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Insights into Membrane Protein-Lipid Interactions from Free Energy Calculations.

ABSTRACT: Integral membrane proteins are regulated by specific interactions with lipids from the surrounding bilayer. The structures of protein-lipid complexes can be determined through a combination of experimental and computational approaches, but the energetic basis of these interactions is difficult to resolve. Molecular dynamics simulations provide the primary computational technique to estimate the free energies of these interactions. We demonstrate that the energetics of protein-lipid interactions may be reliably and reproducibly calculated using three simulation-based approaches: potential of mean force calculations, alchemical free energy perturbation, and well-tempered metadynamics. We employ these techniques within the framework of a coarse-grained force field and apply them to both bacterial and mammalian membrane protein-lipid systems. We demonstrate good agreement between the different techniques, providing a robust framework for their automated implementation within a pipeline for annotation of newly determined membrane protein structures.

SUBMITTER: Corey RA

PROVIDER: S-EPMC6785801 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Insights into Membrane Protein-Lipid Interactions from Free Energy Calculations.

Corey Robin A RA Vickery Owen N ON Sansom Mark S P MSP Stansfeld Phillip J PJ

Journal of chemical theory and computation 20190917 10

Integral membrane proteins are regulated by specific interactions with lipids from the surrounding bilayer. The structures of protein-lipid complexes can be determined through a combination of experimental and computational approaches, but the energetic basis of these interactions is difficult to resolve. Molecular dynamics simulations provide the primary computational technique to estimate the free energies of these interactions. We demonstrate that the energetics of protein-lipid interactions ...[more]

PMID: 31476127

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Project description:Using a recently developed binary bilayer system (BBS) consisting of two patches of laterally contacting bilayers, umbrella sampling molecular dynamics (MD) simulations were performed for quantitative characterization of protein-lipid interactions. The BBS is composed of 1,2-dilauroyl-sn-glycero-3-phosphocholine (DLPC) and 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) with an embedded model membrane protein, a gramicidin A (gA) channel. The calculated free energy difference for the transfer of a gA channel from DLPC (hydrophobic thickness ? 21.5 Å) to DMPC (hydrophobic thickness ? 25.5 Å) bilayers, ?G(DLPC ? DMPC), is -2.2 ± 0.7 kcal/mol. This value appears at odds with the traditional view that the hydrophobic length of the gA channel is ?22 Å. To understand this discrepancy, we first note that recent MD simulations by different groups have shown that lipid bilayer thickness profiles in the vicinity of a gA channel differ qualitatively from the deformation profile predicted from continuum elastic bilayer models. Our MD simulations at low and high gA:lipid molar ratios and different membrane compositions indicate that the gA channel's effective hydrophobic length is ?26 Å. Using this effective hydrophobic length, ?G(DLPC ? DMPC) determined here is in excellent agreement with predictions based on continuum elastic models (-3.0 to -2.2 kcal/mol) where the bilayer deformation energy is approximated as a harmonic function of the mismatch between the channel's effective hydrophobic length and the hydrophobic thickness of the bilayer. The free energy profile for gA in the BBS includes a barrier at the interface between the two bilayers which can be attributed to the line tension at the interface between two bilayers with different hydrophobic thicknesses. This observation implies that translation of a peptide between two different regions of a cell membrane (such as between the liquid ordered and disordered phases) may include effects of a barrier at the interface in addition to the relative free energies of the species far from the interface. The BBS allows for direct transfer free energy calculations between bilayers without a need of a reference medium, such as bulk water, and thus provides an efficient simulation protocol for the quantitative characterization of protein-lipid interactions at all-atom resolution.

Project description:A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

Project description:Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s13321-022-00639-y.

Dataset Information

Insights into Membrane Protein-Lipid Interactions from Free Energy Calculations.

Publications

Insights into Membrane Protein-Lipid Interactions from Free Energy Calculations.

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