Virtual Screening of Human Class-A GPCRs Using Ligand Profiles Built on Multiple Ligand-Receptor Interactions

ERNEST COST action overview on the (patho)physiology of GPCRs and orphan GPCRs in the nervous system

G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that play a critical role in nervous system function by transmitting signals between cells and their environment. They are involved in many, if not all, nervous system processes, and their dysfunction has been linked to various neurological disorders representing important drug targets. This overview emphasises the GPCRs of the nervous system, which are the research focus of the members of ERNEST COST action (CA18133) working group 'Biological roles of signal transduction'. First, the (patho)physiological role of the nervous system GPCRs in the modulation of synapse function is discussed. We then debate the (patho)physiology and pharmacology of opioid, acetylcholine, chemokine, melatonin and adhesion GPCRs in the nervous system. Finally, we address the orphan GPCRs, their implication in the nervous system function and disease, and the challenges that need to be addressed to deorphanize them.

Machine learning-assisted search for novel coagulants: When machine learning can be efficient even if data availability is low

Design of new drugs is a challenging process: a candidate molecule should satisfy multiple conditions to act properly and make the least side-effect-perfect candidates selectively attach to and influence only targets, leaving off-targets intact. The amount of experimental data about various properties of molecules constantly grows, promoting data-driven approaches. However, the applicability of typical predictive machine learning techniques can be substantially limited by a lack of experimental data about a particular target. For example, there are many known Thrombin inhibitors (acting as anticoagulants), but a very limited number of known Protein C inhibitors (coagulants). In this study, we present our approach to suggest new inhibitor candidates by building an effective representation of chemical space. For this aim, we developed a deep learning model-autoencoder, trained on a large set of molecules in the SMILES format to map the chemical space. Further, we applied different sampling strategies to generate novel coagulant candidates. Symmetrically, we tested our approach on anticoagulant candidates, where we were able to predict their inhibition towards Thrombin. We also compare our approach with MegaMolBART-another deep learning generative model, but exploiting similar principles of navigation in a chemical space.

Opsin expression varies across larval development and taxa in pteriomorphian bivalves

Introduction

Many marine organisms have a biphasic life cycle that transitions between a swimming larva with a more sedentary adult form. At the end of the first phase, larvae must identify suitable sites to settle and undergo a dramatic morphological change. Environmental factors, including photic and chemical cues, appear to influence settlement, but the sensory receptors involved are largely unknown. We targeted the protein receptor, opsin, which belongs to large superfamily of transmembrane receptors that detects environmental stimuli, hormones, and neurotransmitters. While opsins are well-known for light-sensing, including vision, a growing number of studies have demonstrated light-independent functions. We therefore examined opsin expression in the Pteriomorphia, a large, diverse clade of marine bivalves, that includes commercially important species, such as oysters, mussels, and scallops.

Methods

Genomic annotations combined with phylogenetic analysis show great variation of opsin abundance among pteriomorphian bivalves, including surprisingly high genomic abundance in many species that are eyeless as adults, such as mussels. Therefore, we investigated the diversity of opsin expression from the perspective of larval development. We collected opsin gene expression in four families of Pteriomorphia, across three distinct larval stages, i.e., trochophore, veliger, and pediveliger, and compared those to adult tissues.

Results

We found larvae express all opsin types in these bivalves, but opsin expression patterns are largely species-specific across development. Few opsins are expressed in the adult mantle, but many are highly expressed in adult eyes. Intriguingly, opsin genes such as retinochrome, xenopsins, and Go-opsins have higher levels of expression in the later larval stages when substrates for settlement are being tested, such as the pediveliger.

Conclusion

Investigating opsin gene expression during larval development provides crucial insights into their intricate interactions with the surroundings, which may shed light on how opsin receptors of these organisms respond to various environmental cues that play a pivotal role in their settlement process.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation

G-protein-coupled receptors (GPCRs) mediate diverse cell signaling cascades after recognizing extracellular ligands. Despite the successful history of known GPCR drugs, a lack of mechanistic insight into GPCR challenges both the deorphanization of some GPCRs and optimization of the structure-activity relationship of their ligands. Notably, replacing a small substituent on a GPCR ligand can significantly alter extracellular GPCR-ligand interaction patterns and motion of transmembrane helices in turn to occur post-binding events of the ligand. In this study, we designed 3D multilevel features to describe the extracellular interaction patterns. Subsequently, these 3D features were utilized to predict the post-binding events that result from conformational dynamics from the extracellular to intracellular areas. To understand the adaptability of GPCR ligands, we collected the conformational information of flexible residues during binding and performed molecular featurization on a broad range of GPCR-ligand complexes. As a result, we developed GPCR-ligand interaction patterns, binding pockets, and ligand features as score (GPCR-IPL score) for predicting the functional selectivity of GPCR ligands (agonism versus antagonism), using the multilevel features of (1) zoomed-out 'residue level' (for flexible transmembrane helices of GPCRs), (2) zoomed-in 'pocket level' (for sophisticated mode of action) and (3) 'atom level' (for the conformational adaptability of GPCR ligands). GPCR-IPL score demonstrated reliable performance, achieving area under the receiver operating characteristic of 0.938 and area under the precision-recall curve of 0.907 (available in gpcr-ipl-score.onrender.com). Furthermore, we used the molecular features to predict the biased activation of downstream signaling (Gi/o, Gq/11, Gs and β-arrestin) as well as the functional selectivity. The resulting models are interpreted and applied to out-of-set validation with three scenarios including the identification of a new MRGPRX antagonist.

Application of Mixed-Solvent Molecular Dynamics Simulations for Prediction of Allosteric Sites on G Protein-Coupled Receptors

The development of small molecule allosteric modulators acting at G protein-coupled receptors (GPCRs) is becoming increasingly attractive. Such compounds have advantages over traditional drugs acting at orthosteric sites on these receptors, in particular target specificity. However, the number and locations of druggable allosteric sites within most clinically relevant GPCRs are unknown. In the present study, we describe the development and application of a mixed-solvent molecular dynamics (MixMD)-based method for the identification of allosteric sites on GPCRs. The method employs small organic probes with druglike qualities to identify druggable hotspots in multiple replicate short-timescale simulations. As proof of principle, we first applied the method retrospectively to a test set of five GPCRs (cannabinoid receptor type 1, C-C chemokine receptor type 2, M2 muscarinic receptor, P2Y purinoceptor 1, and protease-activated receptor 2) with known allosteric sites in diverse locations. This resulted in the identification of the known allosteric sites on these receptors. We then applied the method to the μ-opioid receptor. Several allosteric modulators for this receptor are known, although the binding sites for these modulators are not known. The MixMD-based method revealed several potential allosteric sites on the mu-opioid receptor. Implementation of the MixMD-based method should aid future efforts in the structure-based drug design of drugs targeting allosteric sites on GPCRs. SIGNIFICANCE STATEMENT: Allosteric modulation of G protein-coupled receptors (GPCRs) has the potential to provide more selective drugs. However, there are limited structures of GPCRs bound to allosteric modulators, and obtaining such structures is problematic. Current computational methods utilize static structures and therefore may not identify hidden or cryptic sites. Here we describe the use of small organic probes and molecular dynamics to identify druggable allosteric hotspots on GPCRs. The results reinforce the importance of protein dynamics in allosteric site identification.

Thermodynamic architecture and conformational plasticity of GPCRs

G-protein-coupled receptors (GPCRs) are ubiquitous integral membrane proteins involved in diverse cellular signaling processes. Here, we carry out a large-scale ensemble thermodynamic study of 45 ligand-free GPCRs employing a structure-based statistical mechanical framework. We find that multiple partially structured states co-exist in the GPCR native ensemble, with the TM helices 1, 6 and 7 displaying varied folding status, and shaping the conformational landscape. Strongly coupled residues are anisotropically distributed, accounting for only 13% of the residues, illustrating that a large number of residues are inherently dynamic. Active-state GPCRs are characterized by reduced conformational heterogeneity with altered coupling-patterns distributed throughout the structural scaffold. In silico alanine-scanning mutagenesis reveals that extra- and intra-cellular faces of GPCRs are coupled thermodynamically, highlighting an exquisite structural specialization and the fluid nature of the intramolecular interaction network. The ensemble-based perturbation methodology presented here lays the foundation for understanding allosteric mechanisms and the effects of disease-causing mutations in GCPRs.

Kinetics of Drug Molecule Interactions with a Newly Developed Nano-Gold-Modified Spike Protein Electrochemical Receptor Sensor

In March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic, and the spike protein has been reported to be an important drug target for anti-COVID-19 treatment. As such, in this study, we successfully developed a novel electrochemical receptor biosensor by immobilizing the SARS-CoV-2 spike protein and using AuNPs-HRP as an electrochemical signal amplification system. Moreover, the time-current method was used to quantify seven antiviral drug compounds, such as arbidol and chloroquine diphosphate. The results show that the spike protein and the drugs are linearly correlated within a certain concentration range and that the detection sensitivity of the sensor is extremely high. In the low concentration range of linear response, the kinetics of receptor-ligand interactions are similar to that of an enzymatic reaction. Among the investigated drug molecules, bromhexine exhibits the smallest Ka value, and thus, is most sensitively detected by the sensor. Hydroxychloroquine exhibits the largest Ka value. Molecular docking simulations of the spike protein with six small-molecule drugs show that residues of this protein, such as Asp, Trp, Asn, and Gln, form hydrogen bonds with the -OH or -NH₂ groups on the branched chains of small-molecule drugs. The electrochemical receptor biosensor can directly quantify the interaction between the spike protein and drugs such as abidor and hydroxychloroquine and perform kinetic studies with a limit of detection 3.3 × 10^-20 mol/L, which provides a new research method and idea for receptor-ligand interactions and pharmacodynamic evaluation.

Binding site identification of G protein-coupled receptors through a 3D Zernike polynomials-based method: application to C. elegans olfactory receptors

Studying the binding processes of G protein-coupled receptors (GPCRs) proteins is of particular interest both to better understand the molecular mechanisms that regulate the signaling between the extracellular and intracellular environment and for drug design purposes. In this study, we propose a new computational approach for the identification of the binding site for a specific ligand on a GPCR. The method is based on the Zernike polynomials and performs the ligand-GPCR association through a shape complementarity analysis of the local molecular surfaces. The method is parameter-free and it can distinguish, working on hundreds of experimentally GPCR-ligand complexes, binding pockets from randomly sampled regions on the receptor surface, obtaining an Area Under ROC curve of 0.77. Given its importance both as a model organism and in terms of applications, we thus investigated the olfactory receptors of the C. elegans, building a list of associations between 21 GPCRs belonging to its olfactory neurons and a set of possible ligands. Thus, we can not only carry out rapid and efficient screenings of drugs proposed for GPCRs, key targets in many pathologies, but also we laid the groundwork for computational mutagenesis processes, aimed at increasing or decreasing the binding affinity between ligands and receptors.

Ligand Docking Methods to Recognize Allosteric Inhibitors for G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) are membrane proteins which play an important role in many cellular processes and are excellent drug targets. Despite the existence of several US Food and Drug Administration (FDA)-approved GPCR-targeting drugs, there is a continuing challenge of side effects owing to the nonspecific nature of drug binding. We have investigated the diversity of the ligand binding site for this class of proteins against their cognate ligands using computational docking, even if their structures are known already in the ligand-complexed form. The cognate ligand of some of these receptors dock at allosteric binding site with better score than the binding at the conservative site. Interestingly, amino acid residues at such allosteric binding site are not conserved across GPCR subfamilies. Such a computational approach can assist in the prediction of specific allosteric binders for GPCRs.

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

Tropane alkaloids and terpenes synthase genes of Datura stramonium (Solanaceae)

Background

Plants have evolved physical–chemical defense to prevent/diminish damage by their enemies. Chemical defense involves the synthesis’ pathways of specialized toxic, repellent, or anti-nutritive metabolites to herbivores. Molecular evolutionary studies have revealed the origin of new genes, acquisition and functional diversification along time in different plant lineages.

Methods

Using bioinformatic tools we analyze gene divergence of tropane alkaloids (TAs) and terpene synthases (TPSs) in Datura stramonium and other species of Solanaceae; compared gene and amino acids sequence of TAs and TPSs on genomes, cDNA and proteins sequences of Viridiplantae. We analyzed two recently assembled genomes of D. stramonium (Ticumán and Teotihuacán), transcriptomes of Datura metel and genomes of other Solanaceae. Hence, we analyzed variation of TAs and TPSs to infer genes involved in plant defense and plant responses before stress. We analyzed protein modeling and molecular docking to predict interactions between H6H and ligand; we translated the sequences (Teo19488, Tic8550 and Tic8549) obtained from the two genomes of D. stramonium by using Swiss-Model and Ramachandran plot and MolProbity structure validation of Teo19488 protein model.

Results

For TAs, we detected an expansion event in the tropinone reductase II (TRII) and the ratio synonymous/non-synonymous substitutions indicate positive selection. In contrast, a contraction event and negative selection was detected in tropinone reductase I (TRI). In Hy-oscyamine 6 b-hydroxylase (H6H), enzyme involved in the production of tropane alkaloids atropine and scopolamine, the synonymous/non-synonymous substitution ratio in its dominion indicates positive selection. For terpenes (TPS), we found 18 DsTPS in D. stramomiun and seven in D. metel; evolutionary analyses detected positive selection in TPS10.1 and TPS10.2 of D. stramonium and D. metel. Comparison of copies of TPSs in D. stramonium detected variation among them in the binding site. Duplication events and differentiation of TAs and TPSs of D. stramonium, as compared to other Solanaceae, suggest their possible involvement on adaptive evolution of defense to herbivores. Protein modeling and docking show that the three protein structures obtained of DsH6H from Teo19488, Tic-8550 and Tic8549 maintain the same interactions and the union site of 2OG-FeII_Oxy with the Hy-o ligand as in 6TTM of D. metel.

Conclusion

Our results indicate differences in the number of gene copies involved in the synthesis of tropane alkaloids, between the genomes of D. stramonium from two Mexican populations. More copies of genes related to the synthesis of tropane alkaloids (TRI, TRII, H6H, PMT) are found in D. stramonium as compared to Viridiplantae. Likewise, for terpene synthases (TPS), TPS-10 is duplicated in D. stramonium and D. metel. Further studies should be directed to experimentally assess gain (overexpression) or loss (silencing) of function of duplicated genes.

Chemogenomic approach to identifying nematode chemoreceptor drug targets in the entomopathogenic nematode Heterorhabditis bacteriophora

Parasitic nematodes constitute one of the major threats to human health, causing diseases of major socioeconomic importance worldwide. Recent estimates indicate that more than 1 billion people are infected with parasitic nematodes around the world. Current measures to combat parasitic nematode infections include anthelmintic drugs. However, heavy exposure to anthelmintics has selected populations of livestock parasitic nematodes that are no longer susceptible to the drugs, rendering several anthelmintics useless for parasitic nematode control in many areas of the world. The rapidity with which anthelmintic resistance developed in response to these drugs suggests that increasing the selective pressure on human parasitic nematodes will also rapidly generate resistant worm populations. Therefore, development of new anthelmintics is of major importance before resistance becomes widespread in human parasitic nematode populations. G-Protein Coupled Receptors (GPCRs) represent an important target for many pharmacological interventions due to their ubiquitous expression in various cell types. GPCRs contribute to numerous physiological processes, and their ligand binding sites located on cell surfaces make them accessible targets and attractive substrates in terms of druggability. In fact, ∼35 % of Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved drugs target GPCRs and their associated proteins, with over 300 additional drugs targeting GPCRs at the clinical trial stage. Nematode Chemosensory GPCRs (NemChRs) are unique to nematodes, and therefore represent ideal substrates for target-based drug discovery. Here we set out to identify NemChRs that are transcriptionally active inside the host, and to use these NemChRs in a reverse pharmacological screen to impede parasitic development. Our data identified several NemChRs, and we focused on one that was expressed in neuronal cells and exhibited the highest fold change in transcription after host activation. Next, we performed homology modelling and molecular dynamics simulations of this NemChR in order to conduct a virtual screening campaign to identify candidate drug targets which were ranked and selected for experimental testing in bioassays. Taken together, our results identify and characterize a candidate NemChR drug target, and provide a chemogenomic pipeline for identifying nematicide substrates.