Molecular Medicine

Computer simulations are an indispensable tool in the drug development process to predict binding free energies, solubilities, and membrane permeability of drug candidates. Although the targets are very dierent, all free energy simulations share some common features and challenges. The two fundamental prerequisites for the determination of free energies are the accurate description of inter- and intramolecular interactions, and the adequate sampling of all relevant microstates. These two requirements are in conflict with each other, since a more sophisticated description of molecular interactions entails an increase of the computational costs, which inhibits the capability to search through a multitude of dierent possible conformations. In terms of the balance between those two requirements, one can distinguish two major classes of computational methods: a) classical force elds based on molecular mechanics (MM), which are fast and well suited for sampling, but involve approximations that limit their reliability b) quantum-mechanical methods (QM), which are based on molecular orbital calculations and combine a heavy computational burden with highly accurate interaction strengths. Full Abstract

Fragment-based lead generation (FBLG) involves scanning a library of low molecular weight compounds (fragments) to see if they bind to the target of interest and using those that do as building blocks to create create higher affinity molecules. A frequent strategy is to link multiple fragments binding different regions of the protein. FBLG represents an attractive application for in silico binding anity calculations, but the need to obtain comparable values from different binding modes represents a considerable challenge for many computational techniques. We evaluate the performance of our range of ensemble simulation based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent). In studies of drugs binding to a single site these protocols have been shown to produce results which correlate well with experiment (correlation coecients >0.7) and provide robust uncertainty estimates. Full Abstract

The evolution of resistance to antibiotics was predicted by Fleming in his Nobel Prize speech and is now accepted as posing a threat to modern medicine requiring urgent and concerted action. Helping clinicians make appropriate treatment decisions by improving the coverage, portability, speed, accuracy and cost of species identification and drug susceptibility testing will be an important part of the solution. A promising approach is to sequence the genome of any infecting pathogen(s) found in a clinical sample and, by looking up genetic variants found in genes known to confer resistance to the action of antibiotics, return a prediction of the effectiveness, or otherwise, of a panel of antibiotics to the clinician. The exemplar for this approach is tuberculosis, partly because its growth rate is so slow that culture-based clinical microbiology can take up to two months to return a result to the clinician, and partly because its genetics is simpler than other pathogens and therefore the current second-generation sequencing technologies work well. Genetics clinical microbiology has been shown to be cheaper, faster and probably more accurate than traditional culture-based clinical microbiology
for the drug susceptibility testing of tuberculosis1 and, in addition, facilitates the rapid identification of to phenotype have been carefully and extensively developed, a potential weakness remains: such an approach is fundamentally inferential and so cannot make a prediction when it encounters a genetic variant not present in the catalogue, such as is the case for rare genetic mutations. Full Abstract

Free energy calculations have become a powerful addition to the computational chemist’s toolbox to support structure-based drug design in hit-to-lead and lead optimization stages of drug discovery projects. Methodological advances, the availability of less expensive large computational resources and automated workflows have opened up the possibility to apply the technology in an industry context at large scale. In 2016, we started a large initiative at Merck KGaA to thoroughly investigate the potential of free energy calculations for compound optimization and to define best practices for using this technology. Here, we present prospective data from using FEP+ in 10 drug active discovery projects at Merck KGaA over the course of three years and compare this performance to results obtained on a new, challenging benchmark of five pharmaceutically relevant targets. We further discuss opportunities and challenges and highlight use cases and conditions that can maximize the impact of the method. Full Abstract

Pore-forming protein analogues have been fabricated from triethylene glycol-cholesterol modified DNA sequences, which hybridize to form cholesterol anchored DNA nanopores (TEG-C NP’s). These versatile nanopores can be chemically tuned to exhibit an array of functionalities with a broad range of potential applications in biomedicine e.g. novel ligand controlled and light-controlled drug delivery systems[1,2,3]. The interactions between TEG-C NP’s and membrane lipids are pivotal to their function, but these interactions remain poorly understood. Here we use an ensemble-based, coarse-grained molecular dynamics (CG-MD) protocol to gather detailed, reproducible data on the structure and dynamics of TEG-C NP’s at two experimentally relevant ionic concentrations, allowing us to calculate reliable pore dimensions and perform comprehensive fluctuation analyses on membrane-spanning TEG-C NP’s, as well as TEG-C NP’s in free solution. Thus we can confidently characterise the influence of ionic concentration and membrane encapsulation on the dimensions, structural and mechanical properties of TEG-C NP’s, and pinpoint areas of constriction, strain and stability within their structure. Collecting ensembles of micro-second long trajectories of a membranespanning TEG-C NP allows us to observe a comprehensive spread of large-scale motions available to the TEG-C NP at these timescales and draw parallels with what is observed in experiment. Full Abstract

Water plays an intimate role in protein-ligand binding, not only through solvation/desolvation effects, but more subtly through the formation of direct interactions between the protein and ligand in the binding site. The targeting of bound water molecules for displacement as part of ligand optimization is a long invoked paradigm based around the release of configurational entropy, but there are many examples where displacing water leads to a loss in ligand binding affinity. Quantitatively accurate approaches to address this problem are arguable inadequate – water displacement and ligand interactions are intimately related and difficult to disentangle both experimentally and, hitherto, computationally. We have a long-standing interest in developing and using Grand Canonical Monte Carlo (GCMC) simulation approaches to explore water binding in protein-ligand systems. Through GCMC we are able to locate water molecules with good accuracy when compared against crystal structures. More significantly, the simulations clearly demonstrate the important role of water cooperativity; the mutual tabilization of water molecules means that individual water molecules cannot always be considered in isolation, but rather as part of a network. GCMC allows water binding sites and network binding free energies to be simultaneously calculated. In addition, by combining GCMC with alchemical perturbations of the ligand, networks of bound water molecules are able to adapt and maintain equilibrium with bulk water as the perturbation proceeds. Furthermore, the ability to extract active-site hydration free energies allows the deconvolution of protein-ligand binding free energies into separate protein- and water-mediated components, thereby providing rich, additional detail to the structure-activity relationship (SAR). In this presentation, the underlying methodology GCMC methodology will be described, together with examples of its application to water placement, binding free energy calculations, and protein-ligand affinity prediction Full Abstract

The incorporation of computational approaches into the early drug design process is a relatively young discipline compared to the long-standing history of drug discovery research. Considerable advances in hardware architecture, speed, accuracy and usability of computational algorithms have paved the way towards a quickly developing branch of research within the pharmaceutical industry. This talk will provide a general overview of computational molecular design in a pharmaceutical industry setting, highlight recent methodological advances and discuss their impact on real-world drug discovery projects.
Full Abstract

Drug-target residence time, the lifetime of the ligand-receptor complex, is said to be better than binding affinity at predicting in vivo efficacy. Computational prediction of drug-target residence time, using standard molecular dynamics (MD), is challenging as experimental dissociation times are approximately 107 longer than the simulation times that are currently feasible. We therefore applied steered MD (SMD) to forcibly speed up ligand dissociation. To ensure the interactions of the dissociating ligand with the receptor residues and water, Figure 1, are reproducible, ensemble analysis was performed. We applied this method to 17 ligands of a prototypical GPCR (G protein-coupled receptor), all of which had associated published experimental kinetic binding data. Our results reveal that the computationally-calculated change in ligand-water interaction energy correlates strongly with experimentally-determined residence time (R2 = 0.79). Further, the residues that interact with the dissociating ligand in these simulations are known experimentally to affect binding affinity and residence time. These experimental data indicate that our ensemble-based SMD protocol[1] is a novel, rapid and reproducible method for the rationalisation and determination of drug-target relative residence time. Full Abstract

Recent research has suggested retinoids may hold promise for therapeutic use in motor neuron diseases such as amyotrophic lateral sclerosis (ALS) by promotion of neuronal survival. Despite promising therapeutic potential, little is known about the complex signalling pathways which govern retinoid’s mechanism of action. Endogenous retinoids such as all-trans-retinoic acid (ATRA) are inherently vulnerable to photodegradation and isomerism due to their polyene structure, making their use as a research tool problematic. As such we utilise a range photostable, fluorescent synthetic retinoid analogues we are currently developing between Durham University and LightOx to investigate the retinoid mode of action. In parallel with biological testing, ligand docking and molecular dynamics (MD) simulations form a vital part of our continued research into these compounds. As part of our docking analysis, we have developed a root-mean-square deviation (RMSD)-based clustering script to group and identify commonly occurring ligand-docked protein structures, which we hypothesise will allow for enhanced identification of promising docked solutions via less resource-intensive methods before moving data to resource-intensive MD simulations. This workflow not only introduces a new approach for docking analysis but allows for faster and simpler identification of unique protein-ligand docked solutions. Full Abstract

Osteoporosis is a bone disease characterised by brittle bone and increased fracture incidence. The disease is globally a high burden on health systems which continues to increase with an aging society. There are limited treatments for osteoporosis with just two FDA approved pharmacological agents in the USA. Furthermore the drug discovery pipeline has limited success in producing novel and efficacious molecules. Osteoporosis arises due to changes in bone architecture, mineral density (BMD) and strength. These characteristics are believed to be affected by bio-mechanical stimulations. Such signals are sensed by bone cells in the bone remodelling unit and translated to cellular responses which ultimately maintain healthy bone. Recently, dual bio-mechanical stimulation with intermittent parathyroid hormone (PTH) treatment and mechanical stimulation were shown to increase BMD and bone formation in mice, thus suggesting a promising treatment for osteoporosis 1,2. However, the exact regimes to induce potent therapeutic effects are yet uncharacterised. This is partly due to incomplete understanding of cellular and molecular mechanisms which sense and integrate the dual signals into a cellular response (i.e. mechanotransduction) which evolve into increased BMD, strength and growth at the tissue level. Full Abstract

G-protein coupled receptors (GPCRs) constitute the most important drug target family and account for 30% of the FDA approved drugs1. This large family of receptors detect a remarkably diverse array of molecules outside the cell and initiate a variety of intracellular signalling pathways in response. The transmembrane nature and intrinsic flexibility of GPCRs makes their crystallization difficult. But a number of technical advances, aiming to rigidify the receptor have allowed their crystallization increasing the number of available structures. Despite this breakthrough in crystallography, which lead to the Nobel prize in chemistry to Lefkowitz and Kobilka in 20122,3, these structures are unlikely to cover the conformational diversity of this family of receptors and must be complemented with other techniques to reveal the intrinsic dynamics of the process. We are only starting to understand the role of ligand induced conformational changes (allostery) in GPCRs and there remains a great deal to be discovered in order to facilitate fundamental understanding of the role of allostery and the potential of new allosteric drugs4,5. Here we present a combination of state-of-the-art molecular dynamics enhanced sampling techniques and force fields to understand at an atomistic level how ligands and intracellular partners affect the energy and interconversion rates of GPCRs conformational repertoire. Our study is focused on a prototypical class A GPCR, the adenosine receptor A2a, which is relevant to the occurrence, development and treatment of brain ischemic damage and degenerative disorders, due to its role as neuronal and synaptic function modulator. Full Abstract

Drug design is fraught with challenges. The biological activity of a drug molecule can be considerably affected even with the minor changes in its structure. However, among the common substitutions in drug molecules that lead to adjusted pharmacokinetic and pharmacodynamic properties without affecting the biological activity is bioisosterism. Using the quantum theory of atoms in molecules (QTAIM), this study highlights a newly discovered indicator to evaluate quantitatively the similarities among nonclassical bioisosteres, namely carboxilyc acid, tetrazole [1], squarate [2], sulfonamide [3], isoxazole, oxadiazole, oxazolidinedione, and thiazolidinedione groups. The bioisosteric groups had remarkably close average electron densities regardless of the capping group or the protonation state of the molecule (see Figure below). The electrostatic potential maps, which represent the classical qualitative approach of explaining the bioisosteric activity, or in other words the “key & lock” interactions between the receptor and the drug, did not show the similarities in some cases. Full Abstract

Binding free energy calculations (BFE’s) are routinely used in drug design to accurately predict the binding free energy of small molecules to drug targets,1 however the cost of simulation often prohibits their application to smaller sets of molecules. Groups of molecules are typically compared through free energy maps, where each ligand may be compared to at least two other small molecules, however the decision process involved in the generation of this map is un-rigorous. Certain calculations between pairs of ligands or even a given atom-mapping protocol will converge faster than others, proportional to the thermodynamic length of the specific transformations. Using perturbations with large thermodynamic lengths is inefficient,2 however it is not possible to calculate thermodynamic length a priori, and can only be established post simulation.3 Typically, the generation of a ligand free energy map involves naïve reasoning over which pairs of ligands to compare, while the efficiency of the chosen pairings, and therefore the overall quality of the map is only apparent post simulation. Each perturbation is generally simulated using ‘equal allocation’ whereby the same length of simulation is used for each perturbation. Full Abstract

Protein misfolding is a fundamental disease process implicated in many human disorders, particularly dementias such as Alzheimer’s disease, but also in diabetes and specific types of heart and kidney failure. Proteins are the structural and functional workhouse molecules of the human body – beneficial activities that are dependent upon the protein being folded into a correct shape. Since protein shape is central to health, it is reasonable to postulate the existence of compounds endogenous to the human body which ameliorate the pathological consequences of protein misfolding; the concept of searching for endogenous anti-protein misfolding compounds is unique. We have used an in silico high throughput screen of small molecules endogenous in the human brain to identify multiple classes of agents capable of inhibiting the aberrant protein misfolding implicated in the pathogenesis of Alzheimer’s dementia. We then extend this discovery to show that these endogenous compounds also inhibit the misfolding of proteins implicated in diabetes, thereby demonstrating that these agents are not disease specific and are applicable to multiple classes of protein misfolding diseases, including two of the most significant disorders afflicting humankind, namely dementia and diabetes.Full Abstract

The human genome consists of approximately 3 billion base pairs, stored as nucleic acid sequences. Due to its vast complexity, the genome is fragile – unsurprisingly the DNA within is susceptible to change. The mutations that occur in these DNA sequences are crucial to both natural evolution and the occurrence of genetic diseases. While some of these changes might be a consequence of exposure to high energy electromagnetic fields or other forms of radiation, mutations may also arise due to mistakes during the DNA replication process.
Although remarkably accurate, the high-fidelity DNA replication process generates base substitution errors at a rate of 10-4 to 10-5 per replicated nucleotide. However, due to >various intrinsic repair mechanisms, errors in human genome replication are actually less frequent (approx. ~10-8 to 10-10 per replicated nucleotide). These replication errors, known as p>oint mutations, may occur as a result of wobble base pairing, Hoogsteen (anti-syn) base pairing, ionisation and tautomerisation (the frequency of each is uncertain). Full Abstract

Clinical beam accelerators are widely used in radiation therapy facilities to provide the adequate beams for treatment. The success of the treatment depends on the accuracy of the dose calculated and radiation administered. In this work we conduct a comprehensive modelling of the Varian Clinac 2300C/D from electron beam generation to target response, using GATE simulation toolkit. To validate our numerical model, Gamma Index parameter is used to compare the simulation results against the experimental measurements. Results from Percent Depth Dose and Dose Profile are presented and discussed. This Monte Carlo model and the accuracy of these results can be extended to accurately calculate the dose distribution in real treatment planning systems. Full Abstract

Tight junctions are cell-cell contact structures found in epithelial and endothelial tissues, located at the contact region between neighbouring cells, towards their apical side. They regulate the permeability of small molecules and ions through the intercellular space (paracellular pathway) by either blocking their passage or allowing some molecules with appropriate charge and size to go through. A functional tight junction barrier is critical to the physiology of the body. Its dysregulation can lead to pathologies such as inflammation, metastasis and edema [1]. For instance, mice that lack a key tight junction protein, claudin 1, die after birth due to excessive water loss across the skin. Tight junctions are also the target of several viruses including the hepatitis C virus and bacteria such as the bacterium Clostridium perfringens that produces the enterotoxin responsible for food poisoning. They are also targets of strategies for enhancing drug delivery of large molecules including proteins across the gastrointestinal tract. Further, there are numerous hereditary diseases that are linked with mutations of tight junction proteins, which include hypomagnesemia, deafness, neonatal sclerosing cholangitis with ichthyosis and familiar hypercholanemia.Full Abstract