From Homology Models to a Set of Predictive Binding Pockets-a 5-HT1A Receptor Case Study

Discovery of pyrazolopyrrolidinones as potent, broad-spectrum inhibitors of Leishmania infection

Introduction

Leishmaniasis is a parasitic disease that affects more than 1 million people worldwide annually, predominantly in resource-limited settings. The challenge in compound development is to exhibit potent activity against the intracellular stage of the parasite (the stage present in the mammalian host) without harming the infected host cells. We have identified a compound series (pyrazolopyrrolidinones) active against the intracellular parasites of Leishmania donovani and L. major; the causative agents of visceral and cutaneous leishmaniasis in the Old World, respectively.

Methods

In this study, we performed medicinal chemistry on a newly discovered antileishmanial chemotype, with over 100 analogs tested. Studies included assessments of antileishmanial potency, toxicity towards host cells, and in vitro ADME screening of key drug properties.

Results and discussion

Members of the series showed high potency against the deadliest form, visceral leishmaniasis (approximate EC₅₀ ≥ 0.01 μM without harming the host macrophage up to 10.0 μM). In comparison, the most efficient monotherapy treatment for visceral leishmaniasis is amphotericin B, which presents similar activity in the same assay (EC₅₀ = 0.2 μM) while being cytotoxic to the host cell at 5.0 μM. Continued development of this compound series with the Discovery Partnership with Academia (DPAc) program at the GlaxoSmithKline Diseases of the Developing World (GSK DDW) laboratories found that the compounds passed all of GSK's criteria to be defined as a potential lead drug series for leishmaniasis.

Conclusion

Here, we describe preliminary structure-activity relationships for antileishmanial pyrazolopyrrolidinones, and our progress towards the identification of candidates for future in vivo assays in models of visceral and cutaneous leishmaniasis.

Homology Modeling of Transporter Proteins

Membrane transporter proteins are divided into channels/pores and carriers and constitute protein families of physiological and pharmacological importance. Several presently used therapeutic compounds elucidate their effects by targeting membrane transporter proteins, including anti-arrhythmic, anesthetic, antidepressant, anxiolytic and diuretic drugs. The lack of three-dimensional structures of human transporters hampers experimental studies and drug discovery. In this chapter, the use of homology modeling for generating structural models of membrane transporter proteins is reviewed. The increasing number of atomic resolution structures available as templates, together with improvements in methods and algorithms for sequence alignments, secondary structure predictions, and model generation, in addition to the increase in computational power have increased the applicability of homology modeling for generating structural models of transporter proteins. Different pitfalls and hints for template selection, multiple-sequence alignments, generation and optimization, validation of the models, and the use of transporter homology models for structure-based virtual ligand screening are discussed.

Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design

Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least 2 decades in various fields of cheminformatics, from similarity searching to machine learning (ML). Advances in in silico techniques consequently led to combining both these methodologies into a new approach known as the pharmacophore fingerprint. Herein, we propose a high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes the presence, types, and relationships between pharmacophore features of a molecule. Pharmacoprint was evaluated in classification experiments by using ML algorithms (logistic regression, support vector machines, linear support vector machines, and neural networks) and outperformed other popular molecular fingerprints (i.e., ECFP4, Estate, MACCS, PubChem, Substructure, Klekota-Roth, CDK, Extended, and GraphOnly) and the ChemAxon pharmacophoric features fingerprint. Pharmacoprint consisted of 39 973 bits; several methods were applied for dimensionality reduction, and the best algorithm not only reduced the length of the bit string but also improved the efficiency of the ML tests. Further optimization allowed us to define the best parameter settings for using Pharmacoprint in discrimination tests and for maximizing statistical parameters. Finally, Pharmacoprint generated for three-dimensional (3D) structures with defined hydrogens as input data was applied to neural networks with a supervised autoencoder for selecting the most important bits and allowed us to maximize the Matthews correlation coefficient up to 0.962. The results show the potential of Pharmacoprint as a new, perspective tool for computer-aided drug design.

Radioligand and computational insight in structure - Activity relationship of saccharin derivatives being ipsapirone and revospirone analogues

Schizophrenia and depression are diseases that significantly impede human functioning in society. Current antidepressant drugs are not fully effective. According to literature data, the effect on D₂R or 5-HT_1AR can effectively reduce the symptoms of depression or schizophrenia. Recent research hypothetized that the synergism of both of these receptors can improve the effectiveness of therapy. Ipsapirone, a representative of long-chain arylpiperazines, is a known 5-HT_1AR ligand that has antidepressant effect. This compound has no affinity for the D₂R. Bearing in mind, we decided to design ligands with improved affinity to D₂R and confirmed that in some cases elongation of the carbon linker or arylpiperazine exchange may have beneficial influence on the binding to D2R and 5-HT1AR. Four groups of ligands being ipsapirone analogues with butyl, pentyl, hexyl and stiffened xylene chains were designed. All compounds were obtained in solvent-free reactions supported by a microwave irradiation with an efficiency mainly above 60%. All ligands containing 1-(2-pyrimidinyl)piperazine exhibited high affinity to 5-HT_1AR. In this case, chemical modifications within the chain did not affect the affinity to D₂R. In the case of ligands containing 1-phenylpiperazine, 1-(3-trifluoromethylphenyl)piperazine, 1-(1-naphthyl)piperazine, and 1-(4-chlorophenyl)piperazine, elongation of carbon linker increases of affinity to D₂R. For ligands containing 1- (2-pyridyl) piperazine, and 1-(2,3-dichlorophenyl)piperazine, we observed an opposite effect. For ligands containing 1-phenylpiperazine, 1-(2-methoxyphenyl)piperazine and 1-(2-pyridyl)piperazine, chain elongation had no effect on 5-HT_1AR binding. In turn of ligands containing 1-(3-trifluoromethylphenyl)piperazine and 1- (2,3-dichlorophenyl)piperazine, we observed that elongation of carbon linker has a positive influence to 5-HT_1AR. Molecular modelling was used to support the SAR study.

G protein-coupled receptors: structure- and function-based drug discovery

As one of the most successful therapeutic target families, G protein-coupled receptors (GPCRs) have experienced a transformation from random ligand screening to knowledge-driven drug design. We are eye-witnessing tremendous progresses made recently in the understanding of their structure-function relationships that facilitated drug development at an unprecedented pace. This article intends to provide a comprehensive overview of this important field to a broader readership that shares some common interests in drug discovery.

Using normal mode analysis on protein structural models. How far can we go on our predictions?

Normal mode analysis (NMA) is a fast and inexpensive approach that is largely used to gain insight into functional protein motions, and more recently to create conformations for further computational studies. However, when the protein structure is unknown, the use of computational models is necessary. Here, we analyze the capacity of NMA in internal coordinate space to predict protein motion, its intrinsic flexibility, and atomic displacements, using protein models instead of native structures, and the possibility to use it for model refinement. Our results show that NMA is quite insensitive to modeling errors, but that calculations are strictly reliable only for very accurate models. Our study also suggests that internal NMA is a more suitable tool for the improvement of structural models, and for integrating them with experimental data or in other computational techniques, such as protein docking or more refined molecular dynamics simulations.

Neuropharmacological assessment in mice and molecular docking of piperazine derivative LQFM212

Piperazine derivatives are an attractive class of chemical compounds for the treatment of various mental illness. Herein, we demonstrated the synthesis of LQFM212, a piperazine derivative, behavioral evaluation in mice and computational studies. In neuropharmacological assessment, LQFM212 treatment at doses of 18, 54 or 162 μmol/kg increased the sleep duration in sodium pentobarbital-induced sleep test. LQFM212 at dose of 162 μmol/kg increased climbing time in the chimney test and decreased the number of squares crossed in the open field test, suggesting that LQFM212 in high doses reduces spontaneous movement. However, LQFM212 treatment at the doses of 18 or 54 μmol/kg increased the preference for the center of field which could be indicative of anxiolytic-like effects. In elevated plus maze and light-dark box tests, LQFM212 treatment altered all parameters observed that demonstrate anxiolytic-like activity. These effects were reversed by flumazenil, mecamylamine, WAY-100635 and PCPA, but not with ketanserin, showing that anxiolytic-like activity involve benzodiazepine site of GABA_A receptor, nicotinic and serotonergic pathways. Molecular docking of LQFM212 showed that the ligand has more interactions with GABA_A receptor than with 5-HT_1A receptor. Despite the involvement of benzodiazepine site on anxiolytic-like effect of LQFM212, treatment with this compound did not alter cognitive function in the step-down avoidance test. In this sense, this piperazine derivative is a good prototype for treating anxiety disorders with putative mechanism of action.

Uncarialins A-I, Monoterpenoid Indole Alkaloids from Uncaria rhynchophylla as Natural Agonists of the 5-HT1A Receptor

Nine new monoterpenoid indole alkaloids, uncarialins A-I (1-9), were isolated from Uncaria rhynchophylla as well as 14 known analogues (10-23). Their structures were determined by HRESIMS, 1D and 2D NMR, and experimental and calculated electronic circular dichroism data. Compounds 5, 7, 15, and 22 displayed significant agonistic effects against the 5-HT_1A receptor with EC₅₀ values of 2.2 ± 0.1, 0.1 ± 0.1, 1.6 ± 0.3, and 2.0 ± 0.5 μM, respectively. The mechanisms of action of these four compounds with the 5-HT_1A receptor were investigated by molecular docking, and the results suggested that amino acid residues Asp116, Thr196, Asn386, and Tyr390 played critical roles in the observed activity of the above-mentioned compounds.

Artificial intelligence and big data facilitated targeted drug discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

Design, synthesis and biological evaluation of novel serotonin and dopamine receptor ligands being 6-bromohexyl saccharine derivatives

Due to numerous side effects of current antidepressants, the search for new, safer bioactive compounds is still a valid research topic in medical chemistry. In our research we decided to synthesize and determine SAR for new hexyl arylpiperazines (LACPs) derivated with saccharin moiety. High biological activity has been explained using molecular modelling methods. The compounds obtained show high affinity for the 5-HT_1A (compound 18, K_i = 4 nM - antagonist mode) and D₂ (compound 15, K_i = 7 nM - antagonist mode) receptor, and in some cases also 5-HT₇ receptor (compound 17, K_i = 20 nM). A preliminary ADME analysis showed that the compounds exhibit CNS drugability properties. We have proved that carbon-chain lengthening may have a beneficial effect on increasing the activity towards serotonin and dopamine receptors.

New dual ligands for the D2 and 5-HT1A receptors from the group of 1,8-naphthyl derivatives of LCAP

More than 300 million people are suffering from depression, one of the civilization diseases in the 21st century. Serotonin 5-HT_1AR and dopamine D₂R play an important role in the treatment and pathogenesis of depression. Moreover, in recent years, the efficacy of dual 5-HT_1A/D₂ receptors ligands has been demonstrated in the fight against depression. In this work the new bulky arylpiperazine derivatives (LCAP) were synthesized in microwave radiation field. The affinities for the selected serotonin (5-HT_1A,5-HT_2A,5-HT₆,5-HT₇) and dopamine (D₂) receptors have been evaluated in vitro. Compounds 5.3a, 5.4, 5.1c, 5.3d, 5.2a are promising dual 5-HT_1AR/D₂R ligands. The SAR analysis were additionally supported with molecular docking studies.

Multi-targeting protein-protein interaction inhibitors: Evolution of macrocyclic ligands with embedded carbohydrates (MECs) to improve selectivity

Compounds targeting multiple proteins can have synergistic effects and are therefore of interest in medicinal chemistry. At the same time, inhibiting protein-protein interactions (PPI) is increasingly desired in the treatment of disorders or diseases. The development of non-peptidomimetic inhibitors is still a challenge. Herein we investigate macrocyclic scaffolds with one or two embedded carbohydrates (MECs) that present amino acid side chains, or related isosteres, as pharmacophoric groups. Firstly, retroscreening of the previously reported eannaphane-40 (E40, 40), a MEC presenting two pharmacophoric groups, against a set of 55 receptor-subtypes led to a finding of sub-micromolar inhibitory activity for E40 against three serotonergic isoforms (5HT_1A/_2A/2B) as well as the Na⁺ channel and the NK-2 receptor. We synthesised MECs with an additional pharmacophoric group compared to E40, with a view to identifying compounds where the selectivity profile was altered among the protein hits from the retroscreening. MECs were produced based on scaffolds with two monosaccharide residues, leading to the incorporation of a third pharmacophoric group. Later, homology models were prepared for four proteins (5HT_1A, 5HT_2A, NK₂ and site-2 of the sodium channel) whose 3D structure is unknown. Inverse docking of the synthesised compounds led to the selection of a new MEC (MEC-B) for protein binding assays. MEC-B was found to have its selectivity profile modulated, in line with docking prediction, compared to E40. MEC-B is dual inhibitor of both 5-HT_1A and the sodium channel with improved selectivity for these proteins compared to 5-HT_2A/2B/2C, 5-HT transporter and NK₂ receptor. Thus, a new multitargeting compound, with an improved selectivity profile was identified, based on a MEC peptidomimetic scaffold.

Influence of the Structural Accuracy of Homology Models on Their Applicability to Docking-Based Virtual Screening: The β2 Adrenergic Receptor as a Case Study

How accurate do structures of the β₂ adrenergic receptor (β₂AR) need to be to effectively serve as platforms for docking-based virtual screening campaigns? To answer this research question, here, we targeted through controlled virtual screening experiments 23 homology models of the β₂AR endowed with different levels of structural accuracy. Subsequently, we studied the correlation between virtual screening performance and structural accuracy of the targeted models. Moreover, we studied the correlation between virtual screening performance and template/target receptor sequence identity. Our study demonstrates that docking-based virtual screening campaigns targeting homology models of the β₂AR, in the majority of the cases, yielded results that exceeded random expectations in terms of area under the receiver operating characteristic curve (ROC AUC). Moreover, with the most effective scoring method, over one-third and one-quarter of the models yielded results that exceeded random expectation also in terms of enrichment factors (EF1, EF5, and EF10) and BEDROC (α = 160.9), respectively. Not surprisingly, we found a detectable linear correlation between virtual screening performance and structural accuracy of the ligand-binding cavity. We also found a detectable linear correlation between virtual screening performance and structural accuracy of the second extracellular loop (EL2). Finally, our data indicate that, although there is no detectable linear correlation between virtual screening performance and template/β₂AR sequence identity, models built on the basis of templates that show high sequence identity with the β₂AR, especially within the ligand-biding cavity, performed consistently well. Conversely, models with lower sequence identity displayed performance levels that ranged from very good to random, with no apparent correlation with the sequence identity itself.

Structural bioinformatics analysis of variants on GPCR function

G protein-coupled receptors (GPCRs) are key membrane-embedded receptor proteins, with critical roles in cellular signal transduction. In the era of precision medicine, understanding the role of natural variants on GPCR function is critical, especially from a pharmacogenomics viewpoint. Studies involved in mapping variants to GPCR structures are briefly reviewed here. The endocannabinoid system involving the central nervous system (CNS), the human cannabinoid receptor 1 (CB1), is an important drug target and its variability has implications for disease susceptibility and altered drug and pain response. We have carried out a computational study to map deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) to CB1. CB1 mutations were computationally evaluated from neutral to deleterious, and the top twelve deleterious mutations, with structural information, were found to be either close to the ligand binding region or the G-protein binding site. We have mapped these to the active and inactive CB1 X-ray crystallographic structures to correlate variants with available phenotypic information. We have also carried out molecular dynamics simulations to functionally characterize four selected mutants.

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.

Recognition of repulsive and attractive regions of selected serotonin receptor binding site using FMO-EDA approach

The complexes of selected long-chain arylpiperazines with homology models of 5-HT_1A, 5-HT_2A, and 5-HT₇ receptors were investigated using quantum mechanical methods. The molecular geometries of the ligand-receptor complexes were firstly optimized with the Our own N-layered Integrated molecular Orbital and molecular Mechanics (ONIOM) method. Next, the fragment molecular orbitals method with an energy decomposition analysis scheme (FMO-EDA) was employed to estimate the interaction energies in binding sites. The results clearly showed that orthosteric binding sites of studied serotonin receptors have both attractive and repulsive regions. In the case of 5-HT_1A and 5-HT_2A two repulsive areas, located in the lower part of the binding pocket, and one large area of attraction engaging many residues at the top of all helices were identified. Additionally, for the 5-HT₇ receptor, the third area of destabilization located at the extracellular end of the helix 6 was found.

Oncotoxic Properties of Serotonin Transporter Inhibitors and 5-HT1A Receptor Ligands

The cytotoxic activity of several serotonin transporter (SERT) inhibitors and subtype of serotonin receptor 1A (5-HT_1A receptor) ligands have been examined in androgen-insensitive human PC-3 prostate and neuroblastoma SH-SY5Y cancer cells. Almost all of the studied compounds (except 5-HT_1A receptor agonist (2R)-(+)-8-Hydroxy-2-(di-n-propylamino)tetralin hydrobromide (8-OH-DPAT)) exhibited absolute cytotoxic activity against the examined cancer cells. The compound 4-Fluoro-N-[2-[4-(7-methoxy-1-naphthalenyl)-1-piperazinyl]ethyl]benzamide hydrochloride (S14506) that showed highest activity against neuroblastoma tumors was the 5-HT_1A receptor agonist (although not alike other 5-HT_1A receptor agonists). On the other hand, the compound 6-nitro-2-(4-undecylpiperazin-1-yl)quinoline hydrochloride (AZ07) that had the highest activity against PC-3 prostate cancer cells was a compound exhibiting antagonistic activity against the 5-HT_1A receptor. Thus, compounds of oncotoxic properties S14506 and AZ07 should be evaluated further for their potential use in the prevention and treatment of cancer. Most of the 15 compounds tested exhibited either agonistic or antagonistic activity for both the cyclic adenosine monophosphate (cAMP) and extracellular signal-regulated kinase 1 and 2 (ERK1/2) pathways in human embryonic kidney 293 (HEK293) cells that overexpress the 5HT1AR gene. However, compounds paroxetine, N-Ac-paroxetine and 2-[4-(cyclobutylmethyl)piperazin-1-yl]-6-nitroquinoline hydrochloride (AB22) simultaneously exhibited antagonistic activity on the cAMP pathway and agonistic activity on the ERK1/2 pathway. Fluoxetine relative to compound AZ07 had almost three times lower cytotoxic activity against PC-3 prostate cancer cells. However, the proapoptotic activity of fluoxetine compared to compound AZ07 is almost two times higher which would suggest that the cytotoxic activity of both compounds may be dependent on different cell death mechanisms. Compound S14506 was found to be an antagonist of the serine-threonine protein kinase B (Akt) pathway. Prosurvival Akt activity may be reversed by Akt antagonists. Therefore, the antagonistic activity of S14506 on the Akt pathway may evoke caspase-3 expression and cytotoxicity. It appears that one should not expect a straightforward relationship between the activation of particular serotonergic pathways by selective serotonin reuptake inhibitors (SSRIs) and 5-HT_1A receptor ligands and their cytotoxic or cytoprotective activity. Additionally, nuclear transcription factor κB (NF-κB), which may be involved in 5-HT-dependent biochemical pathways by coordinating different subunits in the formation of a dimer, may regulate the transcription of different transduction pathways. Therefore, it can be suggested that the mechanism of the cytotoxic activity of certain compounds (serotonergic against nonserotonergic) may depend on the compound and cancer type being examined. Docking studies showed that S14506, buspirone and spiperone bind in similar ways in the 5-HT_1A receptor model and interacted with similar 5-HT_1A receptor residues. S14506 and spiperone were found to be located closer to both phenylalanines in TM6 than buspirone, thus exhibiting more antagonist binding modes.

Inhibition of the precursor and mature forms of HIV-1 protease as a tool for drug evaluation

HIV-1 protease (PR) is a homodimeric enzyme that is autocatalytically cleaved from the Gag-Pol precursor. Known PR inhibitors bind the mature enzyme several orders of magnitude more strongly than the PR precursor. Inhibition of PR at the precursor level, however, may stop the process at its rate-limiting step before the proteolytic cascade is initiated. Due to its structural heterogeneity, limited solubility and autoprocessing, the PR precursor is difficult to access by classical methods, and limited knowledge regarding precursor inhibition is available. Here, we describe a cell-based assay addressing precursor inhibition. We used a reporter molecule containing the transframe (TFP) and p6* peptides, PR, and N-terminal fragment of reverse transcriptase flanked by the fluorescent proteins mCherry and EGFP on its N- and C- termini, respectively. The level of FRET between EGFP and mCherry indicates the amount of unprocessed reporter, allowing specific monitoring of precursor inhibition. The inhibition can be quantified by flow cytometry. Additionally, two microscopy techniques confirmed that the reporter remains unprocessed within individual cells upon inhibition. We tested darunavir, atazanavir and nelfinavir and their combinations against wild-type PR. Shedding light on an inhibitor’s ability to act on non-mature forms of PR may aid novel strategies for next-generation drug design.

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.

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.