers. We have developed a novel Monte Carlo move called Molecular Darting (MolDarting) to reversibly sample between predefined binding modes of a ligand. Here, we couple this with nonequilibrium candidate Monte Carlo (NCMC) to improve acceptance of moves.</div><div>We apply this technique to a simple dipeptide system, a ligand binding to T4 Lysozyme L99A, and ligand binding to HIV integrase in order to test this new method. We observe significant increases in acceptance compared to uniformly sampling the internal, and rotational/translational degrees of freedom in these systems.</div>

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

2017 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>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.</div><div><br></div><div>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.</div>

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

2018 ◽  
Author(s):  
Samuel Gill ◽  
Nathan M. Lim ◽  
Patrick Grinaway ◽  
Ariën S. Rustenburg ◽  
Josh Fass ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Binding Mode ◽  
Free Energy Calculations ◽  
Dynamics Simulation ◽  
Binding Modes ◽  
Enhanced Sampling ◽  
Recent Success ◽  
Multiple Binding ◽  
Proper Sampling

<div>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.</div><div><br></div><div>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.</div>

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