Kinetics and Mechanism of Fentanyl Dissociation from the μ-Opioid Receptor

A putative binding model of nitazene derivatives at the μ-opioid receptor

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of μOR-opioid receptor (μOR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the μOR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of μOR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for μOR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a π-hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of μOR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.

Examining the Severity and Progression of Illicitly Manufactured Fentanyl Withdrawal: A Quasi-experimental Comparison

OBJECTIVE

Illicitly manufactured fentanyl has largely replaced heroin throughout the United States. Characteristics of fentanyl-specific withdrawal are not well understood compared to traditional opioid withdrawal. This study examines opioid withdrawal severity among 2 cohorts of study participants who underwent identical morphine stabilization procedures before and after fentanyl was introduced to the local drug market.

METHODS

The Non-Fentanyl study (n = 103) included participants testing positive for non-fentanyl opioids, and the Fentanyl study (n = 30) included participants testing positive for fentanyl. Both studies completed a 7-day morphine stabilization protocol (30 mg subcutaneous, 4 times daily) and multiple daily self-report and observer-rated assessments of opioid withdrawal and vital signs. Two-way repeated-measures analyses of variance (ANOVAs) examined the effects of study, time, and study × time on daily peak ratings for each outcome.

RESULTS

There were significant elevations in self-report and observer-rated withdrawal scores among the Fentanyl versus Non-Fentanyl study (study × time, P < 0.05) during stabilization days 2-5 and days 2-6, respectively. There was a higher rate of tachycardia among the Fentanyl group compared to the Non-Fentanyl study, and peak diastolic blood pressure was greater among the Fentanyl study compared to the Non-Fentanyl study.

CONCLUSIONS

Individuals with fentanyl exposure were less stabilized by morphine and experienced more severe opioid withdrawal via several metrics compared to persons with non-fentanyl opioid exposure. Withdrawal also remained elevated for several days despite morphine initiation. Adjustments to existing treatment induction protocols may be needed given the permeation of fentanyl into the heroin supply.

A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor

Nitazenes are a class of novel synthetic opioids with exceptionally high potency. Currently, an experimental structure of μOR-opioid receptor (μOR) in complex with a nitazene is lacking. Here we used a suite of computational tools, including consensus docking, conventional molecular dynamics (MD) and metadynamics simulations, to investigate the μOR binding modes of nitro-containing meto-, eto-, proto-, buto-, and isotonitazenes and nitro-less analogs, metodes-, etodes-, and protodesnitazenes. Docking generated three binding modes, whereby the nitro-substituted or unsubstituted benzimidazole group extends into SP1 (subpocket 1 between transmembrane helix or TM 2 and 3), SP2 (subpocket 2 between TM1, TM2, and TM7) or SP3 (subpocket 3 between TM5 and TM6). Simulations suggest that etonitazene and likely also other nitazenes favor the SP2-binding mode. Comparison to the experimental structures of μOR in complex with BU72, fentanyl, and mitragynine pseudoindoxyl (MP) allows us to propose a putative model for μOR-ligand recognition in which ligand can access hydrophobic SP1 or hydrophilic SP2, mediated by the conformational change of Gln1242.60. Interestingly, in addition to water-mediated hydrogen bonds, the nitro group in nitazenes forms a π-hole interaction with the conserved Tyr751.39. Our computational analysis provides new insights into the mechanism of μOR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.

Mechanism of negative μ-opioid receptor modulation by sodium ions

Negative allosteric modulation of G-protein coupled receptors (GPCRs) by Na+ ions was first described in the 1970s for opioid receptors (ORs) and has subsequently been detected for most class A GPCRs. In high-resolution structures of inactive-state class A GPCRs, a Na+ ion binds to a conserved pocket near residue D2.50, whereas active-state structures of GPCRs are incompatible with Na+ binding. Correspondingly, Na+ diminishes agonist affinity, stabilizes the receptors in the inactive state, and reduces basal signaling. We applied a mutual-information based analysis to μs-timescale biomolecular simulations of the μ-opioid receptor (μ-OR). Our results reveal that Na+ binding is coupled to a water wire linking the Na+ binding site with the agonist binding pocket and to rearrangements in polar networks propagating conformational changes to the agonist and G-protein binding sites. These findings provide a new mechanistic link between the presence of the ion, altered agonist affinity, receptor deactivation, and lowered basal signaling levels.

Effects of pH on opioid receptor activation and implications for drug design

G-protein-coupled receptors are integral membrane proteins that transduce chemical signals from the extracellular matrix into the cell. Traditional drug design has considered ligand-receptor interactions only under normal conditions. However, studies on opioids indicate that such interactions are very different in diseased tissues. In such microenvironments, protons play an important role in structural and functional alterations of both ligands and receptors. The pertinent literature strongly suggests that future drug design should take these aspects into account in order to reduce adverse side effects while preserving desired effects of novel compounds.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Characterization of binding kinetics and intracellular signaling of new psychoactive substances targeting cannabinoid receptor using transition-based reweighting method

New psychoactive substances (NPS) targeting cannabinoid receptor 1 pose a significant threat to society as recreational abusive drugs that have pronounced physiological side effects. These greater adverse effects compared to classical cannabinoids have been linked to the higher downstream β-arrestin signaling. Thus, understanding the mechanism of differential signaling will reveal important structure-activity relationship essential for identifying and potentially regulating NPS molecules. In this study, we simulate the slow (un)binding process of NPS MDMB-Fubinaca and classical cannabinoid HU-210 from CB1 using multi-ensemble simulation to decipher the effects of ligand binding dynamics on downstream signaling. The transition-based reweighing method is used for the estimation of transition rates and underlying thermodynamics of (un)binding processes of ligands with nanomolar affinities. Our analyses reveal major interaction differences with transmembrane TM7 between NPS and classical cannabinoids. A variational autoencoder-based approach, neural relational inference (NRI), is applied to assess the allosteric effects on intracellular regions attributable to variations in binding pocket interactions. NRI analysis indicate a heightened level of allosteric control of NPxxY motif for NPS-bound receptors, which contributes to the higher probability of formation of a crucial triad interaction (Y7.53-Y5.58-T3.46) necessary for stronger β-arrestin signaling. Hence, in this work, MD simulation, data-driven statistical methods, and deep learning point out the structural basis for the heightened physiological side effects associated with NPS, contributing to efforts aimed at mitigating their public health impact.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Development of Photoswitchable Tethered Ligands that Target the μ-Opioid Receptor

Converting known ligands into photoswitchable derivatives offers the opportunity to modulate compound structure with light and hence, biological activity. In doing so, these probes provide unique control when evaluating G-protein-coupled receptor (GPCR) mechanism and function. Further conversion of such compounds into covalent probes, known as photoswitchable tethered ligands (PTLs), offers additional advantages. These include localization of the PTLs to the receptor binding pocket. Covalent localization increases local ligand concentration, improves site selectivity and may improve the biological differences between the respective isomers. This work describes chemical, photophysical and biochemical characterizations of a variety of PTLs designed to target the μ-opioid receptor (μOR). These PTLs were modeled on fentanyl, with the lead disulfide-containing agonist found to covalently interact with a cysteine-enriched mutant of this medically-relevant receptor.

Molecular basis of opioid receptor signaling

Opioids are used for pain management despite the side effects that contribute to the opioid crisis. The pursuit of non-addictive opioid analgesics remains unattained due to the unresolved intricacies of opioid actions, receptor signaling cascades, and neuronal plasticity. Advancements in structural, molecular, and computational tools illuminate the dynamic interplay between opioids and opioid receptors, as well as the molecular determinants of signaling pathways, which are potentially interlinked with pharmacological responses. Here, we review the molecular basis of opioid receptor signaling with a focus on the structures of opioid receptors bound to endogenous peptides or pharmacological agents. These insights unveil specific interactions that dictate ligand selectivity and likely their distinctive pharmacological profiles. Biochemical analysis further unveils molecular features governing opioid receptor signaling. Simultaneously, the synergy between computational biology and medicinal chemistry continues to expedite the discovery of novel chemotypes with the promise of yielding more efficacious and safer opioid compounds.

Goldilocks Energy Minimum: Peptide-Based Reversible Aggregation and Biosensing

Colorimetric biosensors based on gold nanoparticle (AuNP) aggregation are often challenged by matrix interference in biofluids, poor specificity, and limited utility with clinical samples. Here, we propose a peptide-driven nanoscale disassembly approach, where AuNP aggregates induced by electrostatic attractions are dissociated in response to proteolytic cleavage. Initially, citrate-coated AuNPs were assembled via a short cationic peptide (RRK) and characterized by experiments and simulations. The dissociation peptides were then used to reversibly dissociate the AuNP aggregates as a function of target protease detection, i.e., main protease (Mpro), a biomarker for severe acute respiratory syndrome coronavirus 2. The dissociation propensity depends on peptide length, hydrophilicity, charge, and ligand architecture. Finally, our dissociation strategy provides a rapid and distinct optical signal through Mpro cleavage with a detection limit of 12.3 nM in saliva. Our dissociation peptide effectively dissociates plasmonic assemblies in diverse matrices including 100% human saliva, urine, plasma, and seawater, as well as other types of plasmonic nanoparticles such as silver. Our peptide-enabled dissociation platform provides a simple, matrix-insensitive, and versatile method for protease sensing.

Kinetics from Metadynamics: Principles, Applications, and Outlook

Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.

The PIWI-specific insertion module helps load longer piRNAs for translational activation essential for male fertility

PIWI-clade proteins harness piRNAs of 24-33 nt in length. Of great puzzles are how PIWI-clade proteins incorporate piRNAs of different sizes and whether the size matters to PIWI/piRNA function. Here we report that a PIWI-Ins module unique in PIWI-clade proteins helps define the length of piRNAs. Deletion of PIWI-Ins in Miwi shifts MIWI to load with shorter piRNAs and causes spermiogenic failure in mice, demonstrating the functional importance of this regulatory module. Mechanistically, we show that longer piRNAs provide additional complementarity to target mRNAs, thereby enhancing the assembly of the MIWI/eIF3f/HuR super-complex for translational activation. Importantly, we identify a c.1108C>T (p.R370W) mutation of HIWI (human PIWIL1) in infertile men and demonstrate in Miwi knock-in mice that this genetic mutation impairs male fertility by altering the property of PIWI-Ins in selecting longer piRNAs. These findings reveal a critical role of PIWI-Ins-ensured longer piRNAs in fine-tuning MIWI/piRNA targeting capacity, proven essential for spermatid development and male fertility.

Binding preference at the μ-opioid receptor underlies distinct pharmacology of cyclopropyl versus valeryl analogs of fentanyl

Illicitly manufactured fentanyl is driving the current opioid crisis, and various fentanyl analogs are appearing in recreational drug markets worldwide. To assess the potential health risks posed by fentanyl analogs, it is necessary to understand structure-activity relationships for these compounds. Here we compared the pharmacology of two structurally related fentanyl analogs implicated in opioid overdose: cyclopropylfentanyl and valerylfentanyl. Cyclopropylfentanyl has a three-carbon ring attached to the carbonyl group on the fentanyl scaffold, whereas valerylfentanyl has a four-carbon chain at the same position. In vitro assays examining μ-opioid receptor (MOR) coupling to G proteins in CHO cells showed that cyclopropylfentanyl is a full agonist (EC50 = 8.6 nM, %Emax = 113%), with potency and efficacy similar to fentanyl (EC50 = 10.3 nM, %Emax = 113%). By contrast, valerylfentanyl is a partial agonist at MOR (EC50 = 179.8 nM, %Emax = 60%). Similar results were found in assays assessing MOR-mediated β-arrestin recruitment in HEK cells. In vivo studies in male CD-1 mice demonstrated that both fentanyl analogs induce naloxone-reversible antinociception and respiratory suppression, but cyclopropylfentanyl is 100-times more potent as an antinociceptive agent (ED50 = 0.04 mg/kg, s. c.) than valerylfentanyl (ED50 = 4.0 mg/kg, s. c.). Molecular simulation results revealed that the alkyl chain of valerylfentanyl cannot be well accommodated by the active state of MOR and may transition the receptor toward an inactive state, converting the fentanyl scaffold to a partial agonist. Taken together, our results suggest that cyclopropylfentanyl presents much greater risk of adverse effects when compared to valerylfentanyl. Moreover, the summed findings may provide clues to the design of therapeutic opioids with reduced adverse side effects.

Insights into distinct signaling profiles of the µOR activated by diverse agonists

Drugs targeting the μ-opioid receptor (μOR) are the most effective analgesics available but are also associated with fatal respiratory depression through a pathway that remains unclear. Here we investigated the mechanistic basis of action of lofentanil (LFT) and mitragynine pseudoindoxyl (MP), two μOR agonists with different safety profiles. LFT, one of the most lethal opioids, and MP, a kratom plant derivative with reduced respiratory depression in animal studies, exhibited markedly different efficacy profiles for G protein subtype activation and β-arrestin recruitment. Cryo-EM structures of μOR-Gi1 complex with MP (2.5 Å) and LFT (3.2 Å) revealed that the two ligands engage distinct subpockets, and molecular dynamics simulations showed additional differences in the binding site that promote distinct active-state conformations on the intracellular side of the receptor where G proteins and β-arrestins bind. These observations highlight how drugs engaging different parts of the μOR orthosteric pocket can lead to distinct signaling outcomes.

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning Analysis

The nation's opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is a FDA-approved reversal agent, antagonizes opioids through competitive binding at the μ-opioid receptor (mOR). Thus, knowledge of the opioid's residence time is important for assessing the effectiveness of naloxone. Here, we estimated the residence times (τ) of 15 fentanyl and 4 morphine analogs using metadynamics and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann et al. Clin. Pharmacol. Therapeut. 2022, 120, 1020-1232). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning approach to analyze the kinetic impact of fentanyl's substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computer-aided drug discovery.

Endogenous opiates and behavior: 2021

This paper is the forty-fourth consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2021 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonizts and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

New science, drug regulation, and emergent public health issues: The work of FDA's division of applied regulatory science

The U.S. Food and Drug Administration (FDA) Division of Applied Regulatory Science (DARS) moves new science into the drug review process and addresses emergent regulatory and public health questions for the Agency. By forming interdisciplinary teams, DARS conducts mission-critical research to provide answers to scientific questions and solutions to regulatory challenges. Staffed by experts across the translational research spectrum, DARS forms synergies by pulling together scientists and experts from diverse backgrounds to collaborate in tackling some of the most complex challenges facing FDA. This includes (but is not limited to) assessing the systemic absorption of sunscreens, evaluating whether certain drugs can convert to carcinogens in people, studying drug interactions with opioids, optimizing opioid antagonist dosing in community settings, removing barriers to biosimilar and generic drug development, and advancing therapeutic development for rare diseases. FDA tasks DARS with wide ranging issues that encompass regulatory science; DARS, in turn, helps the Agency solve these challenges. The impact of DARS research is felt by patients, the pharmaceutical industry, and fellow regulators. This article reviews applied research projects and initiatives led by DARS and conducts a deeper dive into select examples illustrating the impactful work of the Division.

Modelling altered signalling of G-protein coupled receptors in inflamed environment to advance drug design

We previously reported the successful design, synthesis and testing of the prototype opioid painkiller NFEPP that does not elicit adverse side effects. The design process of NFEPP was based on mathematical modelling of extracellular interactions between G-protein coupled receptors (GPCRs) and ligands, recognizing that GPCRs function differently under pathological versus healthy conditions. We now present an additional and novel stochastic model of GPCR function that includes intracellular dissociation of G-protein subunits and modulation of plasma membrane calcium channels and their dependence on parameters of inflamed and healthy tissue (pH, radicals). The model is validated against in vitro experimental data for the ligands NFEPP and fentanyl at different pH values and radical concentrations. We observe markedly reduced binding affinity and calcium channel inhibition for NFEPP at normal pH compared to lower pH, in contrast to the effect of fentanyl. For increasing radical concentrations, we find enhanced constitutive G-protein activation but reduced ligand binding affinity. Assessing the different effects, the results suggest that, compared to radicals, low pH is a more important determinant of overall GPCR function in an inflamed environment. Future drug design efforts should take this into account.

Metadynamics simulations leveraged by statistical analyses and artificial intelligence-based tools to inform the discovery of G protein-coupled receptor ligands

G Protein-Coupled Receptors (GPCRs) are a large family of membrane proteins with pluridimensional signaling profiles. They undergo ligand-specific conformational changes, which in turn lead to the differential activation of intracellular signaling proteins and the consequent triggering of a variety of biological responses. This conformational plasticity directly impacts our understanding of GPCR signaling and therapeutic implications, as do ligand-specific kinetic differences in GPCR-induced transducer activation/coupling or GPCR-transducer complex stability. High-resolution experimental structures of ligand-bound GPCRs in the presence or absence of interacting transducers provide important, yet limited, insights into the highly dynamic process of ligand-induced activation or inhibition of these receptors. We and others have complemented these studies with computational strategies aimed at characterizing increasingly accurate metastable conformations of GPCRs using a combination of metadynamics simulations, state-of-the-art algorithms for statistical analyses of simulation data, and artificial intelligence-based tools. This minireview provides an overview of these approaches as well as lessons learned from them towards the identification of conformational states that may be difficult or even impossible to characterize experimentally and yet important to discover new GPCR ligands.

GPU-Accelerated All-Atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber

Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH conditions; however, the current accuracy is limited due to the use of implicit-solvent models or a hybrid solvent scheme. Here, we report the first GPU-accelerated implementation, parametrization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Amber22 pmemd.cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replica-exchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pK_a's of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearson's correlation coefficient for the pK_a shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pK_a's and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pK_a upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pK_a downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD will offer proper pH control to improve the accuracies of MD simulations and enable mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.

A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0]

Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.

Recent Molecular Insights into Agonist-specific Binding to the Mu-Opioid Receptor

Opioid agonists produce their analgesic effects primarily by acting at the µ-opioid receptor (µOR). µOR agonists with different efficacies exert diverse molecular changes in the µOR which dictate the faith of the receptor’s signaling pathway and possibly it’s the degree of desensitization. Since the development of the active conformations of the µOR, growing data have been published in relation to ligand-specific changes in µOR activation. In this regard, this review summarizes recent data regarding the most studied opioid agonists in in silico µOR activation, including how these ligands are recognized by the µOR, how their binding signal is transmitted toward the intracellular parts of the µOR, and finally, what type of large-scale movements do these changes trigger in the µOR’s domains.

Fentanyl Structure as a Scaffold for Opioid/Non-Opioid Multitarget Analgesics

One of the strategies in the search for safe and effective analgesic drugs is the design of multitarget analgesics. Such compounds are intended to have high affinity and activity at more than one molecular target involved in pain modulation. In the present contribution we summarize the attempts in which fentanyl or its substructures were used as a μ-opioid receptor pharmacophoric fragment and a scaffold to which fragments related to non-opioid receptors were attached. The non-opioid ‘second’ targets included proteins as diverse as imidazoline I₂ binding sites, CB₁ cannabinoid receptor, NK₁ tachykinin receptor, D₂ dopamine receptor, cyclooxygenases, fatty acid amide hydrolase and monoacylglycerol lipase and σ₁ receptor. Reviewing the individual attempts, we outline the chemistry, the obtained pharmacological properties and structure-activity relationships. Finally, we discuss the possible directions for future work.