Richard J. Law

Embedded cholesterol in the nicotinic acetylcholine receptor

The nicotinic acetylcholine receptor (nAChR) is a cation-selective channel central to both neuronal and muscular processes and is considered the prototype for ligand-gated ion channels, motivating a structural determination effort that spanned several decades [Unwin N (2005) Refined structure of the nicotinic acetylcholine receptor at 4 Å resolution. J Mol Biol 346:967–989]. Purified nAChR must be reconstituted in a mixture containing cholesterol to function. Proposed modes of interaction between cholesterol and the protein range from specific binding to indirect membrane-mediated mechanisms. However, the underlying cause of nAChR sensitivity to cholesterol remains controversial, in part because the vast majority of functional studies were conducted before a medium resolution structure was reported. We show that the nAChR contains internal sites capable of containing cholesterol, whose occupation stabilizes the protein structure. We detect sites at the protein–lipid interface as conventionally predicted from functional data, as well as deeply buried sites that are not usually considered. Molecular dynamics simulations reveal that occupation of both superficial and deeply buried sites most effectively preserves the experimental structure; the structure collapses in the absence of bound cholesterol. In particular, we find that bound cholesterol directly supports contacts between the agonist-binding domain and the pore that are thought to be essential for activation of the receptor. These results likely apply to those other ion channels within the Cys-loop superfamily that depend on cholesterol, such as the GABA receptor.

Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method

BackgroundThe reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes.ResultsWe applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the abinitio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy.ConclusionsOur results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone.

Structure and dynamics of the pore-lining helix of the nicotinic receptor: MD simulations in water, lipid bilayers, and transbilayer bundles

Multiple nanosecond duration molecular dynamics simulations on the pore‐lining M2 helix of the nicotinic acetylcholine receptor reveal how its structure and dynamics change as a function of environment. In water, the M2 helix partially unfolds to form a molecular hinge in the vicinity of a central Leu residue that has been implicated in the mechanism of ion channel gating. In a phospholipid bilayer, either as a single transmembrane helix, or as part of a pentameric helix bundle, the M2 helix shows less flexibility, but still exhibits a kink in the vicinity of the central Leu. The single M2 helix tilts relative to the bilayer normal by 12°, in agreement with recent solid state NMR data ( Opella et al. , Nat Struct Biol 6:374–379, 1999). The pentameric helix bundle, a model for the pore domain of the nicotinic receptor and for channels formed by M2 peptides in a bilayer, is remarkably stable over a 2‐ns MD simulation in a bilayer, provided one adjusts the pKAs of ionizable residues to their calculated values (when taking their environment into account) before starting the simulation. The resultant transbilayer pore shows fluctuations at either mouth which transiently close the channel. Proteins 2000;39:47–55.

The multiple roles of computational chemistry in fragment-based drug design

Fragment-based drug discovery (FBDD) represents a change in strategy from the screening of molecules with higher molecular weights and physical properties more akin to fully drug-like compounds, to the screening of smaller, less complex molecules. This is because it has been recognised that fragment hit molecules can be efficiently grown and optimised into leads, particularly after the binding mode to the target protein has been first determined by 3D structural elucidation, e.g. by NMR or X-ray crystallography. Several studies have shown that medicinal chemistry optimisation of an already drug-like hit or lead compound can result in a final compound with too high molecular weight and lipophilicity. The evolution of a lower molecular weight fragment hit therefore represents an attractive alternative approach to optimisation as it allows better control of compound properties. Computational chemistry can play an important role both prior to a fragment screen, in producing a target focussed fragment library, and post-screening in the evolution of a drug-like molecule from a fragment hit, both with and without the available fragment-target co-complex structure. We will review many of the current developments in the area and illustrate with some recent examples from successful FBDD discovery projects that we have conducted.

Compound Design by Fragment-Linking

The linking together of two fragment compounds that bind to distinct protein sub‐sites can lead to a superadditivity of binding affinities, in which the binding free energy of the linked fragments exceeds the simple sum of the binding energies of individual fragments (linking coefficient E<1). However, a review of the literature shows that such events are relatively rare and, in the majority of the cases, linking coefficients are far from optimal being much greater than 1. It is critical to design a linker that does not disturb the original binding poses of each fragment in order to achieve successful linking. However, such an ideal linker is often difficult to design and even more difficult to actually synthesize. We suggest that the chance of achieving successful fragment linking can be significantly improved by choosing a fragment pair that consists of one fragment that binds by strong H‐bonds (or non‐classical equivalents) and a second fragment that is more tolerant of changes in binding mode (hydrophobic or vdW binders). We also propose that the fragment molecular orbital (FMO) calculations can be used to analyse the nature of the binding interactions of the fragment hits for the selection of fragments for evolution, merging and linking in order to optimize the chance of success.

Study of human Orexin-1 and -2 G-protein-coupled receptors with novel and published antagonists by modeling, molecular dynamics simulations, and site-directed mutagenesis.

The class A G-protein-coupled receptors (GPCRs) Orexin-1 (OX1) and Orexin-2 (OX2) are located predominantly in the brain and are linked to a range of different physiological functions, including the control of feeding, energy metabolism, modulation of neuro-endocrine function, and regulation of the sleep-wake cycle. Site-directed mutagenesis (SDM) and domain exchange (chimera) studies have provided important insight into key features of the OX1 and OX2 binding sites. However, the precise determinants of antagonist binding and selectivity are still not fully known. In this work, we used homology modeling of OX receptors to direct further SDM studies. These SDM studies were followed by molecular dynamics (MD) simulations to rationalize the full scope of the SDM data and to explain the role of each mutated residue in the binding and selectivity of a set of OX antagonists: Almorexant (dual OX1 and OX2 antagonist), SB-674042 (OX1 selective antagonist), EMPA (OX2 selective antagonist), and others. Our primary interest was focused on transmembrane helix 3 (TM3), which is identified as being of great importance for the selectivity of OX antagonists. These studies revealed conformational differences between the TM3 helices of OX1 and OX2, resulting from differences in amino acid sequences of the OX receptors that affect key interhelical interactions formed between TM3 and neighboring TM domains. The MD simulation protocol used here, which was followed by flexible docking studies, went beyond the use of static models and allowed for a more detailed exploration of the OX structures. In this work, we have demonstrated how even small differences in the amino acid sequences of GPCRs can lead to significant differences in structure, antagonist binding affinity, and selectivity of these receptors. The MD simulations allowed refinement of the OX receptor models to a degree that was not possible with static homology modeling alone and provided a deeper rationalization of the SDM data obtained. To validate these findings and to demonstrate that they can be usefully applied to the design of novel, very selective OX antagonists, we show here two examples of antagonists designed in house: EP-109-0092 (OX1 selective) and EP-009-0513 (OX2 selective).

Cell-free expression for nanolipoprotein particles: Building a high-throughput membrane protein solubility platform

Membrane-associated proteins and protein complexes account for approximately a third or more of the proteins in the cell (1, 2). These complexes mediate essential cellular processes; including signal transduc-tion, transport, recognition, bioenergetics and cell-cell communication. In general, membrane proteins are challenging to study because of their insolubility and tendency to aggregate when removed from their protein lipid bilayer environment. This chapter is focused on describing a novel method for producing and solubilizing membrane proteins that can be easily adapted to high-throughput expression screening. This process is based on cell-free transcription and translation technology coupled with nanolipoprotein par ticles (NLPs), which are lipid bilayers confined within a ring of amphipathic protein of defined diameter. The NLPs act as a platform for inserting, solubilizing and characterizing functional membrane proteins. NLP component proteins (apolipoproteins), as well as membrane proteins can be produced by either traditional cell-based or as discussed here, cell-free expression methodologies.

Pores formed by the nicotinic receptor M2δ peptide: A molecular dynamics simulation study

The M2delta peptide self-assembles to form a pentameric bundle of transmembrane alpha-helices that is a model of the pore-lining region of the nicotinic acetylcholine receptor. Long (>15 ns) molecular dynamics simulations of a model of the M2delta(5) bundle in a POPC bilayer have been used to explore the conformational dynamics of the channel assembly. On the timescale of the simulation, the bundle remains relatively stable, with the polar pore-lining side chains remaining exposed to the lumen of the channel. Fluctuations at the helix termini, and in the helix curvature, result in closing/opening transitions at both mouths of the channel, on a timescale of approximately 10 ns. On average, water within the pore lumen diffuses approximately 4x more slowly than water outside the channel. Examination of pore water trajectories reveals both single-file and path-crossing regimes to occur at different times within the simulation.

Membrane proteins: Aquaporins--channels without ions.

Recently determined structures have shed new light on the way that aquaporins act as passive, but selective, pores for the transport of small molecules--such as water or glycerol--across membranes.

Prediction of Small Molecule Hydration Thermodynamics with Grid Cell Theory.

An efficient methodology has been developed to quantify water energetics by analysis of explicit solvent molecular simulations of organic and biomolecular systems. The approach, grid cell theory (GCT), relies on a discretization of the cell theory methodology on a three-dimensional grid to spatially resolve the density, enthalpy, and entropy of water molecules in the vicinity of solute(s) of interest. Entropies of hydration are found to converge more efficiently than enthalpies of hydration. GCT predictions of free energies of hydration on a data set of small molecules are strongly correlated with thermodynamic integration predictions. Agreement with the experiment is comparable for both approaches. A key advantage of GCT is its ability to provide from a single simulation insightful graphical analyses of spatially resolved components of the enthalpies and entropies of hydration.