Markov State Model Analysis of Haloperidol Binding to the D3 Dopamine Receptor

Dissecting Large-Scale Structural Transitions in Membrane Transporters Using Advanced Simulation Technologies

Membrane transporters are integral membrane proteins that act as gatekeepers of the cell, controlling fundamental processes such as recruitment of nutrients and expulsion of waste material. At a basic level, transporters operate using the "alternating access model," in which transported substances are accessible from only one side of the membrane at a time. This model usually involves large-scale structural changes in the transporter, which often cannot be captured using unbiased, conventional molecular simulation techniques. In this article, we provide an overview of some of the major simulation techniques that have been applied to characterize the structural dynamics and energetics involved in the transition of membrane transporters between their functional states. After briefly introducing each technique, we discuss some of their advantages and limitations and provide some recent examples of their application to membrane transporters.

Computational Analysis of the Fully Activated Orexin Receptor 2 across Various Thermodynamic Ensembles with Surface Tension Monitoring and Markov State Modeling

In this study, we investigated the stability of the fully activated conformation of the orexin receptor 2 (OX2R) embedded in a pure POPC bilayer using MD simulations. Various thermodynamic ensembles (i.e., NPT, NVT, NVE, NPAT, μVT, and NPγT) were employed to explore the dynamical heterogeneity of the system in a comprehensive way. In addition, informational similarity metrics (e.g., Jensen-Shannon divergence) as well as Markov state modeling approaches were utilized to elucidate the receptor kinetics. Special attention was paid to assessing surface tension within the simulation box, particularly under NPγT conditions, where 21 nominal surface tension constants were evaluated. Our findings suggest that traditional thermodynamic ensembles such as NPT may not adequately control physical properties of the POPC membrane, impacting the plausibility of the OX2R model. In general, the performed study underscores the importance of employing the NPγT ensemble for computational investigations of membrane-embedded receptors, as it effectively maintains zero surface tension in the simulated system. These results offer valuable insights for future research aimed at understanding receptor dynamics and designing targeted therapeutics.

Optimisation of pharmacotherapy in psychiatry through therapeutic drug monitoring, molecular brain imaging and pharmacogenetic tests: Focus on antipsychotics

BACKGROUND

For psychotic disorders (i.e. schizophrenia), pharmacotherapy plays a key role in controlling acute and long-term symptoms. To find the optimal individual dose and dosage strategy, specialised tools are used. Three tools have been proven useful to personalise drug treatments: therapeutic drug monitoring (TDM) of drug levels, pharmacogenetic testing (PG), and molecular neuroimaging.

METHODS

In these Guidelines, we provide an in-depth review of pharmacokinetics, pharmacodynamics, and pharmacogenetics for 45 antipsychotics. Over 30 international experts in psychiatry selected studies that have measured drug concentrations in the blood (TDM), gene polymorphisms of enzymes involved in drug metabolism, or receptor/transporter occupancies in the brain (positron emission tomography (PET)).

RESULTS

Study results strongly support the use of TDM and the cytochrome P450 (CYP) genotyping and/or phenotyping to guide drug therapies. Evidence-based target ranges are available for titrating drug doses that are often supported by PET findings.

CONCLUSION

All three tools discussed in these Guidelines are essential for drug treatment. TDM goes well beyond typical indications such as unclear compliance and polypharmacy. Despite its enormous potential to optimise treatment effects, minimise side effects and ultimately reduce the global burden of diseases, personalised drug treatment has not yet become the standard of care in psychiatry.

Ligand entry pathways control the chemical space recognized by GPR183

The G protein-coupled receptor GPR183 is a chemotactic receptor with an important function in the immune system and association with a variety of diseases. It recognizes ligands with diverse physicochemical properties as both the endogenous oxysterol ligand 7α,25-OHC and synthetic molecules can activate the G protein pathway of the receptor. To better understand the ligand promiscuity of GPR183, we utilized both molecular dynamics simulations and cell-based validation experiments. Our work reveals that the receptor possesses two ligand entry channels: one lateral between transmembrane helices 4 and 5 facing the membrane, and one facing the extracellular environment. Using enhanced sampling, we provide a detailed structural model of 7α,25-OHC entry through the lateral membrane channel. Importantly, the first ligand recognition point at the receptor surface has been captured in diverse experimentally solved structures of different GPCRs. The proposed ligand binding pathway is supported by in vitro data employing GPR183 mutants with a sterically blocked lateral entrance, which display diminished binding and signaling. In addition, computer simulations and experimental validation confirm the existence of a polar water channel which might serve as an alternative entrance gate for less lipophilic ligands from the extracellular milieu. Our study reveals knowledge to understand GPR183 functionality and ligand recognition with implications for the development of drugs for this receptor. Beyond, our work provides insights into a general mechanism GPCRs may use to respond to chemically diverse ligands.

Modeling Ensembles of Enzyme Reaction Pathways with Hi-MSM Reveals the Importance of Accounting for Pathway Diversity

Conventional quantum mechanical-molecular mechanics (QM/MM) simulation approaches for modeling enzyme reactions often assume that there is one dominant reaction pathway and that this pathway can be sampled starting from an X-ray structure of the enzyme. These assumptions reduce computational cost; however, their validity has not been extensively tested. This is due in part to the lack of a rigorous formalism for integrating disparate pathway information from dynamical QM/MM calculations. Here, we present a way to model ensembles of reaction pathways efficiently using a divide-and-conquer strategy through Hierarchical Markov State Modeling (Hi-MSM). This approach allows information on multiple, distinct pathways to be incorporated into a chemical kinetic model, and it allows us to test these two assumptions. Applying Hi-MSM to the reaction carried out by dihydrofolate reductase (DHFR) we find (i) there are multiple, distinct pathways significantly contributing to the overall flux of the reaction that the conventional approach does not identify and (ii) that the conventional approach does not identify the dominant reaction pathway. Thus, both assumptions underpinning the conventional approach are violated. Since DHFR is a relatively small enzyme, and configuration space scales exponentially with protein size, accounting for multiple reaction pathways is likely to be necessary for most enzymes.

The prediction of protein-ligand unbinding for modern drug discovery

INTRODUCTION

Drug-target thermodynamic and kinetic information have perennially important roles in drug design. The prediction of protein-ligand unbinding, which can provide important kinetic information, in experiments continues to face great challenges. Uncovering protein-ligand unbinding through molecular dynamics simulations has become efficient and inexpensive with the progress and enhancement of computing power and sampling methods.

AREAS COVERED

In this review, various sampling methods for protein-ligand unbinding and their basic principles are firstly briefly introduced. Then, their applications in predicting aspects of protein-ligand unbinding, including unbinding pathways, dissociation rate constants, residence time and binding affinity, are discussed.

EXPERT OPINION

Although various sampling methods have been successfully applied in numerous systems, they still have shortcomings and deficiencies. Most enhanced sampling methods require researchers to possess a wealth of prior knowledge of collective variables or reaction coordinates. In addition, most systems studied at present are relatively simple, and the study of complex systems in real drug research remains greatly challenging. Through the combination of machine learning and enhanced sampling methods, prediction accuracy can be further improved, and some problems encountered in complex systems also may be solved.

Nonparametric Analysis of Nonequilibrium Simulations

We extend the nonparametric framework of reaction coordinate optimization to nonequilibrium ensembles of (short) trajectories. For example, we show how, starting from such an ensemble, one can obtain an equilibrium free-energy profile along the committor, which can be used to determine important properties of the dynamics exactly. A new adaptive sampling approach, the transition-state ensemble enrichment, is suggested, which samples the configuration space by "growing" committor segments toward each other starting from the boundary states. This framework is suggested as a general tool, alternative to the Markov state models, for a rigorous and accurate analysis of simulations of large biomolecular systems, as it has the following attractive properties. It is immune to the curse of dimensionality, does not require system-specific information, can approximate arbitrary reaction coordinates with high accuracy, and has sensitive and rigorous criteria to test optimality and convergence. The approaches are illustrated on a 50-dimensional model system and a realistic protein folding trajectory.