Alexander Heifetz

Accurate calculation of the absolute free energy of binding for drug molecules

Free energy calculations based on molecular dynamics and thermodynamic cycles accurately reproduce experimental affinities of diverse bromodomain inhibitors.

The Fragment Molecular Orbital Method Reveals New Insight into the Chemical Nature of GPCR–Ligand Interactions

Our interpretation of ligand-protein interactions is often informed by high-resolution structures, which represent the cornerstone of structure-based drug design. However, visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum Mechanics approaches are often too computationally expensive, but one method, Fragment Molecular Orbital (FMO), offers an excellent compromise and has the potential to reveal key interactions that would otherwise be hard to detect. To illustrate this, we have applied the FMO method to 18 Class A GPCR-ligand crystal structures, representing different branches of the GPCR genome. Our work reveals key interactions that are often omitted from structure-based descriptions, including hydrophobic interactions, nonclassical hydrogen bonds, and the involvement of backbone atoms. This approach provides a more comprehensive picture of receptor-ligand interactions than is currently used and should prove useful for evaluation of the chemical nature of ligand binding and to support structure-based drug design.

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations.

Binding selectivity is a requirement for the development of a safe drug, and it is a critical property for chemical probes used in preclinical target validation. Engineering selectivity adds considerable complexity to the rational design of new drugs, as it involves the optimization of multiple binding affinities. Computationally, the prediction of binding selectivity is a challenge, and generally applicable methodologies are still not available to the computational and medicinal chemistry communities. Absolute binding free energy calculations based on alchemical pathways provide a rigorous framework for affinity predictions and could thus offer a general approach to the problem. We evaluated the performance of free energy calculations based on molecular dynamics for the prediction of selectivity by estimating the affinity profile of three bromodomain inhibitors across multiple bromodomain families, and by comparing the results to isothermal titration calorimetry data. Two case studies were considered. In the first one, the affinities of two similar ligands for seven bromodomains were calculated and returned excellent agreement with experiment (mean unsigned error of 0.81 kcal/mol and Pearson correlation of 0.75). In this test case, we also show how the preferred binding orientation of a ligand for different proteins can be estimated via free energy calculations. In the second case, the affinities of a broad-spectrum inhibitor for 22 bromodomains were calculated and returned a more modest accuracy (mean unsigned error of 1.76 kcal/mol and Pearson correlation of 0.48); however, the reparametrization of a sulfonamide moiety improved the agreement with experiment.

GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1–2 September 2014

G-protein coupled receptors (GPCRs) are the targets of over half of all prescribed drugs today. The UniProt database has records for about 800 proteins classified as GPCRs, but drugs have only been developed against 50 of these. Thus, there is huge potential in terms of the number of targets for new therapies to be designed. Several breakthroughs in GPCRs biased pharmacology, structural biology, modelling and scoring have resulted in a resurgence of interest in GPCRs as drug targets. Therefore, an international conference, sponsored by the Royal Society, with world-renowned researchers from industry and academia was recently held to discuss recent progress and highlight key areas of future research needed to accelerate GPCR drug discovery. Several key points emerged. Firstly, structures for all three major classes of GPCRs have now been solved and there is increasing coverage across the GPCR phylogenetic tree. This is likely to be substantially enhanced with data from x-ray free electron sources as they move beyond proof of concept. Secondly, the concept of biased signalling or functional selectivity is likely to be prevalent in many GPCRs, and this presents exciting new opportunities for selectivity and the control of side effects, especially when combined with increasing data regarding allosteric modulation. Thirdly, there will almost certainly be some GPCRs that will remain difficult targets because they exhibit complex ligand dependencies and have many metastable states rendering them difficult to resolve by crystallographic methods. Subtle effects within the packing of the transmembrane helices are likely to mask and contribute to this aspect, which may play a role in species dependent behaviour. This is particularly important because it has ramifications for how we interpret pre-clinical data. In summary, collaborative efforts between industry and academia have delivered significant progress in terms of structure and understanding of GPCRs and will be essential for resolving problems associated with the more difficult targets in the future.

Discovery of the First Selective, Nonpeptidic Orexin 2 Receptor Agonists.

In this issue, Nagase and colleagues report the discovery of the first selective nonpeptidic orexin 2 receptor (OX2R) agonists. The discovery of these OX2R selective agonists opens up new avenues for therapies related to the activation of the orexin system, especially with respect to the treatment of sleep disorders such as narcolepsy.

Guiding lead optimization with GPCR structure modeling and molecular dynamics

G-protein coupled receptor (GPCR) modeling approaches are widely used in the hit-to-lead and lead optimization stages of drug discovery. Modern protocols that involve molecular dynamics simulation can address key issues such as the free energy of binding (affinity), ligand-induced GPCR flexibility, ligand binding kinetics, conserved water positions and their role in ligand binding and the effects of mutations. The goals of these calculations are to predict the structures of the complexes between existing ligands and their receptors, to understand the key interactions and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this review we present a brief survey of various computational approaches illustrated through a hierarchical GPCR modeling protocol and its prospective application in three industrial drug discovery projects.

Using Electrophysiology and In Silico Three-Dimensional Modeling to Reduce Human Ether-à-go-go Related Gene K+ Channel Inhibition in a Histamine H3 Receptor Antagonist Program

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.

Rapid and accurate assessment of GPCR–ligand interactions Using the fragment molecular orbital‐based density‐functional tight‐binding method

The reliable and precise evaluation of receptor–ligand interactions and pair‐interaction energy is an essential element of rational drug design. While quantum mechanical (QM) methods have been a promising means by which to achieve this, traditional QM is not applicable for large biological systems due to its high computational cost. Here, the fragment molecular orbital (FMO) method has been used to accelerate QM calculations, and by combining FMO with the density‐functional tight‐binding (DFTB) method we are able to decrease computational cost 1000 times, achieving results in seconds, instead of hours. We have applied FMO‐DFTB to three different GPCR–ligand systems. Our results correlate well with site directed mutagenesis data and findings presented in the published literature, demonstrating that FMO‐DFTB is a rapid and accurate means of GPCR–ligand interactions.

Using the fragment molecular orbital method to investigate agonist-orexin-2 receptor interactions.

The understanding of binding interactions between any protein and a small molecule plays a key role in the rationalization of affinity and selectivity and is essential for an efficient structure-based drug discovery (SBDD) process. Clearly, to begin SBDD, a structure is needed, and although there has been fantastic progress in solving G-protein-coupled receptor (GPCR) crystal structures, the process remains quite slow and is not currently feasible for every GPCR or GPCR–ligand complex. This situation significantly limits the ability of X-ray crystallography to impact the drug discovery process for GPCR targets in ‘real-time’ and hence there is still a need for other practical and cost-efficient alternatives. We present here an approach that integrates our previously described hierarchical GPCR modelling protocol (HGMP) and the fragment molecular orbital (FMO) quantum mechanics (QM) method to explore the interactions and selectivity of the human orexin-2 receptor (OX2R) and its recently discovered nonpeptidic agonists. HGMP generates a 3D model of GPCR structures and its complexes with small molecules by applying a set of computational methods. FMO allows ab initio approaches to be applied to systems that conventional QM methods would find challenging. The key advantage of FMO is that it can reveal information on the individual contribution and chemical nature of each residue and water molecule to the ligand binding that normally would be difficult to detect without QM. We illustrate how the combination of both techniques provides a practical and efficient approach that can be used to analyse the existing structure–function relationships (SAR) and to drive forward SBDD in a real-world example for which there is no crystal structure of the complex available.

Application of an Integrated GPCR SAR-Modeling Platform To Explain the Activation Selectivity of Human 5-HT2C over 5-HT2B.

Agonism of the 5-HT2C serotonin receptor has been associated with the treatment of a number of diseases including obesity, psychiatric disorders, sexual health, and urology. However, the development of effective 5-HT2C agonists has been hampered by the difficulty in obtaining selectivity over the closely related 5-HT2B receptor, agonism of which is associated with irreversible cardiac valvulopathy. Understanding how to design selective agonists requires exploration of the structural features governing the functional uniqueness of the target receptor relative to related off targets. X-ray crystallography, the major experimental source of structural information, is a slow and challenging process for integral membrane proteins, and so is currently not feasible for every GPCR or GPCR-ligand complex. Therefore, the integration of existing ligand SAR data with GPCR modeling can be a practical alternative to provide this essential structural insight. To demonstrate this, we integrated SAR data from 39 azepine series 5-HT2C agonists, comprising both selective and unselective examples, with our hierarchical GPCR modeling protocol (HGMP). Through this work we have been able to demonstrate how relatively small differences in the amino acid sequences of GPCRs can lead to significant differences in secondary structure and function, as supported by experimental data. In particular, this study suggests that conformational differences in the tilt of TM7 between 5-HT2B and 5-HT2C, which result from differences in interhelical interactions, may be the major source of selectivity in G-protein activation between these two receptors. Our approach also demonstrates how the use of GPCR models in conjunction with SAR data can be used to explain activity cliffs.