Reliability of Docking-Based Virtual Screening for GPCR Ligands with Homology Modeled Structures: A Case Study of the Angiotensin II Type I Receptor

Molecular Dynamic Simulations to Determine Individualized Therapy: Tetrabenazine for the GNAO1 Encephalopathy E246K Variant

INTRODUCTION

GNAO1 encephalopathy is characterized by severe hypotonia, psychomotor retardation, epilepsy, and movement disorders. Genetic variations in GNAO1 have been linked to neurological symptoms including movement disorders like dystonia. The correlation between the E246K mutation in the Gα subunit and aberrant signal transduction of G proteins has been established but no data are reported regarding the efficacy of medical treatment with tetrabenazine.

METHODS

Molecular modeling studies were performed to elucidate the molecular mechanisms underlying this mutation. We developed drug efficacy models using molecular dynamic simulations that replicated the behavior of wild-type and mutated proteins in the presence or absence of ligands.

RESULTS AND DISCUSSION

We demonstrated that the absence of the mutation leads to normal signal transduction upon receptor activation by the endogenous ligand, but not in the presence of tetrabenazine. In contrast, the presence of the mutation resulted in abnormal signal transduction in the presence of the endogenous ligand, which was corrected by the drug tetrabenazine. Tetrabenazine was identified as a promising therapeutic option for pediatric patients suffering from encephalopathy due to an E246K mutation in the GNAO1 gene validated through molecular dynamics. This is a potential first example of the use of this technique in a rare neurological pediatric disease.

Identification of novel nematode succinate dehydrogenase inhibitors: Virtual screening based on ligand-pocket interactions

To discover new nematicidal succinate dehydrogenase (SDH) inhibitors with novel structures, we conducted a virtual screening of the ChemBridge library with 1.7 million compounds based on ligand-pocket interactions. The homology model of Caenorhabditis elegans SDH was established, along with a pharmacophore model based on ligand-pocket interactions. After the pharmacophore-based and docking-based screening, 19 compounds were selected for the subsequent enzymatic assays. The results showed that compound 1 (ID: 7607321) exhibited inhibitory activity against SDH with a determined IC₅₀ value of 19.6 μM. Structural modifications and nematicidal activity studies were then carried out, which provided further evidence that compound 1 exhibited excellent nematicidal activity. Molecular dynamics simulations were then conducted to investigate the underlying molecular basis for the potency of these inhibitors against SDH. This work provides a reliable strategy and useful information for the future design of nematode SDH inhibitors.

Mechanism for the Potential Inhibition Effect of Microcystin-LR Disinfectant By-Products on Protein Phosphatase 2A

The secondary contamination of microcystin disinfection by-products (MC-DBPs) is of concern due to the residual structure similar to their original toxin. Based on identification and preparation, the potential inhibition effect of typical MCLR-DBPs (associated with the oxidation of Adda⁵) on PP2A was confirmed in the sequence of MCLR > P1 > P4 > P3 ≈ P2 > P7 ≈ P6 ≈ P5 > P8. To elucidate the molecular mechanism underlying the inhibition effect, the interaction models for typical MCLR-DBPs and PP2A were constructed using a modeling-based-on-ligand-similarity approach, and the candidate interaction parameters between typical MCLR-DBPs and PP2A were obtained by molecular docking. By analyzing the correlation between inhibition data and candidate interaction parameters, the key interaction parameters were filtered as hydrogen bonds "Adda⁵"←Asn₁₁₇, "Adda⁵"←His₁₁₈, MeAsp³←Arg₈₉, Arg⁴←Arg₂₁₄, Arg⁴→Pro₂₁₃; ionic bonds Glu⁶-Arg₈₉, Asp₈₅-Mn₁²⁺, Asp₅₇-Mn₂²⁺; and metal bonds Glu⁶-Mn₁²⁺, Glu⁶-Mn₂²⁺. With the gradual intensification of chlorination, Adda⁵ was destroyed to varying degrees. The key interactions changed correspondingly, resulting in the discrepant inhibition effects of typical MCLR-DBPs on PP2A.

Combination of Docking-Based and Pharmacophore-Based Virtual Screening Identifies Novel Agonists That Target the Urotensin Receptor

The urotensin receptor (UT receptor), a G-protein-coupled receptor mediating urotensin-II and urotensin-II-related peptide signaling in the urotensinergic system, has multiple pharmacological activities. However, there is no drug targeting the UT receptor currently in clinical use, and the discovery of new leads is still important. The complete crystal structure of the UT receptor has not yet been resolved and a screening strategy combining multiple methods can improve the accuracy and efficiency of drug screening. This study aimed to identify novel UT receptor agonists using a combination of docking-based, pharmacophore-based, and cell-based drug screening. First, the three-dimensional structures of the UT receptor were constructed through single-template, multi-template homologous modeling and threading strategies. After structure evaluation and ligand enrichment analysis, a model from the threading modeling was selected for docking-based virtual screening based on stepwise filtering, and 1368 positive compounds were obtained from our compound library. Second, the pharmacophore models were constructed using known ligands targeting the UT receptor for pharmacophore-based virtual screening. A model was selected after model validation, and 300 positive compounds were retrieved. Then, after intersecting the results of two different virtual screening methods with 570 compound entities from our primary screening, 14 compounds were obtained. Finally, three hits were obtained after in vitro confirmation. Furthermore, preliminary evaluation of the hits showed that they influenced glucose consumption. In summary, by integrating docking-based, pharmacophore-based, and in vitro drug screening, three new agonists targeting the UT receptor were identified which may serve as promising therapeutic agents for urotensinergic system disorders.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Insight into the Molecular Mechanism for the Discrepant Inhibition of Microcystins (MCLR, LA, LF, LW, LY) on Protein Phosphatase 2A

Microcystins (MCs) exhibit diversified inhibition effects on protein phosphatases (PPs) due to their structural differences. To fully evaluate the potential mechanism for the discrepant inhibition effects, the five most frequent MCs with varying residues at position Z4 were selected as the tested toxins. Their inhibition sequence on PP2A was detected as follows: MCLR > MCLW > MCLA > MCLF > MCLY. Combined with homology modeling and molecular docking technology, the major interaction parameters between the MCs and PP2A were obtained. The correlation analysis for the major interaction parameters and inhibition effects showed that the hydrophobicity of Z4 had an important influence on the interaction of the MCs to PP2A. The introduction of hydrophobic Z4 directly weakened hydrogen bonds Z4→Pro213 and Z4←Arg214, indirectly weakened hydrogen bonds Adda5←Asn117, Glu6←Arg89, and MeAsp3←Arg89, but indirectly enhanced ionic bonds Glu6←Arg89, Glu6-Mn12+, and Glu6-Mn22+. In this way, the combination of the MCs with PP2A was blocked, and thus, the interactions between PP2A and the Mn2+ ions (in the catalytic center) were further affected; metal bonds Asp85-Mn12+ and Asp85-Mn22+ were weakened, while metal bond His241-Mn12+ was enhanced. As a result, the interactions in the catalytic center were inhibited to varying degrees, resulting in the reduced toxicity of MCs.

Multiple Molecular Dynamics Simulations and Free-Energy Predictions Uncover the Susceptibility of Variants of HIV-1 Protease against Inhibitors Darunavir and KNI-1657

HIV-1 protease (PR) is considered to be the main targets of anti-AIDS drug design because of its role in the proteolytic processing of viral polyproteins. However, the emergence of drug-resistant HIV has become a major problem in the therapy of HIV-1-infected patients. Focused on the complexes of wild type (WT) PR and two mutant PRs (V32I/L33F/I54M/V82I and V32I/L33F/I54M/I84 V) with inhibitors Darunavir (DRV) and KNI-1657 (KNI), respectively, we have conducted research on the conformational dynamics and the resistance mechanism caused by residue mutations through multiple molecular dynamics (MD) simulations combined with an energy (MM-PBSA and solvated interaction energy (SIE)) prediction. The results indicate that mutated residues of PR alter the distance between flap regions and catalytic sites, the volume of the inner catalytic site, and the curling degree of the flap tips, thereby affecting DRV and KNI inhibitor binding to PR. These mutated residues reduced the binding affinity of the two mutant PRs to DRV, resulting in drug resistance, whereas the two mutant PRs increase the binding affinity with KNI, indicating they enhance the sensitivity to KNI. Compared with the WT PR, the changes in van der Waals interaction and electrostatic interaction in the two variant PRs play a vital part in the binding of PR with DRV and KNI. These results may supply valuable guidance for the design of anti-AIDS drugs targeting PR.

Virtual Screening and Biological Activity Evaluation of New Potent Inhibitors Targeting LRRK2 Kinase Domain

Leucine rich repeat kinase 2 (LRRK2) has been reported in the pathogenesis of Parkinson's disease (PD). Inhibition of LRRK2 kinase activity is a therapeutic approach that may provide new treatments for PD. In this study, novel LRRK2 inhibitors were identified by performing a docking-based virtual screening (VS). Due to the absence of a crystal structure of LRRK2, homology modeling was adopted to model human LRRK2 kinase domain that binds the inhibitor. Next, a docking-based virtual screening protocol was applied to identify LRRK2 small molecule inhibitors targeting the ATP binding pocket. A total of 28 compounds were selected and subjected to LRRK2 kinase inhibition assay. As a result, two small molecules with novel skeleton, compounds LY2019-005 and LY2019-006, were identified as potential LRRK2 kinase inhibitors with the IC₅₀ of these two compounds against the wild-type and G2019S mutant LRRK2 kinase being 424.40 ± 1.31 nM, 378.80 ± 1.20 nM and 1526.00 ± 0.87 nM, 1165.00 ± 1.18 nM, respectively. Molecular dynamics (MD) simulation was carried out to reveal the binding mode of the newly identified compound LY2019-005 to the LRRK2 kinase domain. The binding modes indicate that the important hydrogen bond between hinge region (such as Ala1950) and inhibitor is crucial for the inhibition activity. In summary, our study provides a highly efficient way to discover LRRK2 inhibitors, and we find two highly efficient novel LRRK2 inhibitors, which could be helpful for the development of potential drugs targeting LRRK2 in PD therapy.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Indian Ethnomedicinal Phytochemicals as Promising Inhibitors of RNA-Binding Domain of SARS-CoV-2 Nucleocapsid Phosphoprotein: An In Silico Study

SARS-CoV-2, an etiological agent of COVID-19, has been the reason for the unexpected global pandemic, causing severe mortality and imposing devastative effects on public health. Despite extensive research work put forward by scientist around globe, so far, no suitable drug or vaccine (safe, affordable, and efficacious) has been identified to treat SARS-CoV-2. As an alternative way of improvising the COVID-19 treatment strategy, that is, strengthening of host immune system, a great deal of attention has been given to phytocompounds from medicinal herbs worldwide. In a similar fashion, the present study deliberately focuses on the phytochemicals of three Indian herbal medicinal plants viz., Mentha arvensis, Coriandrum sativum, and Ocimum sanctum for their efficacy to target well-recognized viral receptor protein through molecular docking and dynamic analyses. Nucleocapsid phosphoprotein (N) of SARS-CoV-2, being a pivotal player in replication, transcription, and viral genome assembly, has been recognized as one of the most attractive viral receptor protein targets for controlling the viral multiplication in the host. Out of 127 phytochemicals screened, nine (linarin, eudesmol, cadinene, geranyl acetate, alpha-thujene, germacrene A, kaempferol-3-O-glucuronide, kaempferide, and baicalin) were found to be phenomenal in terms of exhibiting high binding affinity toward the catalytic pocket of target N-protein. Further, the ADMET prediction analysis unveiled the non-tumorigenic, noncarcinogenic, nontoxic, non-mutagenic, and nonreproductive nature of the identified bioactive molecules. Furthermore, the data of molecular dynamic simulation validated the conformational and dynamic stability of the docked complexes. Concomitantly, the data of the present study validated the anti-COVID efficacy of the bioactives from selected medicinal plants of Indian origin.

Negative allosteric modulators of cannabinoid receptor 1: Ternary complexes including CB1, orthosteric CP55940 and allosteric ORG27569

In October 2019, the first X-ray crystal structure of a ternary cannabinoid receptor 1 (CB1) complex (PDB ID: 6KQI) was published, including the well-known orthosteric agonist, CP55940, and the well-studied negative allosteric modulator, ORG27569. Prior to the release of 6KQI, we applied binding pocket analysis and molecular docking to carefully prepared computational models of the ternary CB1 complex, in order to predict the binding site for ORG27569 with the CP55940-bound CB1 receptor. We carefully studied the binding pose of agonist ligands in the CB1 orthosteric pocket, including CP55940. Our computational studies identified the most favorable binding site for ORG27569, in the CP55940-CB1 complex, to be at the intracellular end of the receptor. However, in the 6KQI structure, ORG27569 was found at an extrahelical, intramembrane site on the complex, a site that partially overlaps with the site predicted in our calculations to be second-best. We performed molecular dynamics simulations of the CP55940-bound CB1 complex with ORG27569 at different binding sites. Our analysis of the simulations indicated that ORG27569 bound favorably and stably in each simulation, but, as in the earlier calculations, bound best at the intracellular site, which is different than that found in the crystal structure. These results suggest that the intracellular site might serve as an alternative binding site in CB1. Our studies show that the computational techniques we used are valuable in identifying ligand-binding pockets in proteins, and could be useful for the study of the interaction mode of other allosteric modulators.

Ligand- and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists

The α2a adrenoceptor is a medically relevant subtype of the G protein-coupled receptor family. Unfortunately, high-throughput techniques aimed at producing novel drug leads for this receptor have been largely unsuccessful because of the complex pharmacology of adrenergic receptors. As such, cutting-edge in silico ligand- and structure-based assessment and de novo deep learning methods are well positioned to provide new insights into protein-ligand interactions and potential active compounds. In this work, we (i) collect a dataset of α2a adrenoceptor agonists and provide it as a resource for the drug design community; (ii) use the dataset as a basis to generate candidate-active structures via deep learning; and (iii) apply computational ligand- and structure-based analysis techniques to gain new insights into α2a adrenoceptor agonists and assess the quality of the computer-generated compounds. We further describe how such assessment techniques can be applied to putative chemical probes with a case study involving proposed medetomidine-based probes.

Rapid Structure-Based Screening Informs Potential Agents for Coronavirus Disease (COVID-19) Outbreak

Coronavirus Disease 2019 (COVID-19), caused by the novel coronavirus, has spread rapidly across China. Consequently, there is an urgent need to sort and develop novel agents for the prevention and treatment of viral infections. A rapid structure-based virtual screening is used for the evaluation of current commercial drugs, with structures of human angiotensin converting enzyme II (ACE2), and viral main protease, spike, envelope, membrane and nucleocapsid proteins. Our results reveal that the reported drugs Arbidol, Chloroquine and Remdesivir may hinder the entry and release of virions through the bindings with ACE2, spike and envelope proteins. Due to the similar binding patterns, NHC (β-d-N4-hydroxycytidine) and Triazavirin are also in prospects for clinical use. Main protease (3CLpro) is likely to be a feasible target of drug design. The screening results to target 3CL-pro reveal that Mitoguazone, Metformin, Biguanide Hydrochloride, Gallic acid, Caffeic acid, Sulfaguanidine and Acetylcysteine seem be possible inhibitors and have potential application in the clinical therapy of COVID-19.

Computational Investigations on the Binding Mode of Ligands for the Cannabinoid-Activated G Protein-Coupled Receptor GPR18

GPR18 is an orphan G protein-coupled receptor (GPCR) expressed in cells of the immune system. It is activated by the cannabinoid receptor (CB) agonist ∆⁹-tetrahydrocannabinol (THC). Several further lipids have been proposed to act as GPR18 agonists, but these results still require unambiguous confirmation. In the present study, we constructed a homology model of the human GPR18 based on an ensemble of three GPCR crystal structures to investigate the binding modes of the agonist THC and the recently reported antagonists which feature an imidazothiazinone core to which a (substituted) phenyl ring is connected via a lipophilic linker. Docking and molecular dynamics simulation studies were performed. As a result, a hydrophobic binding pocket is predicted to accommodate the imidazothiazinone core, while the terminal phenyl ring projects towards an aromatic pocket. Hydrophobic interaction of Cys251 with substituents on the phenyl ring could explain the high potency of the most potent derivatives. Molecular dynamics simulation studies suggest that the binding of imidazothiazinone antagonists stabilizes transmembrane regions TM1, TM6 and TM7 of the receptor through a salt bridge between Asp118 and Lys133. The agonist THC is presumed to bind differently to GPR18 than to the distantly related CB receptors. This study provides insights into the binding mode of GPR18 agonists and antagonists which will facilitate future drug design for this promising potential drug target.

Exploring the Activation Mechanism of a Metabotropic Glutamate Receptor Homodimer via Molecular Dynamics Simulation

Metabotropic glutamate receptors of class C GPCRs exist as constitutive dimers, which play important roles in activating excitatory synapses of the central nervous system. However, the activation mechanism induced by agonists has not been clarified in experiments. To address the problem, we used microsecond all-atom molecular dynamics (MD) simulation couple with protein structure network (PSN) to explore the glutamate-induced activation for the mGluR1 homodimer. The results indicate that glutamate binding stabilizes not only the closure of Venus flytrap domains but also the polar interaction of LB2-LB2, in turn keeping the extracelluar domain in the active state. The activation of the extracelluar domain drives transmembrane domains (TMDs) of the two protomers closer and induces asymmetric activation for the TMD domains of the two protomers. One protomer with lower binding affinity to the agonist is activated, while the other protomer with higher binding energy is still in the inactive state. The PSN analysis identifies the allosteric regulation pathway from the ligand-binding pocket in the extracellular domain to the G-protein binding site in the intracellular TMD region and further reveals that the asymmetric activation is attributed to a combination of trans-pathway and cis-pathway regulations from two glumatates, rather than a single activation pathway. These observations could provide valuable molecular information for understanding of the structure and the implications in drug efficacy for the class C GPCR dimers.

Revealing the binding and drug resistance mechanism of amprenavir, indinavir, ritonavir, and nelfinavir complexed with HIV-1 protease due to double mutations G48T/L89M by molecular dynamics simulations and free energy analyses

MD simulations, MM-PBSA, and SIE analyses were used to investigate the drug resistance mechanisms of two mutations G48T and L89M in HIV-1 protease toward four inhibitors.

An Integrated Pan-Cancer Analysis and Structure-Based Virtual Screening of GPR15

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan G protein-coupled receptors (GPCRs) involving human immunodeficiency virus (HIV) infection, colonic inflammation, and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability to inhibit cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ) than in normal tissues. Among 33 cancer types, GPR15 expression was significantly positively correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), and lung adenocarcinoma (LUAD) and significantly negatively correlated with stomach adenocarcinoma (STAD). This study also revealed that commonly upregulated gene sets in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD, and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted structure-based virtual screening. The top eight hit compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provides novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.

Investigation of ECD conformational transition mechanism of GLP-1R by molecular dynamics simulations and Markov state model

As a member of the class B G protein-coupled receptors (GPCRs), the glucagon-like peptide-1 (GLP-1) can regulate the blood glucose level by binding to the glucagon-like peptide-1 receptor (GLP-1R). Since the extracellular domain (ECD) of GLP-1R is considered as one of the binding sites of GLP-1, the open and closed states of ECD play an important role in the binding process of GLP-1. To investigate the transition path of GLP-1R ECD, the crystal structures of GLP-1R in its bound and unbound states (apo-state) are chosen to perform a total of 1.6 μs of molecular dynamics simulations. The simulated results show that the ECD of GLP-1R closes in the GLP-1 bound state and opens in the GLP-1 unbound state. To determine the critical role that GLP-1 played in regulating the open and closed states of the ECD, we applied the independent gradient model (IGM) to the simulation trajectories. We found that the "hand-like" N-terminal of the GLP-1R ECD plays an important role in the GLP-1 binding. In contrast, the apo-state GLP-1R ECD opens and exposes the two ligand binding domains of GLP-1 after 200 ns of simulations. To elucidate the open and closed mechanisms of GLP-1R ECD in the apo-state and GLP-1 bound state, the Markov state model (MSM) is performed on the MD simulation trajectories. Our results provide possible transition pathways from the closed state to open state of the apo-state GLP-1R ECD. Each pathway contains several intermediate states that correspond to different local minima in deep wells. The dynamical relationships and the most possible conversion pathway between two states are detailed through the MSM analysis. Our results profile the conformation transition mechanism of the GLP-1R ECD and will help in hypoglycemic peptide design of GLP-1R.

Applications of machine learning in GPCR bioactive ligand discovery

GPCRs constitute the largest druggable family having targets for 475 Food and Drug Administration (FDA) approved drugs. As GPCRs are of great interest to pharmaceutical industry, enormous efforts are being expended to find relevant and potent GPCR ligands as lead compounds. There are tens of millions of compounds present in different chemical databases. In order to scan this immense chemical space, computational methods, especially machine learning (ML) methods, are essential components of GPCR drug discovery pipelines. ML approaches have applications in both ligand-based and structure-based virtual screening. We present here a cheminformatics overview of ML applications to different stages of GPCR drug discovery. Focusing on olfactory receptors, which are the largest family of GPCRs, a case study for predicting agonists for an ectopic olfactory receptor, OR1G1, compares four classical ML methods.

Molecular Basis of Mammalian Odor Discrimination: A Status Report

Humans have 396 unique, intact olfactory receptors (ORs), G-protein coupled receptors (GPCRs) containing receptor-specific binding sites; other mammals have more. Activation of these transmembrane proteins by an odorant initiates a signaling cascade, evoking an action potential leading to perception of a smell. Because the number of distinguishable odorants vastly exceeds the number of ORs, research has focused on mechanisms of recognition and signaling processes for classes of odorants. In this review, selected recent examples will be presented of "deorphaned" mammalian receptors, where the OR ligands (odorants) as well as key aspects of receptor-odorant interactions were identified using odorant-mediated receptor activation data together with site-directed mutagenesis and molecular modeling. Based on cumulative evidence from OR deorphaning and olfactory receptor neuron activation studies, a receptor-ligand docking model rather than an alternative bond vibration model is suggested to best explain the molecular basis of the exquisitely sensitive odor discrimination in mammals.