Discovery of selective ACAT2 antagonist via a combination strategy based on deep docking, pharmacophore modelling, and molecular dynamics simulation.

Acyl-CoA: cholesterol acyltransferase (ACAT), a pivotal enzyme in the absorption and metabolism of cholesterol, is primarily responsible for intracellular esterification. ACAT inhibition is expected to diminish plasma lipid levels by impeding intestinal cholesterol absorption, thereby preventing the progression of atherosclerotic lesions. A previous study shows that selective inhibition of ACAT2 significantly mitigated hypercholesterolaemia and atherosclerosis in mouse models. Therefore, the need for ACAT2 selective inhibitors becomes particularly urgent. In this study, we established a multilayer virtual screening workflow and subjected biologically evaluated representative compounds to enzyme inhibitory assays. The experimental results indicated that the two compounds, STL565001 (inhibition rate at 25 µM: 75.7 ± 27.8%, selectivity = 6) and STL528213 (inhibition rate at 25 µM: 87.8 ± 12.4%, selectivity = 13), demonstrated robust activity against ACAT2, displaying greater selectivity for ACAT2 than for ACAT1. The molecular mechanisms governing the inhibitory activities of the selected compounds were systematically elucidated using computational approaches. In addition, hotspot residues in ACAT2 that are crucial for ligand binding were successfully identified. In summary, we devised a multilayer screening scheme to expeditiously and efficiently identify compounds with enzyme inhibitory activity, offering novel scaffolds for subsequent drug design centred on ACAT2 targets.

Ligand-based pharmacophore modelling, structure optimisation, and biological evaluation for the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors.

Targeting Heat shock protein 90 (HSP90) C-terminus is an important strategy to develop HSP90 inhibitors without inducing heat shock response. The development of C-terminal inhibitors, however, is hampered by a lack of understanding regarding the interaction between the HSP90 C-terminus and the present inhibitors. We collected seven classical and structurally diverse HSP90 C-terminal inhibitors and constructed a ligand-based pharmacophore model. The subsequent virtual screening and structural optimisation led to the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors. 9 and 27 exhibited strong antitumour activity in vitro by inhibiting proliferation and inducing apoptosis in multiple cancer cell lines. These compounds disrupted the interaction between HSP90 C-terminus and peptidylprolyl isomerase D, exerting a stronger inhibitory effect than novobiocin. 27 significantly induced the degradation of HSP90 clients without triggering heat shock response. In an in vivo study using 4T1 mice breast cancer models, 9 showed a potent antitumour effect without obvious toxicity.

Discovery of novel and potent dual-targeting AXL/HDAC2 inhibitors for colorectal cancer treatment via structure-based pharmacophore modelling, virtual screening, and molecular docking, molecular dynamics simulation studies, and biological evaluation.

Colorectal cancer (CRC) is one of the most common cancers worldwide. Nowadays, owing to the complex mechanism of tumorigenesis, simultaneous inhibition of multiple targets is an important anticancer strategy. Recent studies have demonstrated receptor tyrosine kinase AXL (AXL) and histone deacetylase 2 (HDAC2) are closely associated with colorectal cancer. Herein, we identified five hit compounds concurrently targeting AXL and HDAC2 using virtual screening. Inhibitory experiments revealed these hit compounds potently inhibited AXL and HDAC2 in the nanomolar range. Among them, Hit-3 showed the strongest inhibitory effects which were better than that of the positive control groups. Additionally, MD assays showed that Hit-3 could bind stably to the AXL and HDAC2 active pockets. Further MTT assays demonstrated that Hit-3 showed potent anti-proliferative activity. Most importantly, Hit-3 exhibited significant in vivo antitumor efficacy in xenograft models. Collectively, this study is the first discovery of dual-targeting AXL/HDAC2 inhibitors for colorectal cancer treatment.

[Chemical composition analysis of Ganoderma lucidum based on UPLC-Orbitrap-HRMS and virtual screening of FXR activators for treatment of liver fibrosis].

The chemical composition of Ganoderma lucidum ethanol extracts was systematically analyzed and identified by ultra-high performance liquid chromatography-quadrupole electrostatic field orbitrap high-resolution mass spectrometry(UPLC-Orbitrap-HRMS). The fragmentation pattern of the representative chemical compounds was summarized, and the potential anti-liver fibrosis active compounds of G. lucidum acting on the farnesoid X receptor(FXR) target were studied to elucidate its pharmacodynamic substance basis. Preliminarily, 95 chemical constituents of G. lucidum ethanol extracts were identified, including 24 ganoderic acids, 9 ganoderenic acids, 13 lucidenic acids, 3 ganolucidic acids, 1 ganoderma lactone, 40 other triterpenoids, 4 fatty acids, and 1 other constituent. In addition, the fragmentation patterns of the representative compounds were also analyzed. The structural characteristics of ganoderic acids and ganoderenic acids were the C30 skeleton, containing free-COOH and-OH groups, which could easily lose H_2O and CO_2 to form fragment ions. The D-ring was mostly a five-membered ring, which was prone to breakage. Lucidenic acids were the lanosterolane-type of the C27 skeleton, and the side-chain structure became shorter and contained the same free-COOH and-OH compared with ganoderic acids, which had been reduced from 8 to 5 cartons and prone to lose H_2O and CO_2. Then, six reported FXR receptor agonists were selected to form a training set for establishing a pharmacophore model based on FXR ligands. The 95 identified chemical constituents of G. lucidum were matched with the pharmacophore, and the optimal pharmacophore model 02(sensitivity=0.750 00, specificity=0.555 56, ROC=0.750) was selected for the virtual screening of the G. lucidum compound library through the validation of the test set. Finally, 31 potential G. lucidum active constituents were screened and chosen to activate the FXRs. The ADMET results showed that ganoderic acid H and lucidenic acid J had less than 90% plasma protein binding rate and no hepatotoxicity, which could be used as FXR activators for developing clinical drugs for the treatment of liver fibrosis, either alone or in combination.

Discovery of newer pyrazole derivatives with potential anti-tubercular activity via 3D-QSAR based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics simulation studies.

Tuberculosis is one of the leading causes of death of at least one million people annually. The deadliest infectious disease has caused more than 120 million deaths in humans since 1882. The cell wall structure of Mycobacterium tuberculosis is important for survival in the host environment. InhA is the foremost target for the development of novel anti-tubercular agents. Therefore, we report pharmacophore-based virtual screening (ZINC and ASINEX databases) and molecular docking study (PDB Code: 4TZK) to identify and design potent inhibitors targeting to InhA. A five-point pharmacophore model AADHR_1 (with R2 = 0.97 and Q2 = 0.77) was developed by using 47 compounds with its reported MIC values. Further, to identify and design potent hit molecules based on lead identification and modification, generated hypothesis employed for virtual screening using ZINC and ASINEX databases. Predicted pyrazole derivatives further gauged for drug likeliness and docked against enoyl acyl carrier protein reductase to categorize the essential amino acid interactions to the active site of the enzyme. Structure elucidation of these synthesized compounds was carried out using IR, MS, 1H-NMR and 13C-NMR spectroscopy. Amongst all the synthesized compounds, some of the compounds 5a, 5c, 5d and 5e were found to be potent with their MIC ranging from 2.23 to 4.61 µM. Based on preliminary anti-tubercular activity synthesized potent molecules were further assessed for MDR-TB, XDR-TB and cytotoxic study.

Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study.

Activity cliffs (ACs) are defined as closely analogous compounds of significant affinity discrepancies against certain biotarget. In this paper we propose to use AC pair(s) for extracting valid binding pharmacophores through exposing corresponding protein complexes to stochastic deformation/relaxation followed by applying genetic algorithm/machine learning (GA-ML) for selecting optimal pharmacophore(s) that best classify a long list of inhibitors. We compared the performances of ligand-based and structure-based pharmacophores with counterparts generated by this newly introduced technique. Sphingosine kinase 1 (SPHK-1) was used as case study. SPHK-1 is a lipid kinase that plays pivotal role in the regulation of a variety of biological processes including, cell growth, apoptosis, and inflammation. The new approach proved to yield pharmacophore and ML models of comparable accuracies to established ligand-based and structure-based pharmacophores. The resulting pharmacophores and ML models were used to capture hits from the national cancer institute list of compounds and predict their bioactivity categories. Two hits of novel chemotypes showed selective and low micromolar inhibitory IC50 values against SPHK-1.

Identification of novel antiplatelet agents by targeting Glycoprotein VI: A combined virtual screening study.

Cardiovascular diseases are the primary reason for deaths in the world. However, antiplatelet drugs in the market have limitations in use because of the risk of increased bleeding and other side effects that impair primary homeostasis. Therefore, safe and effective antithrombotic drugs are needed for the treatment of plaque formation in blood vessels. Glycoprotein VI (GPVI) is a platelet major collagen receptor and a target for potent and safe antithrombotic therapy. We designed this study based on the molecular interaction pattern of previously published GPVI receptor antagonists within the reported binding site. We selected sixteen hit compounds from a large chemical database that contains>6 million in-stock compounds by following a combined virtual screening. Then, we evaluated their inhibitory effects on platelet aggregation induced by GPVI receptor agonists (collagen, collagen related peptide (CRP), convulxin) and the most potent platelet agonist, thrombin, in vitro by using washed human platelets. IC50 values of compounds 1 and 2 are, respectively, 0.35 µM and 1.01 µM for collagen, 0.80 µM and 1.92 µM for CRP, 195.2 and 7.24 µM for convulxin and 81.38 and 51.74 µM for thrombin. We identified compounds 1 and 2 as the most promising antiplatelet agents out of sixteen compounds. Additionally, compounds 1 and 2 may serve as promising starting points and shed light on the design of new, potent and selective GPVI receptor antagonists.

Ligand-Enhanced Negative Images Optimized for Docking Rescoring.

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein's inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach-actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

Identification of Novel Dopamine D2 Receptor Ligands-A Combined In Silico/In Vitro Approach.

Diseases of the central nervous system are an alarming global problem showing an increasing prevalence. Dopamine receptor D2 (D2R) has been shown to be involved in central nervous system diseases. While different D2R-targeting drugs have been approved by the FDA, they all suffer from major drawbacks due to promiscuous receptor activity leading to adverse effects. Increasing the number of potential D2R-targeting drug candidates bears the possibility of discovering molecules with less severe side-effect profiles. In dire need of novel D2R ligands for drug development, combined in silico/in vitro approaches have been shown to be efficient strategies. In this study, in silico pharmacophore models were generated utilizing both ligand- and structure-based approaches. Subsequently, different databases were screened for novel D2R ligands. Selected virtual hits were investigated in vitro, quantifying their binding affinity towards D2R. This workflow successfully identified six novel D2R ligands exerting micro- to nanomolar (most active compound KI = 4.1 nM) activities. Thus, the four pharmacophore models showed prospective true-positive hit rates in between 4.5% and 12%. The developed workflow and identified ligands could aid in developing novel drug candidates for D2R-associated pathologies.

Analysing the essential proteins set of Plasmodium falciparum PF3D7 for novel drug targets identification against malaria.

BACKGROUND: Plasmodium falciparum is an obligate intracellular parasite of humans that causes malaria. Falciparum malaria is a major public health threat to human life responsible for high mortality. Currently, the risk of multi-drug resistance of P. falciparum is rapidly increasing. There is a need to address new anti-malarial therapeutics strategies to combat the drug-resistance threat. METHODS: The P. falciparum essential proteins were retrieved from the recently published studies. These proteins were initially scanned against human host and its gut microbiome proteome sets by comparative proteomics analyses. The human host non-homologs essential proteins of P. falciparum were additionally analysed for druggability potential via in silico methods to possibly identify novel therapeutic targets. Finally, the PfAp4AH target was prioritized for pharmacophore modelling based virtual screening and molecular docking analyses to identify potent inhibitors from drug-like compounds databases. RESULTS: The analyses identified six P. falciparum essential and human host non-homolog proteins that follow the key druggability features. These druggable targets have not been catalogued so far in the Drugbank repository. These prioritized proteins seem novel and promising drug targets against P. falciparum due to their key protein-protein interactions features in pathogen-specific biological pathways and to hold appropriate drug-like molecule binding pockets. The pharmacophore features based virtual screening of Pharmit resource predicted a lead compound i.e. MolPort-045-917-542 as a promising inhibitor of PfAp4AH among prioritized targets. CONCLUSION: The prioritized protein targets may worthy to test in malarial drug discovery programme to overcome the anti-malarial resistance issues. The in-vitro and in-vivo studies might be promising for additional validation of these prioritized lists of drug targets against malaria.

Repurposing old drugs to fight multidrug resistant cancers.

Overcoming multidrug resistance represents a major challenge for cancer treatment. In the search for new chemotherapeutics to treat malignant diseases, drug repurposing gained a tremendous interest during the past years. Repositioning candidates have often emerged through several stages of clinical drug development, and may even be marketed, thus attracting the attention and interest of pharmaceutical companies as well as regulatory agencies. Typically, drug repositioning has been serendipitous, using undesired side effects of small molecule drugs to exploit new disease indications. As bioinformatics gain increasing popularity as an integral component of drug discovery, more rational approaches are needed. Herein, we show some practical examples of in silico approaches such as pharmacophore modelling, as well as pharmacophore- and docking-based virtual screening for a fast and cost-effective repurposing of small molecule drugs against multidrug resistant cancers. We provide a timely and comprehensive overview of compounds with considerable potential to be repositioned for cancer therapeutics. These drugs are from diverse chemotherapeutic classes. We emphasize the scope and limitations of anthelmintics, antibiotics, antifungals, antivirals, antimalarials, antihypertensives, psychopharmaceuticals and antidiabetics that have shown extensive immunomodulatory, antiproliferative, pro-apoptotic, and antimetastatic potential. These drugs, either used alone or in combination with existing anticancer chemotherapeutics, represent strong candidates to prevent or overcome drug resistance. We particularly focus on outcomes and future perspectives of drug repositioning for the treatment of multidrug resistant tumors and discuss current possibilities and limitations of preclinical and clinical investigations.

Greedy 3-Point Search (G3PS)-A Novel Algorithm for Pharmacophore Alignment.

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

Discovery of a nanomolar inhibitor of the human glyoxalase-I enzyme using structure-based poly-pharmacophore modelling and molecular docking.

The glyoxalase-I (GLO-I) enzyme, which is the initial enzyme of the glyoxalase system that is responsible for the detoxification of cytotoxic α-ketoaldehydes, such as methylglyoxal, has been approved as a valid target in cancer therapy. Overexpression of GLO-I has been observed in several types of carcinomas, including breast, colorectal, prostate, and bladder cancer. In this work we aimed to identify potential GLO-I inhibitors via employing different structure-based drug design techniques including structure-based poly-pharmacophore modelling, virtual screening, and molecular docking. Poly-pharmacophore modelling was applied in this study in order to thoroughly explore the binding site of the target enzyme, thereby, revealing hits that could bind in a nonconventional way which can pave the way for designing more potent and selective ligands with novel chemotypes. The modelling phase has resulted in the selection of 31 compounds that were biologically evaluated against human GLO-I enzyme. Among the tested set, seven compounds showed excellent inhibitory activities with IC50 values ranging from 0.34 to 30.57 µM. The most active compound (ST018515) showed an IC50 of 0.34 ± 0.03 µM, which, compared to reported GLO-I inhibitors, can be considered a potent inhibitor, making it a good candidate for further optimization towards designing more potent GLO-I inhibitors.

Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches.

The complement system is associated with various diseases such as inflammation or auto-immune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 µM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively.

Rational design of small molecules that modulate the transcriptional function of the response regulator PhoP.

The response regulator PhoP, which is part of the PhoP/PhoQ two-component system, regulates the expression of multiple genes involved in controlling virulence in Salmonella enterica serovar Typhimurium and other species of Gram-negative bacteria. Modulating the phosphorylation-mediated dimerization in the receiver domain may interfere with the transcriptional function of PhoP. In this study, we analyzed the therapeutic potential of the PhoP receiver domain by exploring it as a potential target for drug design. The structural information was then applied to identify the first hit compounds from commercial chemical libraries by combining pharmacophore modelling and docking methods with a GFP (Green Fluorescent Protein)-based promoter-fusion bioassay. In total, one hundred and forty compounds were selected, purchased, and tested for biological activity. Several novel scaffolds showed acceptable potency to modulate the transcriptional function of PhoP, either by enhancing or inhibiting the expression of PhoP-dependent genes. These compounds may be used as the starting point for developing modulators that target the protein-protein interface of the PhoP protein as an alternative strategy against antibiotic resistance.

Evaluation of known and novel inhibitors of Orai1-mediated store operated Ca2+ entry in MDA-MB-231 breast cancer cells using a Fluorescence Imaging Plate Reader assay.

The Orai1 Ca2+ permeable ion channel is an important component of store operated Ca2+ entry (SOCE) in cells. It's over-expression in basal molecular subtype breast cancers has been linked with poor prognosis, making it a potential target for drug development. We pharmacologically characterised a number of reported inhibitors of SOCE in MDA-MB-231 breast cancer cells using a convenient Fluorescence Imaging Plate Reader (FLIPR) assay, and show that the rank order of their potencies in this assay is the same as those reported in a wide range of published assays. The assay was also used in a screening project seeking novel inhibitors. Following a broad literature survey of classes of calcium channel inhibitors we used simplified ligand structures to query the ZINC on-line database, and following two iterations of refinement selected a novel Orai1-selective dichlorophenyltriazole hit compound. Analogues of this were synthesized and evaluated in the FLIPR assay to develop structure-activity relationships (SAR) for the three domains of the hit; triazole (head), dichlorophenyl (body) and substituted phenyl (tail). For this series, the results suggested the need for a lipophilic tail domain and an out-of-plane twist between the body and tail domains.

Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets.

Targeting Proviral integration-site of murine Moloney leukemia virus 1 kinase, hereafter called Pim-1 kinase, is a promising strategy for treating different kinds of human cancer. Headed for this a total list of 328 formerly reported Pim-1 kinase inhibitors has been explored and divided based on the pharmacophoric features of the most active molecules into 10 subsets projected to represent potential active binding manners accessible to ligands within the binding pocket of Pim-1 kinase. Discovery Studio 4.1 (DS 4.1) was employed to detect potential pharmacophoric active binding manners anticipated by Pim-1 Kinase inhibitors. The pharmacophoric models were then allowed to compete within Quantitative Structure Activity Relationship (QSAR) framework with other 2D descriptors. Accordingly Genetic algorithm and multiple linear regression investigation were engaged to find the finest QSAR equation that has the best predictive power r262(2) = 0.70, F = 119.14, rLOO(2) = 0.693, rPRESS(2) against 66 external test inhibitors = 0.71 q(2) = 0.55. Three different pharmacophores appeared in the successful QSAR equation this represents three different binding modes for inhibitors within the Pim-1 kinase binding pocket. Pharmacophoric models were later used to screen compounds within the National Cancer Institute database. Several low micromolar Pim-1 Kinase inhibitors were captured. The most potent hits show IC50 values of 0.77 and 1.03 µM. Also, upon analyzing the successful QSAR Equation we found that some polycyclic aromatic electron-rich structures namely 6-Chloro-2-methoxy-acridine can be considered as putative hits for Pim-1 kinase inhibition.

Ligand-based pharmacophore modelling and screening of DNA minor groove binders targeting Staphylococcus aureus.

The recognition of DNA by small molecules is of special importance in the design of new drugs. Many natural and synthetic compounds have the ability to interact with the minor groove of DNA. In the present study, identification of minor groove binding compounds was attained by the combined approach of pharmacophore modelling, virtual screening and molecular dynamics approach. Experimentally reported 32 minor groove binding compounds were used to develop the pharmacophore model. Based on the fitness score, best three pharmacophore hypotheses were selected and used as template for screening the compounds from drug bank database. This pharmacophore-based screening provides many compounds with the same pharmacological properties. All these compounds were subjected to four phases of docking protocols with combined Glide-quantum-polarized ligand docking approach. Molecular dynamics results indicated that selected compounds are more active and showed good interaction in the binding site of DNA. Based on the scoring parameters and energy values, the best compounds were selected, and antibacterial activity of these compounds was identified using in vitro antimicrobial techniques.

Elucidating the binding mechanisms of GABA and Muscimol as an avenue to discover novel GABA-mimetic small molecules.

Gamma-aminobutyric acid (GABA) signaling is the principal inhibitory pathway in the central nervous system. It is critical in neuronal cell proliferation and fate determination. Any aberration in GABA inhibition results in psychiatric and neurological diseases. Thus, modulating GABAergic neurotransmission has become the basis of drug therapy for psychiatric and several neurological diseases. Though GABA and muscimol are classical inhibitors of GABA receptors, the search for novel inhibitors continues unabated. In this study, the binding mechanism of GABA and muscimol was elucidated and applied in the search for small molecule GABAergic inhibitors using comprehensive computational techniques. It was revealed that a high-affinity binding of GABA and muscimol was mediated by a water molecule involving α1Thr129 and then stabilized by strong interactions including salt bridges with ß2Glu155 and α1Arg66 amidst hydrogen bonds, π-π stacking, and π -cation interactions with other residues. The binding of GABA and muscimol was also characterized by stability and deeper penetration into the hydrophobic core of the protein which resulted in conformational changes of the binding pocket and domain, by inducing correlated motions of the residues. Thermodynamics analysis showed GABA and muscimol exhibited total binding free energies of -19.85 ± 8.83 Kcal/mol and -26.55 ± 3.42 Kcal/mol, respectively. A pharmacophore model search, based on the energy contributions of implicating binding residues, resulted in the identification of ZINC68604167, ZINC19735138, ZINC04202466, ZINC00901626, and ZINC01532854 as potential GABA-mimetic compounds from metabolites and natural products libraries. This study has elucidated the binding mechanisms of GABA and muscimol and successfully applied in the identification of GABA-mimetic compounds.Communicated by Ramaswamy H. Sarma.

Unveiling structural determinants for FXR antagonism in 1,3,4-trisubstituted-Pyrazol amide derivatives: A multi-scale in silico modelling approach.

Non-alcoholic fatty liver disease (NAFLD) is a growing global health concern due to its potential to progress into severe liver diseases. Targeting the bile acid receptor FXR has emerged as a promising strategy for managing NAFLD. Building upon our previous research on FXR partial agonism, the present study investigates a series of 1,3,4-trisubstituted-pyrazol amide derivatives as FXR antagonists, aiming to delineate the structural features for antagonism. By means of 2D-QSAR (quantitative structure-activity relationships) modelling techniques, we elucidated the key structural elements responsible for the antagonistic properties of these derivatives. We then employed QPhAR, an open-access software, to identify key molecular features within the compounds that enhance their antagonistic activity. Additionally, 3D-QSAR modelling allowed us to analyse the steric and electrostatic fields of aligned 3D structures, further refining our understanding of structure-activity relationships. Subsequent molecular dynamics simulations provided insights into the binding mode interactions between the compounds and FXR, with varying potencies, confirming and complementing the findings from 2D-QSAR, pharmacophore, and 3D-QSAR modelling. Particularly, our study highlighted the significance of hydrophobic interactions in conferring potent antagonism by the 1,3,4-trisubstituted-pyrazol amide derivatives against FXR. Overall, this work underscores the potential of 1,3,4-trisubstituted-pyrazol amides as FXR antagonists for NAFLD treatment. Notably, our reliance on open-access software fosters reproducibility and broadens the accessibility of our findings.