Predicting ligand binding poses for low-resolution membrane protein models: Perspectives from multiscale simulations

Orphan G protein-coupled receptors: the ongoing search for a home

G protein-coupled receptors (GPCRs) make up the largest receptor superfamily, accounting for 4% of protein-coding genes. Despite the prevalence of such transmembrane receptors, a significant number remain orphans, lacking identified endogenous ligands. Since their conception, the reverse pharmacology approach has been used to characterize such receptors. However, the multifaceted and nuanced nature of GPCR signaling poses a great challenge to their pharmacological elucidation. Considering their therapeutic relevance, the search for native orphan GPCR ligands continues. Despite limited structural input in terms of 3D crystallized structures, with advances in machine-learning approaches, there has been great progress with respect to accurate ligand prediction. Though such an approach proves valuable given that ligand scarcity is the greatest hurdle to orphan GPCR deorphanization, the future pairings of the remaining orphan GPCRs may not necessarily take a one-size-fits-all approach but should be more comprehensive in accounting for numerous nuanced possibilities to cover the full spectrum of GPCR signaling.

Fast, Accurate, and System-Specific Variable-Resolution Modeling of Proteins

In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.

Modeling the Orthosteric Binding Site of the G Protein-Coupled Odorant Receptor OR5K1

With approximately 400 encoding genes in humans, odorant receptors (ORs) are the largest subfamily of class A G protein-coupled receptors (GPCRs). Despite its high relevance and representation, the odorant-GPCRome is structurally poorly characterized: no experimental structures are available, and the low sequence identity of ORs to experimentally solved GPCRs is a significant challenge for their modeling. Moreover, the receptive range of most ORs is unknown. The odorant receptor OR5K1 was recently and comprehensively characterized in terms of cognate agonists. Here, we report two additional agonists and functional data of the most potent compound on two mutants, L104^3.32 and L255^6.51. Experimental data was used to guide the investigation of the binding modes of OR5K1 ligands into the orthosteric binding site using structural information from AI-driven modeling, as recently released in the AlphaFold Protein Structure Database, and from homology modeling. Induced-fit docking simulations were used to sample the binding site conformational space for ensemble docking. Mutagenesis data guided side chain residue sampling and model selection. We obtained models that could better rationalize the different activity of active (agonist) versus inactive molecules with respect to starting models and also capture differences in activity related to minor structural differences. Therefore, we provide a model refinement protocol that can be applied to model the orthosteric binding site of ORs as well as that of GPCRs with low sequence identity to available templates.

Presynaptic Release-Regulating Alpha2 Autoreceptors: Potential Molecular Target for Ellagic Acid Nutraceutical Properties

Polyphenol ellagic acid (EA) possesses antioxidant, anti-inflammatory, anti-carcinogenic, anti-diabetic and cardio protection activities, making it an interesting multi-targeting profile. EA also controls the central nervous system (CNS), since it was proven to reduce the immobility time of mice in both the forced swimming and the tail-suspension tests, with an efficiency comparable to that of classic antidepressants. Interestingly, the anti-depressant-like effect was almost nulled by the concomitant administration of selective antagonists of the noradrenergic receptors, suggesting the involvement of these cellular targets in the central effects elicited by EA and its derivatives. By in silico and in vitro studies, we discuss how EA engages with human α_2A-ARs and α_2C-AR catalytic pockets, comparing EA behaviour with that of known agonists and antagonists. Structurally, the hydrophobic residues surrounding the α_2A-AR pocket confer specificity on the intermolecular interactions and hence lead to favourable binding of EA in the α_2A-AR, with respect to α_2C-AR. Moreover, EA seems to better accommodate within α_2A-ARs into the TM5 area, close to S200 and S204, which play a crucial role for activation of aminergic GPCRs such as the α₂-AR, highlighting its promising role as a partial agonist. Consistently, EA mimics clonidine in inhibiting noradrenaline exocytosis from hippocampal nerve endings in a yohimbine-sensitive fashion that confirms the engagement of naïve α₂-ARs in the EA-mediated effect.

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.

Bitter taste in silico: A review on virtual ligand screening and characterization methods for TAS2R-bitterant interactions

The growing pharmaceutical interest in the human bitter taste receptors (hTAS2Rs) has two dimensions; i) evaluation of the bitterness of active pharmaceutical compounds, in order to develop strategies for improving patients' adherence to medication, and ii) application of ligands for extra-cellular hTAS2Rs for potential preventive therapeutic achievements. The result is an increasing demand on robust tools for bitterness assessment and screening the receptor-ligand affinity. In silico tools are useful for aiding experimental-screening, as well as to elucide ligand-receptor interactions. In this review, the ligand-based and structure-based approaches are described as the two main in silico tools for bitter taste analysis. The strengths and weaknesses of each approach are discussed. Both approaches provide key tools for understanding and exploiting bitter taste for human health applications.

Atomistic detailed free-energy landscape of intrinsically disordered protein studied by multi-scale divide-and-conquer molecular dynamics simulation

Calcineurin (CaN) is a eukaryotic serine/threonine protein phosphatase activated by both Ca²⁺ and calmodulin (CaM), including intrinsically disordered region (IDR). The region undergoes folding into an α-helix form in the presence Ca²⁺ -loaded CaM. To sample the ordered structure of the IDR by conventional all atom model (AAM) molecular dynamics (MD) simulation, the IDR and Ca²⁺ -loaded CaM must be simultaneously treated. However, it is time-consuming task because the coupled folding and binding should include repeated binding and dissociation. Then, in this study, we propose novel multi-scale divide-and-conquer MD (MSDC-MD), which combines AAM-MD and coarse-grained model MD (CGM-MD). To speed up the conformation sampling, MSDC-MD simulation first treats the IDR by CGM to sample conformations from wide conformation space; then, multiple AAM-MD in a limited area is initiated using the resultant CGM conformation, which is reconstructed by homology modeling method. To investigate performance, we sampled the ordered conformation of the IDR using MSDC-MD; the root-mean-square distance (RMSD) with respect to the experimental structure was 2.23 Å.

In Silico Investigation of Bitter Hop-Derived Compounds and Their Cognate Bitter Taste Receptors

The typical bitter taste of beer is caused by adding hops (Humulus lupulus L.) during the wort boiling process. The bitter taste of hop-derived compounds was found to be mediated by three bitter taste receptors: TAS2R1, TAS2R14, and TAS2R40. In this work, structural bioinformatics analyses were used to characterize the binding modes of trans-isocohumulone, trans-isohumulone, trans-isoadhumulone, cis-isocohumulone, cis-isohumulone, cis-isoadhumulone, cohumulone, humulone, adhumulone, and 8-prenylnaringenin into the orthosteric binding site of their cognate receptors. A conserved asparagine in transmembrane 3 was found to be essential for the recognition of hop-derived compounds, whereas the surrounding residues in the binding site of the three receptors encode the ligand specificity. Hop-derived compounds are renowned bioactive molecules and are considered as potential hit molecules for drug discovery to treat metabolic diseases. A chemoinformatics analysis revealed that hop-derived compounds cluster in a different region of the chemical space compared to known bitter food-derived compounds, pinpointing hop-derived compounds as a very peculiar class of bitter compounds.

Hybrid MM/CG Webserver: Automatic Set Up of Molecular Mechanics/Coarse-Grained Simulations for Human G Protein-Coupled Receptor/Ligand Complexes

Hybrid Molecular Mechanics/Coarse-Grained (MM/CG) simulations help predict ligand poses in human G protein-coupled receptors (hGPCRs), the most important protein superfamily for pharmacological applications. This approach allows the description of the ligand, the binding cavity, and the surrounding water molecules at atomistic resolution, while coarse-graining the rest of the receptor. Here, we present the Hybrid MM/CG Webserver (mmcg.grs.kfa-juelich.de) that automatizes and speeds up the MM/CG simulation setup of hGPCR/ligand complexes. Initial structures for such complexes can be easily and efficiently generated with other webservers. The Hybrid MM/CG server also allows for equilibration of the systems, either fully automatically or interactively. The results are visualized online (using both interactive 3D visualizations and analysis plots), helping the user identify possible issues and modify the setup parameters accordingly. Furthermore, the prepared system can be downloaded and the simulation continued locally.

Ligand Pose Predictions for Human G Protein-Coupled Receptors: Insights from the Amber-Based Hybrid Molecular Mechanics/Coarse-Grained Approach

Human G protein-coupled receptors (hGPCRs) are the most frequent targets of Food and Drug Administration (FDA)-approved drugs. Structural bioinformatics, along with molecular simulation, can support structure-based drug design targeting hGPCRs. In this context, several years ago, we developed a hybrid molecular mechanics (MM)/coarse-grained (CG) approach to predict ligand poses in low-resolution hGPCR models. The approach was based on the GROMOS96 43A1 and PRODRG united-atom force fields for the MM part. Here, we present a new MM/CG implementation using, instead, the Amber 14SB and GAFF all-atom potentials for proteins and ligands, respectively. The new implementation outperforms the previous one, as shown by a variety of applications on models of hGPCR/ligand complexes at different resolutions, and it is also more user-friendly. Thus, it emerges as a useful tool to predict poses in low-resolution models and provides insights into ligand binding similarly to all-atom molecular dynamics, albeit at a lower computational cost.

Multiscale simulation approaches to modeling drug-protein binding

Simulations can provide detailed insight into the molecular processes involved in drug action, such as protein-ligand binding, and can therefore be a valuable tool for drug design and development. Processes with a large range of length and timescales may be involved, and understanding these different scales typically requires different types of simulation methodology. Ideally, simulations should be able to connect across scales, to analyze and predict how changes at one scale can influence another. Multiscale simulation methods, which combine different levels of treatment, are an emerging frontier with great potential in this area. Here we review multiscale frameworks of various types, and selected applications to biomolecular systems with a focus on drug-ligand binding.

Toward the Identification of Extra-Oral TAS2R Agonists as Drug Agents for Muscle Relaxation Therapies via Bioinformatics-Aided Screening of Bitter Compounds in Traditional Chinese Medicine

Significant advances have been made in the past decade in mapping the distributions and the physiological functions of extra-oral bitter taste receptors (TAS2Rs) in non-gustatory tissues. In particular, it has been found that TAS2Rs are expressed in various muscle tissues and activation of TAS2Rs can lead to muscle cell relaxation, which suggests that TAS2Rs may be important new targets in muscle relaxation therapy for various muscle-related diseases. So far, however, there is a lack of potent extra-oral TAS2R agonists that can be used as novel drug agents in muscle relaxation therapies. Interestingly, traditional Chinese medicine (TCM) often characterizes a drug’s property in terms of five distinct flavors (bitter, sweet, sour, salty, and pungent) according to its taste and function, and commonly regards “bitterness” as an intrinsic property of “good medicine.” In addition, many bitter flavored TCM are known in practice to cause muscle relaxation after long term use, and in lab experiments the compounds identified from some bitter flavored TCM do activate TAS2Rs and thus relax muscle cells. Therefore, it is highly possible to discover very useful extra-oral TAS2R agonists for muscle relaxation therapies among the abundant bitter compounds used in bitter flavored TCM. With this perspective, we reviewed in literature the distribution of TAS2Rs in different muscle systems with a focus on the map of bitter flavored TCM which can regulate muscle contractility and related functional chemical components. We also reviewed the recently established databases of TCM chemical components and the bioinformatics software which can be used for high-throughput screening and data mining of the chemical components associated with bitter flavored TCM. All together, we aim to present a knowledge-based approach and technological platform for identification or discovery of extra-oral TAS2R agonists that can be used as novel drug agents for muscle relaxation therapies through screening and evaluation of chemical compounds used in bitter flavored TCM.

Dual binding mode of "bitter sugars" to their human bitter taste receptor target

The 25 human bitter taste receptors (hTAS2Rs) are responsible for detecting bitter molecules present in food, and they also play several physiological and pathological roles in extraoral compartments. Therefore, understanding their ligand specificity is important both for food research and for pharmacological applications. Here we provide a molecular insight into the exquisite molecular recognition of bitter β-glycopyranosides by one of the members of this receptor subclass, hTAS2R16. Most of its agonists have in common the presence of a β-glycopyranose unit along with an extremely structurally diverse aglycon moiety. This poses the question of how hTAS2R16 can recognize such a large number of "bitter sugars". By means of hybrid molecular mechanics/coarse grained molecular dynamics simulations, here we show that the three hTAS2R16 agonists salicin, arbutin and phenyl-β-D-glucopyranoside interact with the receptor through a previously unrecognized dual binding mode. Such mechanism may offer a seamless way to fit different aglycons inside the binding cavity, while maintaining the sugar bound, similar to the strategy used by several carbohydrate-binding lectins. Our prediction is validated a posteriori by comparison with mutagenesis data and also rationalizes a wealth of structure-activity relationship data. Therefore, our findings not only provide a deeper molecular characterization of the binding determinants for the three ligands studied here, but also give insights applicable to other hTAS2R16 agonists. Together with our results for other hTAS2Rs, this study paves the way to improve our overall understanding of the structural determinants of ligand specificity in bitter taste receptors.

From quantum to subcellular scales: multi-scale simulation approaches and the SIRAH force field

Modern molecular and cellular biology profits from astonishing resolution structural methods, currently even reaching the whole cell level. This is encompassed by the development of computational methods providing a deep view into the structure and dynamics of molecular processes happening at very different scales in time and space. Linking such scales is of paramount importance when aiming at far-reaching biological questions. Computational methods at the interface between classical and coarse-grained resolutions are gaining momentum with several research groups dedicating important efforts to their development and tuning. An overview of such methods is addressed herein, with special emphasis on the SIRAH force field for coarse-grained and multi-scale simulations. Moreover, we provide proof of concept calculations on the implementation of a multi-scale simulation scheme including quantum calculations on a classical fine-grained/coarse-grained representation of double-stranded DNA. This opens the possibility to include the effect of large conformational fluctuations in chromatin segments on, for instance, the reactivity of particular base pairs within the same simulation framework.

Mutagenesis computer experiments in pentameric ligand-gated ion channels: the role of simulation tools with different resolution

Pentameric ligand-gated ion channels (pLGICs) are an important class of widely expressed membrane neuroreceptors, which play a crucial role in fast synaptic communications and are involved in several neurological conditions. They are activated by the binding of neurotransmitters, which trigger the transmission of an electrical signal via facilitated ion flux. They can also be activated, inhibited or modulated by a number of drugs. Mutagenesis electrophysiology experiments, with natural or unnatural amino acids, have provided a large body of functional data that, together with emerging structural information from X-ray spectroscopy and cryo-electron microscopy, are helping unravel the complex working mechanisms of these neuroreceptors. Computer simulations are complementing these mutagenesis experiments, with insights at various levels of accuracy and resolution. Here, we review how a selection of computational tools, including first principles methods, classical molecular dynamics and enhanced sampling techniques, are contributing to construct a picture of how pLGICs function and can be pharmacologically targeted to treat the disorders they are responsible for.

Novel combinations of experimental and computational analysis tested on the binding of metalloprotoporphyrins to albumin

The evidence that Human Serum Albumin (HSA) binds metal ions and organometallic compounds has generated interest in its physiological role as a metalloprotein and as a vehicle for synthetic biology applications (e.g., synthetic blood and solar energy conversion). HSA has been shown to bind metallo-porphyrins, however, the structural details of such interactions are available only for the HSA:heme complex. A typical challenge for studying the interaction of proteins with metalloporphyrins is the poor solubility of the ligands that affect the characterization the complexes. The manuscript shows that a combination of dialysis and centrifugation yields aqueous solutions that contain >90% HSA:porphyrin complexes and virtually eliminate aggregated ligands. The removal of aggregates increases the quality of the optical spectroscopy data which, in turn, yield more accurate binding constants (~0.1 and 2.1 × 10⁶ M^-1) and reveal FRET between Trp214 and the porphyrins. The Trp-porphyrin distance was estimated to be within the 28-34 Å range and was used to guide the search of binding sites through a novel feedback approach with docking simulations. Results suggest while some protoporphyrins (metal-free, Fe(III)PPIX and Mg(II)PPIX) bind HSA at the heme site, others (Zn(II)PPIX, Mn(III)PPIX and Sn(IV)PPIX) are more likely to bind the Cys34.

Understanding Ligand Binding to G-Protein Coupled Receptors Using Multiscale Simulations

Human G-protein coupled receptors (GPCRs) convey a wide variety of extracellular signals inside the cell and they are one of the main targets for pharmaceutical intervention. Rational drug design requires structural information on these receptors; however, the number of experimental structures is scarce. This gap can be filled by computational models, based on homology modeling and docking techniques. Nonetheless, the low sequence identity across GPCRs and the chemical diversity of their ligands may limit the quality of these models and hence refinement using molecular dynamics simulations is recommended. This is the case for olfactory and bitter taste receptors, which constitute the first and third largest GPCR groups and show sequence identities with the available GPCR templates below 20%. We have developed a molecular dynamics approach, based on the combination of molecular mechanics and coarse grained (MM/CG), tailored to study ligand binding in GPCRs. This approach has been applied so far to bitter taste receptor complexes, showing significant predictive power. The protein/ligand interactions observed in the simulations were consistent with extensive mutagenesis and functional data. Moreover, the simulations predicted several binding residues not previously tested, which were subsequently verified by carrying out additional experiments. Comparison of the simulations of two bitter taste receptors with different ligand selectivity also provided some insights into the binding determinants of bitter taste receptors. Although the MM/CG approach has been applied so far to a limited number of GPCR/ligand complexes, the excellent agreement of the computational models with the mutagenesis and functional data supports the applicability of this method to other GPCRs for which experimental structures are missing. This is particularly important for the challenging case of GPCRs with low sequence identity with available templates, for which molecular docking shows limited predictive power.

Multiscale simulations on human Frizzled and Taste2 GPCRs

Recently, molecular dynamics simulations, from all atom and coarse grained to hybrid methods bridging the two scales, have provided exciting functional insights into class F (Frizzled and Taste2) GPCRs (about 40 members in humans). Findings include: (i) The activation of one member of the Frizzled receptors (FZD4) involves a bending of transmembrane helix TM7 far larger than that in class A GPCRs. (ii) The affinity of an anticancer drug targeting another member (Smoothened receptor) decreases in a specific drug-resistant variant, because the mutation ultimately disrupts the binding cavity and affects TM6. (iii) A novel two-state recognition mechanism explains the very large agonist diversity for at least one member of the Taste2 GPCRs, hTAS2R46.

Automated discovery of GPCR bioactive ligands

While G-protein-coupled receptors (GPCRs) constitute the largest class of membrane proteins, structures and endogenous ligands of a large portion of GPCRs remain unknown. Because of the involvement of GPCRs in various signaling pathways and physiological roles, the identification of endogenous ligands as well as designing novel drugs is of high interest to the research and medical communities. Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning. Machine learning is centered around the development and applications of algorithms that can learn from data automatically. Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies. This review describes the most recent and successful applications of machine learning for bioactive ligand discovery, concluding with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.

Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) belong to the largest class of drug targets. Approximately half of the members of the human GPCR superfamily are chemosensory receptors, including odorant receptors (ORs), trace amine-associated receptors (TAARs), bitter taste receptors (TAS2Rs), sweet and umami taste receptors (TAS1Rs). Interestingly, these chemosensory GPCRs (csGPCRs) are expressed in several tissues of the body where they are supposed to play a role in biological functions other than chemosensation. Despite their abundance and physiological/pathological relevance, the druggability of csGPCRs has been suggested but not fully characterized. Here, we aim to explore the potential of targeting csGPCRs to treat diseases by reviewing the current knowledge of csGPCRs expressed throughout the body and by analysing the chemical space and the drug-likeness of flavour molecules.

Open-Boundary Molecular Mechanics/Coarse-Grained Framework for Simulations of Low-Resolution G-Protein-Coupled Receptor-Ligand Complexes

G-protein-coupled receptors (GPCRs) constitute as much as 30% of the overall proteins targeted by FDA-approved drugs. However, paucity of structural experimental information and low sequence identity between members of the family impair the reliability of traditional docking approaches and atomistic molecular dynamics simulations for in silico pharmacological applications. We present here a dual-resolution approach tailored for such low-resolution models. It couples a hybrid molecular mechanics/coarse-grained (MM/CG) scheme, previously developed by us for GPCR-ligand complexes, with a Hamiltonian-based adaptive resolution scheme (H-AdResS) for the solvent. This dual-resolution approach removes potentially inaccurate atomistic details from the model while building a rigorous statistical ensemble-the grand canonical one-in the high-resolution region. We validate the method on a well-studied GPCR-ligand complex, for which the 3D structure is known, against atomistic simulations. This implementation paves the way for future accurate in silico studies of low-resolution ligand/GPCRs models.