Pathway and mechanism of drug binding to G-protein-coupled receptors

Unbiased MD simulations identify lipid binding sites in lipid transfer proteins

The characterization of lipid binding to lipid transfer proteins (LTPs) is fundamental to understand their molecular mechanism. However, several structures of LTPs, and notably those proposed to act as bridges between membranes, do not provide the precise location of their endogenous lipid ligands. To address this limitation, computational approaches are a powerful alternative methodology, but they are often limited by the high flexibility of lipid substrates. Here, we develop a protocol based on unbiased coarse-grain molecular dynamics simulations in which lipids placed away from the protein can spontaneously bind to LTPs. This approach accurately determines binding pockets in LTPs and provides a working hypothesis for the lipid entry pathway. We apply this approach to characterize lipid binding to bridge LTPs of the Vps13-Atg2 family, for which the lipid localization inside the protein is currently unknown. Overall, our work paves the way to determine binding pockets and entry pathways for several LTPs in an inexpensive, fast, and accurate manner.

The translational value of ligand-receptor binding kinetics in drug discovery

The translation of in vitro potency of a candidate drug, as determined by traditional pharmacology metrics (such as EC50/IC50 and KD/Ki values), to in vivo efficacy and safety is challenging. Residence time, which represents the duration of drug-target interaction, can be part of a more comprehensive understanding of the dynamic nature of drug-target interactions in vivo, thereby enabling better prediction of drug efficacy and safety. As a consequence, a prolonged residence time may help in achieving sustained pharmacological activity, while transient interactions with shorter residence times may be favourable for targets associated with side effects. Therefore, integration of residence time into the early stages of drug discovery and development has yielded a number of clinical candidates with promising in vivo efficacy and safety profiles. Insights from residence time research thus contribute to the translation of in vitro potency to in vivo efficacy and safety. Further research and advances in measuring and optimizing residence time will bring a much-needed addition to the drug discovery process and the development of safer and more effective drugs. In this review, we summarize recent research progress on residence time, highlighting its importance from a translational perspective.

Secondary Binding Site of CYP17A1 in Enhanced Sampling Simulations

Androgens like testosterone and dihydrotestosterone play a key role in prostate cancer progression, making the enzyme CYP17A1, essential for androgen synthesis, a crucial therapeutic target. Recent studies have revealed electron density at the substrate entry channel, suggesting the presence of a secondary binding site. In this study, we calculated the binding free energy landscape of known ligands at this site using Funnel Metadynamics. Our results characterize this binding site and indicate that nonheme-interacting ligands could effectively bind to CYP17A1, providing a novel approach to the design of CYP17A1 inhibitors.

Homodimerization of CB2 cannabinoid receptor triggered by a bivalent ligand enhances cellular signaling

G protein-coupled receptors (GPCRs) exist within a landscape of interconvertible conformational states and in dynamic equilibrium between monomers and higher-order oligomers, both influenced by ligand binding. Here, we show that a homobivalent ligand formed by equal chromenopyrazole moieties as pharmacophores, connected by 14 methylene units, can modulate the dynamics of the cannabinoid CB2 receptor (CB2R) homodimerization by simultaneously binding both protomers of the CB2R-CB2R homodimer. Computational and pharmacological experiments showed that one of the ligand pharmacophores binds to the orthosteric site of one protomer, and the other pharmacophore to a membrane-oriented pocket between transmembranes 1 and 7 of the partner protomer. This results in unique pharmacological properties, including increased potency in Gi-mediated signaling and enhanced recruitment of β-arrestin. Thus, by modulating dimerization dynamics, it may be possible to fine-tune CB2R activity, potentially leading to improved therapeutic outcomes.

Molecular glues as potential GPCR therapeutics

"Molecular Glues" are defined as small molecules that can either be endogenous or synthetic which promote interactions between proteins at their interface. Allosteric modulators, specifically GPCR allosteric modulators, can promote both the association and the dissociation of a given receptor's transducer but accomplishes this "at a distance" from the interface. However, recent structures of GPCR G protein complexes in the presence of allosteric modulators indicate that some GPCR allosteric modulators can act as "molecular glues" interacting with both the receptor and the transducer at the interface biasing transducer signaling in both a positive and negative manner depending on the transducer. Given these phenomena we discuss the implications for this class of allosteric modulators to be used as molecular tools and for future drug development.

Metadynamics Study of Lipid-Mediated Antibacterial Toxin Binding to the EmrE Multiefflux Protein

EmrE is a bacterial efflux protein in the small multidrug-resistant (SMR) family present in Escherichia coli. Due to its small size, 110 residues in each dimer subunit, it is an ideal model system to study ligand-protein-membrane interactions. Here in our work, we have calculated the free energy landscape of benzyltrimetylammonium (BTMA) and tetraphenyl phosphonium (TPP) binding to EmrE using the enhanced sampling method-multiple walker metadynamics. We estimate that the free energy of BTMA binding to EmrE is -21.2 ± 3.3 kJ/mol and for TPP is -43.6 ± 3.8 kJ/mol. BTMA passes through two metastable states to reach the binding pocket, while TPP has a more complex binding landscape with four metastable states and one main binding site. Our simulations show that the ligands interact with the membrane lipids at a distance 1 nm away from the binding site which forms a broad local minimum, consistent for both BTMA and TPP. This site can be an alternate entry point for ligands to partition from the membrane into the protein, especially for bulky and/or branched ligands. We also observed the membrane lipid and C-terminal 110HisA form salt-bridge interactions with the helix-1 residue 22LysB. Our free energy estimates and clusters are in close agreement with experimental data and give us an atomistic view of the ligand-protein-lipid interactions. Understanding the binding pathway of these ligands can guide us in future design of ligands that can alter or halt the function of EmrE.

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

Structural basis for the ligand recognition and G protein subtype selectivity of kisspeptin receptor

Kisspeptin receptor (KISS1R), belonging to the class A peptide-GPCR family, plays a key role in the regulation of reproductive physiology after stimulation by kisspeptin and is regarded as an attractive drug target for reproductive diseases. Here, we demonstrated that KISS1R can couple to the Gi/o pathway besides the well-known Gq/11 pathway. We further resolved the cryo-electron microscopy (cryo-EM) structure of KISS1R-Gq and KISS1R-Gi complexes bound to the synthetic agonist TAK448 and structure of KISS1R-Gq complex bound to the endogenous agonist KP54. The high-resolution structures provided clear insights into mechanism of KISS1R recognition by its ligand and can facilitate the design of targeted drugs with high affinity to improve treatment effects. Moreover, the structural and functional analyses indicated that conformational differences in the extracellular loops (ECLs), intracellular loops (ICLs) of the receptor, and the "wavy hook" of the Gα subunit may account for the specificity of G protein coupling for KISS1R signaling.

Pathways and mechanism of MRTX1133 binding to KRAS G12D elucidated by molecular dynamics simulations and Markov state models

KRAS G12D is the most common oncogenic mutation identified in several types of cancer. Therefore, design of inhibitors targeting KRAS G12D represents a promising strategy for anticancer therapy. MRTX1133 is a highly potent inhibitor (approximate experiment Kd ≈ 0.0002 nM) of KRAS G12D and is currently in Phase 1/2 study, however, pathways of the compound binding to KRAS G12D has remained unknown, and the mechanism underlying the complicated dynamic process are challenging to capture experimentally, which hinder the structure-based anti-cancer drug design. Here, using MRTX1133 as a probe, unbiased molecular dynamics (MD) was used to simulate the process of MRTX1133 spontaneously binding to KRAS G12D. In six of 42 independent MD simulation (a total of 99 μs), MRTX1133 was observed to successfully associate with KRAS G12D. The kinetically metastable states refer to the potential pathways of MRTX1133 binding to KRAS G12D were revealed by Markov state models (MSM) analysis. Additionally, 8 key residues that are essential for MRTX1133 recognition and tight binding at the preferred low energy states were identified by MM/GBSA analysis. In sum, this study provides a new perspective on understanding the pathways and mechanism of MRTX1133 binding to KRAS G12D.

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites

Because most proteins have buried active sites, protein tunnels or channels play a crucial role in the transport of small molecules into buried cavities for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, a recent example being energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasted the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a nontrivial system of a haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The most knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting drug development efforts and the rational design of enzymes with buried active sites.

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Bitopic Ligands Support the Presence of a Metastable Binding Site at the β2 Adrenergic Receptor

Metastable binding sites (MBS) have been observed in a multitude of molecular dynamics simulations and can be considered low affinity allosteric binding sites (ABS) that function as stepping stones as the ligand moves toward the orthosteric binding site (OBS). Herein, we show that MBS can be utilized as ABS in ligand design, resulting in ligands with improved binding kinetics. Four homobivalent bitopic ligands (1-4) were designed by molecular docking of (S)-alprenolol ((S)-ALP) in the cocrystal structure of the β2 adrenergic receptor (β2AR) bound to the antagonist ALP. Ligand 4 displayed a potency and affinity similar to (S)-ALP, but with a >4-fold increase in residence time. The proposed binding mode was confirmed by X-ray crystallography of ligand 4 in complex with the β2AR. This ligand design principle can find applications beyond the β2AR and G protein-coupled receptors (GPCRs) as a general approach for improving the pharmacological profile of orthosteric ligands by targeting the OBS and an MBS simultaneously.

Activation of Polycystin-1 Signaling by Binding of Stalk-derived Peptide Agonists

Polycystin-1 (PC1) is the membrane protein product of the PKD1 gene whose mutation is responsible for 85% of the cases of autosomal dominant polycystic kidney disease (ADPKD). ADPKD is primarily characterized by the formation of renal cysts and potential kidney failure. PC1 is an atypical G protein-coupled receptor (GPCR) consisting of 11 transmembrane helices and an autocatalytic GAIN domain that cleaves PC1 into extracellular N-terminal (NTF) and membrane-embedded C-terminal (CTF) fragments. Recently, signaling activation of the PC1 CTF was shown to be regulated by a stalk tethered agonist (TA), a distinct mechanism observed in the adhesion GPCR family. A novel allosteric activation pathway was elucidated for the PC1 CTF through a combination of Gaussian accelerated molecular dynamics (GaMD), mutagenesis and cellular signaling experiments. Here, we show that synthetic, soluble peptides with 7 to 21 residues derived from the stalk TA, in particular, peptides including the first 9 residues (p9), 17 residues (p17) and 21 residues (p21) exhibited the ability to re-activate signaling by a stalkless PC1 CTF mutant in cellular assays. To reveal molecular mechanisms of stalk peptide-mediated signaling activation, we have applied a novel Peptide GaMD (Pep-GaMD) algorithm to elucidate binding conformations of selected stalk peptide agonists p9, p17 and p21 to the stalkless PC1 CTF. The simulations revealed multiple specific binding regions of the stalk peptide agonists to the PC1 protein including an "intermediate" bound yet inactive state. Our Pep-GaMD simulation findings were consistent with the cellular assay experimental data. Binding of peptide agonists to the TOP domain of PC1 induced close TOP-putative pore loop interactions, a characteristic feature of the PC1 CTF signaling activation mechanism. Using sequence covariation analysis of PC1 homologs, we further showed that the peptide binding regions were consistent with covarying residue pairs identified between the TOP domain and the stalk TA. Therefore, structural dynamic insights into the mechanisms of PC1 activation by stalk-derived peptide agonists have enabled an in-depth understanding of PC1 signaling. They will form a foundation for development of PC1 as a therapeutic target for the treatment of ADPKD.

Learning Protein-Ligand Unbinding Pathways via Single-Parameter Community Detection

Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the comprehension of paths such systems take between metastable states. In MD simulations, paths are usually not observable per se, but they need to be inferred from simulation trajectories. Here, we present a novel approach to cluster trajectories based on a community detection algorithm that necessitates only the definition of a single parameter. The unbinding of the streptavidin-biotin complex is used as a benchmark system and the A2a adenosine receptor in complex with the inhibitor ZM241385 as an elaborate application. We demonstrate how such clusters of trajectories correspond to pathways and how the approach helps in the identification of reaction coordinates for a considered (un)binding process.

Understanding the Differences of Danusertib's Residence Time in Aurora Kinases A/B: Dissociation Paths and Key Residues Identified using Conventional and Enhanced Molecular Dynamics Simulations

The accurate experimental estimation of protein-ligand systems' residence time (τ) has become very relevant in drug design projects due to its importance in the last stages of refinement of the drug's pharmacodynamics and pharmacokinetics. It is now well-known that it is not sufficient to estimate the affinity of a protein-drug complex in the thermodynamic equilibrium process in in vitro experiments (closed systems), where the concentrations of the drug and protein remain constant. On the contrary, it is mandatory to consider the conformational dynamics of the system in terms of the binding and unbinding processes between protein and drugs in in vivo experiments (open systems), where their concentrations are in constant flux. This last model has been proven to dictate much of several drugs' pharmacological activities in vivo. At the atomistic level, molecular dynamics simulations can explain why some drugs are more effective than others or unveil the molecular aspects that make some drugs work better in one molecular target. Here, the protein kinases Aurora A/B, complexed with its inhibitor Danusertib, were studied using conventional and enhanced molecular dynamics (MD) simulations to estimate the dissociation paths and, therefore, the computational τ values and their comparison with experimental ones. Using classical molecular dynamics (cMD), three differential residues within the Aurora A/B active site, which seems to play an essential role in the observed experimental Danusertib's residence time against these kinases, were characterized. Then, using WT-MetaD, the relative Danusertib's residence times against Aurora A/B kinases were measured in a nanosecond time scale and were compared to those τ values observed experimentally. In addition, the potential dissociation paths of Danusertib in Aurora A and B were characterized, and differences that might be explained by the differential residues in the enzyme's active sites were found. In perspective, it is expected that this computational protocol can be applied to other protein-ligand complexes to understand, at the molecular level, the differences in residence times and amino acids that may contribute to it.

Structural basis of tolvaptan binding to the vasopressin V2 receptor

The vasopressin V2 receptor (V2R) is a validated therapeutic target for autosomal dominant polycystic kidney disease (ADPKD), with tolvaptan being the first FDA-approved antagonist. Herein, we used Gaussian accelerated molecular dynamics simulations to investigate the spontaneous binding of tolvaptan to both active and inactive V2R conformations at the atomic-level. Overall, the binding process consists of two stages. Tolvaptan binds initially to extracellular loops 2 and 3 (ECL2/3) before overcoming an energy barrier to enter the pocket. Our simulations result highlighted key residues (e.g., R181, Y205, F287, F178) involved in this process, which were experimentally confirmed by site-directed mutagenesis. This work provides structural insights into tolvaptan-V2R interactions, potentially aiding the design of novel antagonists for V2R and other G protein-coupled receptors.

Ligand Gaussian accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional Molecular Dynamics (cMD), due to limited simulation timescales. Based on our previously developed Ligand Gaussian accelerated Molecular Dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3", in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 microsecond simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 agreed with available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligand and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

Time-resolved cryo-EM of G-protein activation by a GPCR

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by stimulating guanine nucleotide exchange in the Gα subunit1. To visualize this mechanism, we developed a time-resolved cryo-EM approach that examines the progression of ensembles of pre-steady-state intermediates of a GPCR-G-protein complex. By monitoring the transitions of the stimulatory Gs protein in complex with the β2-adrenergic receptor at short sequential time points after GTP addition, we identified the conformational trajectory underlying G-protein activation and functional dissociation from the receptor. Twenty structures generated from sequential overlapping particle subsets along this trajectory, compared to control structures, provide a high-resolution description of the order of main events driving G-protein activation in response to GTP binding. Structural changes propagate from the nucleotide-binding pocket and extend through the GTPase domain, enacting alterations to Gα switch regions and the α5 helix that weaken the G-protein-receptor interface. Molecular dynamics simulations with late structures in the cryo-EM trajectory support that enhanced ordering of GTP on closure of the α-helical domain against the nucleotide-bound Ras-homology domain correlates with α5 helix destabilization and eventual dissociation of the G protein from the GPCR. These findings also highlight the potential of time-resolved cryo-EM as a tool for mechanistic dissection of GPCR signalling events.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.

Data-driven classification of ligand unbinding pathways

Studying the pathways of ligand-receptor binding is essential to understand the mechanism of target recognition by small molecules. The binding free energy and kinetics of protein-ligand complexes can be computed using molecular dynamics (MD) simulations, often in quantitative agreement with experiments. However, only a qualitative picture of the ligand binding/unbinding paths can be obtained through a conventional analysis of the MD trajectories. Besides, the higher degree of manual effort involved in analyzing pathways limits its applicability in large-scale drug discovery. Here, we address this limitation by introducing an automated approach for analyzing molecular transition paths with a particular focus on protein-ligand dissociation. Our method is based on the dynamic time-warping algorithm, originally designed for speech recognition. We accurately classified molecular trajectories using a very generic descriptor set of contacts or distances. Our approach outperforms manual classification by distinguishing between parallel dissociation channels, within the pathways identified by visual inspection. Most notably, we could compute exit-path-specific ligand-dissociation kinetics. The unbinding timescale along the fastest path agrees with the experimental residence time, providing a physical interpretation to our entirely data-driven protocol. In combination with appropriate enhanced sampling algorithms, this technique can be used for the initial exploration of ligand-dissociation pathways as well as for calculating path-specific thermodynamic and kinetic properties.

Mechanism of Ligand Binding to Theophylline RNA Aptamer

Studying RNA-ligand interactions and quantifying their binding thermodynamics and kinetics are of particular relevance in the field of drug discovery. Here, we combined biochemical binding assays and accelerated molecular simulations to investigate ligand binding and dissociation in RNA using the theophylline-binding RNA as a model system. All-atom simulations using a Ligand Gaussian accelerated Molecular Dynamics method (LiGaMD) have captured repetitive binding and dissociation of theophylline and caffeine to RNA. Theophylline's binding free energy and kinetic rate constants align with our experimental data, while caffeine's binding affinity is over 10,000 times weaker, and its kinetics could not be determined. LiGaMD simulations allowed us to identify distinct low-energy conformations and multiple ligand binding pathways to RNA. Simulations revealed a "conformational selection" mechanism for ligand binding to the flexible RNA aptamer, which provides important mechanistic insights into ligand binding to the theophylline-binding model. Our findings suggest that compound docking using a structural ensemble of representative RNA conformations would be necessary for structure-based drug design of flexible RNA.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Pharmacological Characterization of the Zebrafish (Danio Rerio) Histamine H1 Receptor Reveals the Involvement of the Second Extracellular Loop in the Binding of Histamine

The zebrafish (Danio rerio) histamine H1 receptor gene (zfH1R) was cloned in 2007 and reported to be involved in fish locomotion. Yet, no detailed characterization of its pharmacology and signaling properties have so far been reported. In this study, we pharmacologically characterized the zfH1R expressed in HEK-293T cells by means of [3H]-mepyramine binding and G protein-signaling assays. The zfH1R [dissociation constant (KD), 0.7 nM] displayed similar affinity for the antagonist [3H]-mepyramine as the human histamine H1 receptor (hH1R) (KD, 1.5 nM), whereas the affinity for histamine is 100-fold higher than for the human H1R. The zfH1R couples to Gαq/11 proteins and activates several reporter genes, i.e., NFAT, NFϰB, CRE, VEGF, COX-2, SRE, and AP-1, and zfH1R-mediated signaling is prevented by the Gαq/11 inhibitor YM-254890 and the antagonist mepyramine. Molecular modeling of the zfH1R and human H1R shows that the binding pockets are identical, implying that variations along the ligand binding pathway could underly the differences in histamine affinity instead. Targeting differentially charged residues in extracellular loop 2 (ECL2) using site-directed mutagenesis revealed that Arg21045x55 is most likely involved in the binding process of histamine in zfH1R. This study aids the understanding of the pharmacological differences between H1R orthologs and the role of ECL2 in histamine binding and provides fundamental information for the understanding of the histaminergic system in the zebrafish. SIGNIFICANCE STATEMENT: The use of the zebrafish as in vivo models in neuroscience is growing exponentially, which asks for detailed characterization of the aminergic neurotransmitter systems in this model. This study is the first to pharmacologically characterize the zebrafish histamine H1 receptor after expression in HEK-293T cells. The results show a high pharmacological and functional resemblance with the human ortholog but also reveal interesting structural differences and unveils an important role of the second extracellular loop in histamine binding.

Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors

A metadynamics protocol is presented to characterize the binding and unbinding of peptide ligands to class A G-protein-coupled receptors (GPCRs). The protocol expands on the one previously presented for binding and unbinding small-molecule ligands to class A GPCRs and accounts for the more demanding nature of the peptide binding-unbinding process. It applies to almost all class A GPCRs. Exemplary simulations are described for subtypes Y1R, Y2R, and Y4R of the neuropeptide Y receptor family, vasopressin binding to the vasopressin V2 receptor (V2R), and oxytocin binding to the oxytocin receptor (OTR). Binding free energies and the positions of alternative binding sites are presented and, where possible, compared with the experiment.

Computational Methods to Target Protein-Protein Interactions

The chapter emphasizes the importance of understanding protein-protein interactions in cellular mechanisms and highlights the role of computational modeling in predicting these interactions. It discusses sequence-based approaches such as evolutionary trace (ET), correlated mutation analysis (CMA), and subtractive correlated mutation (SCM) for identifying crucial amino acid residues, considering interface conservation or evolutionary changes. The chapter also explores methods like differential ET, hidden-site class model, and spatial cluster detection (SCD) for interface specificity and spatial clustering. Furthermore, it examines approaches combining structural and sequential methodologies and evaluates modeled predictions through initiatives like critical assessment of prediction of interactions (CAPRI). Additionally, the chapter provides an overview of various software programs used for molecular docking, detailing their search, sampling, refinement and scoring stages, along with innovative techniques and tools like normal mode analysis (NMA) and adaptive Poisson-Boltzmann solver (APBS) for electrostatic calculations. These computational and experimental approaches are crucial for unraveling protein-protein interactions and aid in developing potential therapeutics for various diseases.

Roles of Accelerated Molecular Dynamics Simulations in Predictions of Binding Kinetic Parameters

Rational predictions on binding kinetics parameters of drugs to targets play significant roles in future drug designs. Full conformational samplings of targets are requisite for accurate predictions of binding kinetic parameters. In this review, we mainly focus on the applications of enhanced sampling technologies in calculations of binding kinetics parameters and residence time of drugs. The methods involved in molecular dynamics simulations are applied to not only probe conformational changes of targets but also reveal calculations of residence time that is significant for drug efficiency. For this review, special attention are paid to accelerated molecular dynamics (aMD) and Gaussian aMD (GaMD) simulations that have been adopted to predict the association or disassociation rate constant. We also expect that this review can provide useful information for future drug design.

Cannabidiol at Nanomolar Concentrations Negatively Affects Signaling through the Adenosine A2A Receptor

Cannabidiol (CBD) is a phytocannabinoid with potential as a therapy for a variety of diseases. CBD may act via cannabinoid receptors but also via other G-protein-coupled receptors (GPCRs), including the adenosine A2A receptor. Homogenous binding and signaling assays in Chinese hamster ovary (CHO) cells expressing the human version of the A2A receptor were performed to address the effect of CBD on receptor functionality. CBD was not able to compete for the binding of a SCH 442416 derivative labeled with a red emitting fluorescent probe that is a selective antagonist that binds to the orthosteric site of the receptor. However, CBD reduced the effect of the selective A2A receptor agonist, CGS 21680, on Gs-coupling and on the activation of the mitogen activated kinase signaling pathway. It is suggested that CBD is a negative allosteric modulator of the A2A receptor.

Homo- and hetero-dimeric subunit interactions set affinity and efficacy in metabotropic glutamate receptors

Metabotropic glutamate receptors (mGluRs) are dimeric class C G-protein-coupled receptors that operate in glia and neurons. Glutamate affinity and efficacy vary greatly between the eight mGluRs. The molecular basis of this diversity is not understood. We used single-molecule fluorescence energy transfer to monitor the structural rearrangements of activation in the mGluR ligand binding domain (LBD). In saturating glutamate, group II homodimers fully occupy the activated LBD conformation (full efficacy) but homodimers of group III mGluRs do not. Strikingly, the reduced efficacy of Group III homodimers does not arise from differences in the glutamate binding pocket but, instead, from interactions within the extracellular dimerization interface that impede active state occupancy. By contrast, the functionally boosted mGluR II/III heterodimers lack these interface 'brakes' to activation and heterodimer asymmetry in the flexibility of a disulfide loop connecting LBDs greatly favors occupancy of the activated conformation. Our results suggest that dimerization interface interactions generate substantial functional diversity by differentially stabilizing the activated conformation. This diversity may optimize mGluR responsiveness for the distinct spatio-temporal profiles of synaptic versus extrasynaptic glutamate.

Elucidation of the structural basis for ligand binding and translocation in conserved insect odorant receptor co-receptors

In numerous insects, the olfactory receptor family forms a unique class of heteromeric cation channels. Recent progress in resolving the odorant receptor structures offers unprecedented opportunities for deciphering their molecular mechanisms of ligand recognition. Unexpectedly, these structures in apo or ligand-bound states did not reveal the pathway taken by the ligands between the extracellular space and the deep internal cavities. By combining molecular modeling with electrophysiological recordings, we identified amino acids involved in the dynamic entry pathway and the binding of VUAA1 to Drosophila melanogaster's odorant receptor co-receptor (Orco). Our results provide evidence for the exact location of the agonist binding site and a detailed and original mechanism of ligand translocation controlled by a network of conserved residues. These findings would explain the particularly high selectivity of Orcos for their ligands.

Is the Functional Response of a Receptor Determined by the Thermodynamics of Ligand Binding?

For an effective drug, strong binding to the target protein is a prerequisite, but it is not enough. To produce a particular functional response, drugs need to either block the proteins' functions or modulate their activities by changing their conformational equilibrium. The binding free energy of a compound to its target is routinely calculated, but the timescales for the protein conformational changes are prohibitively long to be efficiently modeled via physics-based simulations. Thermodynamic principles suggest that the binding free energies of the ligands with different receptor conformations may infer their efficacy. However, this hypothesis has not been thoroughly validated. We present an actionable protocol and a comprehensive study to show that binding thermodynamics provides a strong predictor of the efficacy of a ligand. We apply the absolute binding free energy perturbation method to ligands bound to active and inactive states of eight G protein-coupled receptors and a nuclear receptor and then compare the resulting binding free energies. We find that carefully designed restraints are often necessary to efficiently model the corresponding conformational ensembles for each state. Our method achieves unprecedented performance in classifying ligands as agonists or antagonists across the various investigated receptors, all of which are important drug targets.

3site Multisubstrate-Bound State of Cytochrome P450cam

We identified a multisubstrate-bound state, hereby referred as a 3site state, in cytochrome P450cam via integrating molecular dynamics simulation with nuclear magnetic resonance (NMR) pseudocontact shift measurements. The 3site state is a result of simultaneous binding of three camphor molecules in three locations around P450cam: (a) in a well-established "catalytic" site near heme, (b) in a kink-separated "waiting" site along channel-1, and (c) in a previously reported "allosteric" site at E, F, G, and H helical junctions. These three spatially distinct binding modes in the 3site state mutually communicate with each other via homotropic allostery and act cooperatively to render P450cam functional. The 3site state shows a significantly superior fit with NMR pseudo contact shift (PCS) data with a Q-score of 0.045 than previously known bound states and consists of D251 free of salt-bridges with K178 and R186, rendering the enzyme functionally primed. To date, none of the reported cocomplex of P450cam with its redox partner putidaredoxin (pdx) has been able to match solution NMR data and controversial pdx-induced opening of P450cam's channel-1 remains a matter of recurrent discourse. In this regard, inclusion of pdx to the 3site state is able to perfectly fit the NMR PCS measurement with a Q-score of 0.08 and disfavors the pdx-induced opening of channel-1, reconciling previously unexplained remarkably fast hydroxylation kinetics with a koff of 10.2 s-1. Together, our findings hint that previous experimental observations may have inadvertently captured the 3site state as an in vitro solution state, instead of the catalytic state alone, and provided a distinct departure from the conventional understanding of cytochrome P450.

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.

General Chemical Reaction Network Theory for Olfactory Sensing Based on G-Protein-Coupled Receptors: Elucidation of Odorant Mixture Effects and Agonist-Synergist Threshold

This work presents a general chemical reaction network theory for olfactory sensing processes that employ G-protein-coupled receptors as olfactory receptors (ORs). The theory can be applied to general mixtures of odorants and an arbitrary number of ORs. Reactions of ORs with G-proteins, in both the presence and absence of odorants, are explicitly considered. A unique feature of the theory is the definition of an odor activity vector consisting of strengths of odorant-induced signals from ORs relative to those due to background G-protein activity in the absence of odorants. It is demonstrated that each component of the odor activity defined this way reduces to a Michaelis-Menten form capable of accounting for cooperation or competition effects between different odorants. The main features of the theory are illustrated for a two-odorant mixture. Known and potential mixture effects, such as suppression, shadowing, inhibition, and synergy, are quantitatively described. Effects of relative values of rate constants, basal activity, and G-protein concentration are also demonstrated.

Xanomeline displays concomitant orthosteric and allosteric binding modes at the M4 mAChR

The M4 muscarinic acetylcholine receptor (M4 mAChR) has emerged as a drug target of high therapeutic interest due to its expression in regions of the brain involved in the regulation of psychosis, cognition, and addiction. The mAChR agonist, xanomeline, has provided significant improvement in the Positive and Negative Symptom Scale (PANSS) scores in a Phase II clinical trial for the treatment of patients suffering from schizophrenia. Here we report the active state cryo-EM structure of xanomeline bound to the human M4 mAChR in complex with the heterotrimeric Gi1 transducer protein. Unexpectedly, two molecules of xanomeline were found to concomitantly bind to the monomeric M4 mAChR, with one molecule bound in the orthosteric (acetylcholine-binding) site and a second molecule in an extracellular vestibular allosteric site. Molecular dynamic simulations supports the structural findings, and pharmacological validation confirmed that xanomeline acts as a dual orthosteric and allosteric ligand at the human M4 mAChR. These findings provide a basis for further understanding xanomeline's complex pharmacology and highlight the myriad of ways through which clinically relevant ligands can bind to and regulate GPCRs.

Temperature Accelerated Sliced Sampling to Probe Ligand Dissociation from Protein

Modeling ligand unbinding in proteins to estimate the free energy of binding and probing the mechanism presents several challenges. They primarily pertain to the entropic bottlenecks resulting from protein and solvent conformations. While exploring the unbinding processes using enhanced sampling techniques, very long simulations are required to sample all of the conformational states as the system gets trapped in local free energy minima along transverse coordinates. Here, we demonstrate that temperature accelerated sliced sampling (TASS) is an ideal approach to overcome some of the difficulties faced by conventional sampling methods in studying ligand unbinding. Using TASS, we study the unbinding of avibactam inhibitor molecules from the Class C β-lactamase (CBL) active site. Extracting CBL-avibactam unbinding free energetics, unbinding pathways, and identifying critical interactions from the TASS simulations are demonstrated.

Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization.

Recessive CHRM5 variant as a potential cause of neurogenic bladder

Neurogenic bladder is caused by disruption of neuronal pathways regulating bladder relaxation and contraction. In severe cases, neurogenic bladder can lead to vesicoureteral reflux, hydroureter, and chronic kidney disease. These complications overlap with manifestations of congenital anomalies of the kidney and urinary tract (CAKUT). To identify novel monogenic causes of neurogenic bladder, we applied exome sequencing (ES) to our cohort of families with CAKUT. By ES, we have identified a homozygous missense variant (p.Gln184Arg) in CHRM5 (cholinergic receptor, muscarinic, 5) in a patient with neurogenic bladder and secondary complications of CAKUT. CHRM5 codes for a seven transmembrane-spanning G-protein-coupled muscarinic acetylcholine receptor. CHRM5 is shown to be expressed in murine and human bladder walls and is reported to cause bladder overactivity in Chrm5 knockout mice. We investigated CHRM5 as a potential novel candidate gene for neurogenic bladder with secondary complications of CAKUT. CHRM5 is similar to the cholinergic bladder neuron receptor CHRNA3, which Mann et al. published as the first monogenic cause of neurogenic bladder. However, functional in vitro studies did not reveal evidence to strengthen the status as a candidate gene. Discovering additional families with CHRM5 variants could help to further assess the genes' candidate status.

Stereochemistry-Driven Interactions of α,γ-Peptide Ligands with the Neuropeptide Y Y4-Receptor

The G-protein-coupled Y₄-receptor (Y₄R) and its endogenous ligand, pancreatic polypeptide (PP), suppress appetite in response to food intake and, thus, are attractive drug targets for body-weight control. The C-terminus of human PP (hPP), T³²-R³³-P³⁴-R³⁵-Y³⁶-NH₂, penetrates deep into the binding pocket with its tyrosine-amide and di-arginine motif. Here, we present two C-terminally amidated α,γ-hexapeptides (1a/b) with sequence Ac-R³¹-γ-CBAA³²-R³³-L³⁴-R³⁵-Y³⁶-NH₂, where γ-CBAA is the (1R,2S,3R)-configured 2-(aminomethyl)-3-phenylcyclobutanecarboxyl moiety (1a) or its mirror image (1b). Both peptides bind the Y₄R (K_i of 1a/b: 0.66/12 nM) and act as partial agonists (intrinsic activity of 1a/b: 50/39%). Their induced-fit binding poses in the Y₄R pocket are unique and build ligand-receptor contacts distinct from those of the C-terminus of the endogenous ligand hPP. We conclude that energetically favorable interactions, although they do not match those of the native ligand hPP, still guarantee high binding affinity (with 1a rivaling hPP) but not the maximum receptor activation.

15 Years of molecular simulation of drug-binding kinetics

INTRODUCTION

Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made.

AREAS COVERED

This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years.

EXPERT OPINION

The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.

Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event

Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.

A deep encoder-decoder framework for identifying distinct ligand binding pathways

The pathway(s) that a ligand would adopt en route to its trajectory to the native pocket of the receptor protein act as a key determinant of its biological activity. While Molecular Dynamics (MD) simulations have emerged as the method of choice for modeling protein-ligand binding events, the high dimensional nature of the MD-derived trajectories often remains a barrier in the statistical elucidation of distinct ligand binding pathways due to the stochasticity inherent in the ligand's fluctuation in the solution and around the receptor. Here, we demonstrate that an autoencoder based deep neural network, trained using an objective input feature of a large matrix of residue-ligand distances, can efficiently produce an optimal low-dimensional latent space that stores necessary information on the ligand-binding event. In particular, for a system of L99A mutant of T4 lysozyme interacting with its native ligand, benzene, this deep encoder-decoder framework automatically identifies multiple distinct recognition pathways, without requiring user intervention. The intermediates involve the spatially discrete location of the ligand in different helices of the protein before its eventual recognition of native pose. The compressed subspace derived from the autoencoder provides a quantitatively accurate measure of the free energy and kinetics of ligand binding to the native pocket. The investigation also recommends that while a linear dimensional reduction technique, such as time-structured independent component analysis, can do a decent job of state-space decomposition in cases where the intermediates are long-lived, autoencoder is the method of choice in systems where transient, low-populated intermediates can lead to multiple ligand-binding pathways.

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.

Constrained catecholamines gain β2AR selectivity through allosteric effects on pocket dynamics

G protein-coupled receptors (GPCRs) within the same subfamily often share high homology in their orthosteric pocket and therefore pose challenges to drug development. The amino acids that form the orthosteric binding pocket for epinephrine and norepinephrine in the β1 and β2 adrenergic receptors (β1AR and β2AR) are identical. Here, to examine the effect of conformational restriction on ligand binding kinetics, we synthesized a constrained form of epinephrine. Surprisingly, the constrained epinephrine exhibits over 100-fold selectivity for the β2AR over the β1AR. We provide evidence that the selectivity may be due to reduced ligand flexibility that enhances the association rate for the β2AR, as well as a less stable binding pocket for constrained epinephrine in the β1AR. The differences in the amino acid sequence of the extracellular vestibule of the β1AR allosterically alter the shape and stability of the binding pocket, resulting in a marked difference in affinity compared to the β2AR. These studies suggest that for receptors containing identical binding pocket residues, the binding selectivity may be influenced in an allosteric manner by surrounding residues, like those of the extracellular loops (ECLs) that form the vestibule. Exploiting these allosteric influences may facilitate the development of more subtype-selective ligands for GPCRs.

The anomalous pharmacology of fentanyl

Fentanyl is a key therapeutic, used in anaesthesia and pain management. It is also increasingly used illicitly and is responsible for a large and growing number of opioid overdose deaths, especially in North America. A number of factors have been suggested to contribute to fentanyl's lethality, including rapid onset of action, in vivo potency, ligand bias, induction of muscle rigidity and reduced sensitivity to reversal by naloxone. Some of these factors can be considered to represent 'anomalous' pharmacological properties of fentanyl when compared with prototypical opioid agonists such as morphine. In this review, we examine the nature of fentanyl's 'anomalous' properties, to determine whether there is really a pharmacological basis to support the existence of such properties, and also discuss whether such properties are likely to contribute to overdose deaths involving fentanyls. LINKED ARTICLES: This article is part of a themed issue on Advances in Opioid Pharmacology at the Time of the Opioid Epidemic. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v180.7/issuetoc.

Time-resolved cryo-EM of G protein activation by a GPCR

G protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by stimulating the exchange of guanine nucleotide in the Gα subunit. To visualize this mechanism, we developed a time-resolved cryo-EM approach that examines the progression of ensembles of pre-steady-state intermediates of a GPCR-G protein complex. Using variability analysis to monitor the transitions of the stimulatory Gs protein in complex with the β 2 -adrenergic receptor (β 2 AR) at short sequential time points after GTP addition, we identified the conformational trajectory underlying G protein activation and functional dissociation from the receptor. Twenty transition structures generated from sequential overlapping particle subsets along this trajectory, compared to control structures, provide a high-resolution description of the order of events driving G protein activation upon GTP binding. Structural changes propagate from the nucleotide-binding pocket and extend through the GTPase domain, enacting alterations to Gα Switch regions and the α5 helix that weaken the G protein-receptor interface. Molecular dynamics (MD) simulations with late structures in the cryo-EM trajectory support that enhanced ordering of GTP upon closure of the alpha-helical domain (AHD) against the nucleotide-bound Ras-homology domain (RHD) correlates with irreversible α5 helix destabilization and eventual dissociation of the G protein from the GPCR. These findings also highlight the potential of time-resolved cryo-EM as a tool for mechanistic dissection of GPCR signaling events.