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The description of protein internal motions aids selection of ligand binding poses by the INPHARMA method.

ABSTRACT: Protein internal motions influence observables of NMR experiments. The effect of internal motions occurring at the sub-nanosecond timescale can be described by NMR order parameters. Here, we report that the use of order parameters derived from Molecular Dynamics (MD) simulations of two holo-structures of Protein Kinase A increase the discrimination power of INPHARMA, an NMR based methodology that selects docked ligand orientations by maximizing the correlation of back-calculated to experimental data. By including internal motion in the back-calculation of the INPHARMA transfer, we obtain a more realistic description of the system, which better represents the experimental data. Furthermore, we propose a set of generic order parameters, derived from MD simulations of globular proteins, which can be used in the back-calculation of INPHARMA NOEs for any protein-ligand complex, thus by-passing the need of obtaining system-specific order parameters for new protein-ligand complexes.

SUBMITTER: Stauch B

PROVIDER: S-EPMC3483107 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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The description of protein internal motions aids selection of ligand binding poses by the INPHARMA method.

Stauch Benjamin B Orts Julien J Carlomagno Teresa T

Journal of biomolecular NMR 20120922 3

Protein internal motions influence observables of NMR experiments. The effect of internal motions occurring at the sub-nanosecond timescale can be described by NMR order parameters. Here, we report that the use of order parameters derived from Molecular Dynamics (MD) simulations of two holo-structures of Protein Kinase A increase the discrimination power of INPHARMA, an NMR based methodology that selects docked ligand orientations by maximizing the correlation of back-calculated to experimental ...[more]

PMID: 23001323

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Project description:Recent advances in improved force fields and sampling methods have made it possible for the accurate calculation of protein–ligand binding free energies. Alchemical free energy perturbation (FEP) using an explicit solvent model is one of the most rigorous methods to calculate relative binding free energies. However, for cases where there are high energy barriers separating the relevant conformations that are important for ligand binding, the calculated free energy may depend on the initial conformation used in the simulation due to the lack of complete sampling of all the important regions in phase space. This is particularly true for ligands with multiple possible binding modes separated by high energy barriers, making it difficult to sample all relevant binding modes even with modern enhanced sampling methods. In this paper, we apply a previously developed method that provides a corrected binding free energy for ligands with multiple binding modes by combining the free energy results from multiple alchemical FEP calculations starting from all enumerated poses, and the results are compared with Glide docking and MM-GBSA calculations. From these calculations, the dominant ligand binding mode can also be predicted. We apply this method to a series of ligands that bind to c-Jun N-terminal kinase-1 (JNK1) and obtain improved free energy results. The dominant ligand binding modes predicted by this method agree with the available crystallography, while both Glide docking and MM-GBSA calculations incorrectly predict the binding modes for some ligands. The method also helps separate the force field error from the ligand sampling error, such that deviations in the predicted binding free energy from the experimental values likely indicate possible inaccuracies in the force field. An error in the force field for a subset of the ligands studied was identified using this method, and improved free energy results were obtained by correcting the partial charges assigned to the ligands. This improved the root-mean-square error (RMSE) for the predicted binding free energy from 1.9 kcal/mol with the original partial charges to 1.3 kcal/mol with the corrected partial charges.

Project description:Accurately computing the free energy for biological processes like protein folding or protein-ligand association remains a challenging problem. Both describing the complex intermolecular forces involved and sampling the requisite configuration space make understanding these processes innately difficult. Herein, we address the sampling problem using a novel methodology we term "movable type". Conceptually it can be understood by analogy with the evolution of printing and, hence, the name movable type. For example, a common approach to the study of protein-ligand complexation involves taking a database of intact drug-like molecules and exhaustively docking them into a binding pocket. This is reminiscent of early woodblock printing where each page had to be laboriously created prior to printing a book. However, printing evolved to an approach where a database of symbols (letters, numerals, etc.) was created and then assembled using a movable type system, which allowed for the creation of all possible combinations of symbols on a given page, thereby, revolutionizing the dissemination of knowledge. Our movable type (MT) method involves the identification of all atom pairs seen in protein-ligand complexes and then creating two databases: one with their associated pairwise distant dependent energies and another associated with the probability of how these pairs can combine in terms of bonds, angles, dihedrals and non-bonded interactions. Combining these two databases coupled with the principles of statistical mechanics allows us to accurately estimate binding free energies as well as the pose of a ligand in a receptor. This method, by its mathematical construction, samples all of configuration space of a selected region (the protein active site here) in one shot without resorting to brute force sampling schemes involving Monte Carlo, genetic algorithms or molecular dynamics simulations making the methodology extremely efficient. Importantly, this method explores the free energy surface eliminating the need to estimate the enthalpy and entropy components individually. Finally, low free energy structures can be obtained via a free energy minimization procedure yielding all low free energy poses on a given free energy surface. Besides revolutionizing the protein-ligand docking and scoring problem this approach can be utilized in a wide range of applications in computational biology which involve the computation of free energies for systems with extensive phase spaces including protein folding, protein-protein docking and protein design.

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The description of protein internal motions aids selection of ligand binding poses by the INPHARMA method.

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The description of protein internal motions aids selection of ligand binding poses by the INPHARMA method.

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