Richard J. Law

Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38 MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are… Show more

Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38 MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are observed. Protocols that vary in their use of restraints on protein and ligand atoms were compared to determine the influence of protein/ligand flexibility on computed water structure and energetics, and to assess protocols for routine analyses of protein-ligand complexes. The GCT-derived binding affinities correctly reproduce experimental trends, but the magnitude of the predicted changes in binding affinities is exaggerated with respect to results from a previous Monte Carlo Free Energy Perturbation study. Breakdown of the GCT water free energies into enthalpic and entropic components indicates that enthalpy changes dominate the observed variations in energetics. In EGFR kinase GCT analyses revealed that replacement of a pyrimidine by a cyanopyridine in a ligand perturbs water energetics up three hydration shells away from the cyano group. Show less

A previously developed cell theory model of liquid water was used to evaluate the excess thermodynamic properties of confined clusters of water molecules. The results are in good agreement with reference thermodynamic integration calculations, suggesting that the model is adequate to probe the thermodynamic properties of water at interfaces or in cavities. Next, the grid cell theory (GCT) method was applied to elucidate the thermodynamic signature of nonpolar association for a range of… Show more

A previously developed cell theory model of liquid water was used to evaluate the excess thermodynamic properties of confined clusters of water molecules. The results are in good agreement with reference thermodynamic integration calculations, suggesting that the model is adequate to probe the thermodynamic properties of water at interfaces or in cavities. Next, the grid cell theory (GCT) method was applied to elucidate the thermodynamic signature of nonpolar association for a range of idealized host–guest systems. Polarity and geometry of the host cavities were systematically varied, and enthalpic and entropic solvent components were spatially resolved for detailed graphical analyses. Perturbations in the thermodynamic properties of water molecules upon guest binding are restricted to the immediate vicinity of the guest in solvent-exposed cavities, whereas longer-ranged perturbations are observed in buried cavities. Depending on the polarity and geometry of the host, water displacement by a nonpolar guest makes a small or large enthalpic or entropic contribution to the free energy of binding. Thus, no assumptions about the thermodynamic signature of the hydrophobic effect can be made in general. Overall the results warrant further applications of GCT to more complex systems such as protein–ligand complexes. Show less

G-Protein Coupled Receptors (GPCRs) have enormous physiological and biomedical
importance, being the primary target of a large number of modern drugs. The availability of
structural information of the binding site of the targeted GPCR plays a key role in
rationalization, efficiency and cost-effectiveness of the drug discovery process. However,
obtaining structural information on GPCRs using X-ray crystallography or NMR requires a
large investment of time and is technically very… Show more

G-Protein Coupled Receptors (GPCRs) have enormous physiological and biomedical
importance, being the primary target of a large number of modern drugs. The availability of
structural information of the binding site of the targeted GPCR plays a key role in
rationalization, efficiency and cost-effectiveness of the drug discovery process. However,
obtaining structural information on GPCRs using X-ray crystallography or NMR requires a
large investment of time and is technically very challenging. This situation significantly limits
the ability of these methods to have an impact in drug discovery for GPCR targets in the
short term and hence there is an urgent need for other effective and cost-efficient
alternatives. We present here a practical approach that integrates GPCR modelling with
fragment based screening to provide structural insights on the H3 and H4 histamine receptor
binding sites. This approach creates a cost-efficient new avenue for structure-based drug
design (SBDD) against GPCR targets. We report here a success of using this protocol for the
discovery of selective and dual H3 and H4 antagonists. Our fragment screen yielded 44 H3,
21 H4 selective and 20 dual fragment hits. These fragments were used to construct highquality
H3 and H4 models followed by binding site exploration and structure based virtual
screening (VS). Overall, 172 compounds were purchased for testing based on the virtual
screening results. Of the 74 compounds predicted to have dual activity, 33 had activity
against one or other of the two receptors (44%), of which 17 had activity against both. Of the
19 compounds predicted to be H3 selective, 13 were active against H3 (68%) and 10 of
these also had selectivity over H4. Of the 79 compounds predicted to be H4 selective, 36
were active against H4 (45%) and 2 of these also had selectivity over H3. Show less

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. The natural agonists for OX1 and OX2 are two neuropeptides Orexin A and Orexin B, which have activity at both receptors. Site-directed mutagenesis (SDM) has been reported on both the… Show more

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. The natural agonists for OX1 and OX2 are two neuropeptides Orexin A and Orexin B, which have activity at both receptors. Site-directed mutagenesis (SDM) has been reported on both the receptors and the peptides and has provided important insight into key features responsible for agonist activity. However, the structural interpretation of how these data are linked together is still lacking. In this work we produced and used SDM data, homology modelling followed by MD simulation and ensemble-flexible docking to generate binding poses of the Orexin peptides in the OX receptors to rationalize the SDM data. We also developed a protein pair-wise similarity comparing method (ProS) and a GPCR-likeness assessment score (GLAS) to explore the structural data generated within an MD simulation and to help distinguish between different GPCR substates. The results demonstrate how these newly developed methods of structural assessment for GPCRs can be used to provide a working model of neuropeptide-Orexin receptor interaction. Show less

Obesity is an increasingly common disease. While antagonism of the melanin-concentrating hormone-1 receptor (MCH-1R) has been widely reported as a promising therapeutic avenue for obesity treatment, no MCH-1R antagonists have reached the market. Discovery and optimization of new chemical matter targeting MCH-1R is hindered by reduced HTS success rates and a lack of structural information about the MCH-1R binding site. X-ray crystallography and NMR, the major experimental sources of structural… Show more

Obesity is an increasingly common disease. While antagonism of the melanin-concentrating hormone-1 receptor (MCH-1R) has been widely reported as a promising therapeutic avenue for obesity treatment, no MCH-1R antagonists have reached the market. Discovery and optimization of new chemical matter targeting MCH-1R is hindered by reduced HTS success rates and a lack of structural information about the MCH-1R binding site. X-ray crystallography and NMR, the major experimental sources of structural information, are very slow processes for membrane proteins, and are not currently feasible for every GPCR or GPCR-ligand complex. This situation significantly limits the ability of these methods to impact the drug discovery process for GPCR targets in "real-time" and hence there is an urgent need for other practical and cost-efficient alternatives. We present here a conceptually pioneering approach that integrates GPCR modeling with design, synthesis and screening of a diverse library of sugar-based compounds from the VAST technology (Versatile Assembly on Stable Templates), to provide structural insights on the MCH-1R binding site. This approach creates a cost-efficient new avenue for structure-based drug discovery (SBDD) against GPCR targets. In our work, a primary VAST hit was used to construct a high-quality MCH-1R model. Following model validation, a structure-based virtual screen yielded a 14% hit rate and 10 novel chemotypes of potent MCH-1R antagonists, including EOAI3367472 (IC50 = 131 nM) and EOAI3367474 (IC50 = 213 nM). Show less

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… Show more

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). Show less

The benefit of fragment screening and optimisation within the drug discovery process is maximised when the technique is used in conjunction with other hit-finding methods. This is because information from fragments can inform the optimisation of hits identified by other means (e.g. high-throughput screening [HTS] and/or virtual screening) and vice versa. Illustrative of this approach is the discovery of inhibitors for two targets, beta-secretase (BACE-1) and phosphodiesterase 10a (PDE10a)… Show more

The benefit of fragment screening and optimisation within the drug discovery process is maximised when the technique is used in conjunction with other hit-finding methods. This is because information from fragments can inform the optimisation of hits identified by other means (e.g. high-throughput screening [HTS] and/or virtual screening) and vice versa. Illustrative of this approach is the discovery of inhibitors for two targets, beta-secretase (BACE-1) and phosphodiesterase 10a (PDE10a), where X-ray crystal structures can be readily obtained for complexes with fragments. Fragment information can also be applied to the discovery of ligands for targets that are not readily amenable to structural analysis, such as G-protein-coupled receptors (GPCRs), through the application of computational modelling methods. This is illustrated through the discovery of potent and selective histamine H3 and H4 antagonists. Show less

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… Show more

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.

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Our 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.

The histamine H3 receptor (H3R) plays a regulatory role in the presynaptic release of histamine and several other neurotransmitters, and thus, it is an attractive target for central nervous system indications including cognitive disorders, narcolepsy, attention-deficit hyperactivity disorder, and pain. The development of H3R antagonists was complicated by the similarities between the pharmacophores of H3R and human Ether-à-go-go related gene (hERG) channel blockers, a fact that probably… Show more

The histamine H3 receptor (H3R) plays a regulatory role in the presynaptic release of histamine and several other neurotransmitters, and thus, it is an attractive target for central nervous system indications including cognitive disorders, narcolepsy, attention-deficit hyperactivity disorder, and pain. The development of H3R antagonists was complicated by the similarities between the pharmacophores of H3R and human Ether-à-go-go related gene (hERG) channel blockers, a fact that probably prevented promising compounds from being progressed into the clinic. Using a three-dimensional in silico modeling approach complemented with automated and manual patch clamping, we were able to separate these two pharmacophores and to develop highly potent H3R antagonists with reduced risk of hERG liabilities from initial hit series with low selectivity identified in a high-throughput screening campaign Show less

Fragment-based drug discovery has come a long way in a short period of time and is now being used throughout the biopharmaceutical industry. Here we review the origin of the approach, discuss how it is being applied and the prospects for future development. We illustrate this with examples from our own projects where we have found that information from fragments can inform the optimisation of hits identified by other means (e.g. HTS and/or virtual screening) and vice versa. We further discuss… Show more

Fragment-based drug discovery has come a long way in a short period of time and is now being used throughout the biopharmaceutical industry. Here we review the origin of the approach, discuss how it is being applied and the prospects for future development. We illustrate this with examples from our own projects where we have found that information from fragments can inform the optimisation of hits identified by other means (e.g. HTS and/or virtual screening) and vice versa. We further discuss that fragment information can also be applied to the discovery of ligands for targets that are not readily amenable to structural analysis by experimentation such as GPCRs, particularly through the application of computational modelling methods.

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The histamine H3 receptor (H3R) plays a regulatory role in the presynaptic release of histamine and several other neurotransmitters, and thus, it is an attractive target for central nervous system indications including cognitive disorders, narcolepsy, attention-deficit hyperactivity disorder, and pain. The development of H3R antagonists was complicated by the similarities between the pharmacophores of H3R and human Ether-à-go-go related gene (hERG) channel blockers, a fact that probably… Show more

The histamine H3 receptor (H3R) plays a regulatory role in the presynaptic release of histamine and several other neurotransmitters, and thus, it is an attractive target for central nervous system indications including cognitive disorders, narcolepsy, attention-deficit hyperactivity disorder, and pain. The development of H3R antagonists was complicated by the similarities between the pharmacophores of H3R and human Ether-à-go-go related gene (hERG) channel blockers, a fact that probably prevented promising compounds from being progressed into the clinic. Using a three-dimensional in silico modeling approach complemented with automated and manual patch clamping, we were able to separate these two pharmacophores and to develop highly potent H3R antagonists with reduced risk of hERG liabilities from initial hit series with low selectivity identified in a high-throughput screening campaign.

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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… Show more

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.

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