Homology modeling of human muscarinic acetylcholine receptors

How good are AlphaFold models for docking-based virtual screening?

A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

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.

Selectivity mechanism of muscarinic acetylcholine receptor antagonism through in silico investigation

Structures of muscarinic acetylcholine receptors illustrate the strikingly high degree of homology of the residues among isoforms, thus leading to difficulty in achieving subtype selectivity when targeting these receptors and causing undesired side effects when treating the corresponding diseases. Considering the urgent need for more selective and potency therapies, this study is aimed at revealing the selectivity mechanism against M4/5 via in silico strategies, revealing crucial molecular interactions such as hydrogen bonds and pi-cation interaction formed between the key residues TYR416, ASN417, and TRP435 of M4, respectively, hydrophobic pocket formed by the key residues, especially CYS484 of M5. Besides, the water around TYR416^M4 and ASN459^M5, which can be replaced by substituent groups which can form the hydrogen bond interaction network by simulated bridging water and the water around ASP112^M4, whose replacement maybe not contribute to the increase in binding affinity of the compound, may affect the inhibitory selectivity among M4/5 in the aspect of the solvent. Moreover, from the point of inhibitors, compounds with a positively ionizable group could selectively bind to M4 receptors, while hydrophobic molecules may bind preferably to M5. We believe that the current study would provide a basis for the design of subsequent M4/5 selective antagonists.

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.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state.

G protein-coupled receptors (GPCRs) are a major target for drug discovery. These receptors are highly dynamic membrane proteins, and have had limited tractability using with biophysical screens that are widely adopted for globular protein targets. Thus, structure-based virtual screening (SBVS) holds great promise as a complement to physical screening for rational design of novel drugs. Indeed, the increasing number of atomic-detail GPCR X-ray crystal structures has coincided with an increase in prospective SBVS studies that have identified novel compounds. However, experimentally solved GPCR structures do not meet the full demand for SBVS, as the GPCR structural landscape is incomplete, lacking both in coverage of available GPCRs, and diversity in both receptor conformations and the chemistry of co-crystalised ligands. Here we present a novel computational GPCR binding pocket refinement method that can generate predictive GPCR/ligand complexes with improved SBVS performance. This ligand-directed modeling workflow uses parallel processing and efficient algorithms to search the GPCR/ligand conformational space faster and more efficiently than the widely used protein refinement method molecular dynamics. In this study, the resulting models are evaluated both structurally, and in retrospective SBVS. We demonstrate improved performance of refined models over their starting structures in the majority of our test cases.

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation.

Ligand binding modes from low resolution GPCR models and mutagenesis: chicken bitter taste receptor as a test-case

Bitter taste is one of the basic taste modalities, warning against consuming potential poisons. Bitter compounds activate members of the bitter taste receptor (Tas2r) subfamily of G protein-coupled receptors (GPCRs). The number of functional Tas2rs is species-dependent. Chickens represent an intriguing minimalistic model, because they detect the bitter taste of structurally different molecules with merely three bitter taste receptor subtypes. We investigated the binding modes of several known agonists of a representative chicken bitter taste receptor, ggTas2r1. Because of low sequence similarity between ggTas2r1 and crystallized GPCRs (~10% identity, ~30% similarity at most), the combination of computational approaches with site-directed mutagenesis was used to characterize the agonist-bound conformation of ggTas2r1 binding site between TMs 3, 5, 6 and 7. We found that the ligand interactions with N93 in TM3 and/or N247 in TM5, combined with hydrophobic contacts, are typically involved in agonist recognition. Next, the ggTas2r1 structural model was successfully used to identify three quinine analogues (epiquinidine, ethylhydrocupreine, quinidine) as new ggTas2r1 agonists. The integrated approach validated here may be applicable to additional cases where the sequence identity of the GPCR of interest and the existing experimental structures is low.

Using molecular dynamics for the refinement of atomistic models of GPCRs by homology modeling

Despite GPCRs sharing a common seven helix bundle, analysis of the diverse crystallographic structures available reveal specific features that might be relevant for ligand design. Despite the number of crystallographic structures of GPCRs steadily increasing, there are still challenges that hamper the availability of new structures. In the absence of a crystallographic structure, homology modeling remains one of the important techniques for constructing 3D models of proteins. In the present study we investigated the use of molecular dynamics simulations for the refinement of GPCRs models constructed by homology modeling. Specifically, we investigated the relevance of template selection, ligand inclusion as well as the length of the simulation on the quality of the GPCRs models constructed. For this purpose we chose the crystallographic structure of the rat muscarinic M3 receptor as reference and constructed diverse atomistic models by homology modeling, using different templates. Specifically, templates used in the present work include the human muscarinic M2; the more distant human histamine H1 and the even more distant bovine rhodopsin as shown in the GPCRs phylogenetic tree. We also investigated the use or not of a ligand in the refinement process. Hence, we conducted the refinement process of the M3 model using the M2 muscarinic as template with tiotropium or NMS docked in the orthosteric site and compared with the results obtained with a model refined without any ligand bound.

Ligand Binding Pathways of Clozapine and Haloperidol in the Dopamine D2 and D3 Receptors

The binding of a small molecule ligand to its protein target is most often characterized by binding affinity and is typically viewed as an on/off switch. The more complex reality is that binding involves the ligand passing through a series of intermediate states between the solution phase and the fully bound pose. We have performed a set of 29 unbiased molecular dynamics simulations to model the binding pathways of the dopamine receptor antagonists clozapine and haloperidol binding to the D2 and D3 dopamine receptors. Through these simulations we have captured the binding pathways of clozapine and haloperidol from the extracellular vestibule to the orthosteric binding site and thereby, we also predict the bound pose of each ligand. These are the first long time scale simulations of haloperidol or clozapine binding to dopamine receptors. From these simulations, we have identified several important stages in the binding pathway, including the involvement of Tyr7.35 in a "handover" mechanism that transfers the ligand between the extracellular vestibule and Asp3.32. We have also performed interaction and cluster analyses to determine differences in binding pathways between the D2 and D3 receptors and identified metastable states that may be of use in drug design.

Towards predictive docking at aminergic G-protein coupled receptors

G protein-coupled receptors (GPCRs) are hard to crystallize. However, attempts to predict their structure have boomed as a result of advancements in crystallographic techniques. This trend has allowed computer-aided molecular modeling of GPCRs. We analyzed the performance of four molecular modeling programs in pose evaluation of re-docked antagonists / inverse agonists to 11 original crystal structures of aminergic GPCRs using an induced fit-docking procedure. AutoDock and Glide were used for docking. AutoDock binding energy function, GlideXP, Prime MM-GB/SA, and YASARA binding function were used for pose scoring. Root mean square deviation (RMSD) of the best pose ranged from 0.09 to 1.58 Å, and median RMSD of the top 60 poses ranged from 1.47 to 3.83 Å. However, RMSD of the top pose ranged from 0.13 to 7.33 Å and ranking of the best pose ranged from the 1st to 60th out of 60 poses. Moreover, analysis of ligand-receptor interactions of top poses revealed substantial differences from interactions found in crystallographic structures. Bad ranking of top poses and discrepancies between top docked poses and crystal structures render current simple docking methods unsuitable for predictive modeling of receptor-ligand interactions. Prime MM-GB/SA optimized for 3NY9 by multiple linear regression did not work well at 3NY8 and 3NYA, structures of the same receptor with different ligands. However, 9 of 11 trajectories of molecular dynamics simulations by Desmond of top poses converged with trajectories of crystal structures. Key interactions were properly detected for all structures. This procedure also worked well for cross-docking of tested β2-adrenergic antagonists. Thus, this procedure represents a possible way to predict interactions of antagonists with aminergic GPCRs.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Docking and Virtual Screening Strategies for GPCR Drug Discovery

Progress in structure determination of G protein-coupled receptors (GPCRs) has made it possible to apply structure-based drug design (SBDD) methods to this pharmaceutically important target class. The quality of GPCR structures available for SBDD projects fall on a spectrum ranging from high resolution crystal structures (<2 Å), where all water molecules in the binding pocket are resolved, to lower resolution (>3 Å) where some protein residues are not resolved, and finally to homology models that are built using distantly related templates. Each GPCR project involves a distinct set of opportunities and challenges, and requires different approaches to model the interaction between the receptor and the ligands. In this review we will discuss docking and virtual screening to GPCRs, and highlight several refinement and post-processing steps that can be used to improve the accuracy of these calculations. Several examples are discussed that illustrate specific steps that can be taken to improve upon the docking and virtual screening accuracy. While GPCRs are a unique target class, many of the methods and strategies outlined in this review are general and therefore applicable to other protein families.

Expanding the horizons of G protein-coupled receptor structure-based ligand discovery and optimization using homology models

We show the key role of structural homology models in GPCR structure-based lead discovery and optimization, highlighting methodological aspects, recent progress and future directions.

Toward activated homology models of the human M1 muscarinic acetylcholine receptor

Structure-based virtual screening offers a good opportunity for the discovery of selective M1 muscarinic acetylcholine receptor (mAChR) agonists for the treatment of Alzheimer's disease. However, no 3-D structure of an M1 mAChR is yet available and the homology models that have been previously reported are only able to identify antagonists in virtual screening experiments. In this study, we generated a homology model of the human M1 mAChR, based on the crystal structure of an M3 mAChR as the template. This initial model was modified, using the agonist-bound crystal structure of a β2-adrenergic receptor as a guide, to give two possible activated structures. The T192 side chain was adjusted in both structures and one of the structures also had the whole of transmembrane (TM) 5 rotated and tilted toward the inner channel of the transmembrane region. The binding sites of all three structures were then refined by induced-fit docking (IFD) with acetylcholine. Virtual screening experiments showed that all three refined models could efficiently differentiate agonists from decoy molecules, with the TM5-modified models also giving good agonist/antagonist selectivity. The whole range of agonists and antagonists was observed to bind within the orthosteric site of the structure obtained by IFD refinement alone, implying that it has inactive state character. In contrast, the two TM5-modified structures were unable to accommodate the antagonists, supporting the proposition that they possess activated state character.