Architecture of the rings of 5-arylidenerhodanine derivatives versus P-gp inhibition

5-Arylidene derivatives of rhodanine show various biological activities. The new crystal structures of five derivatives investigated towards ABCB1 efflux pump modulation are reported, namely, 2-[5-([1,1'-biphenyl]-4-ylmethylidene]-4-oxo-2-thioxothiazolidin-3-yl)acetic acid dimethyl sulfoxide monosolvate, C18H13NO3S2·C2H6OS (1), 4-[5-([1,1'-biphenyl]-4-ylmethylidene]-4-oxo-2-thioxothiazolidin-3-yl)butanoic acid, C20H17NO3S2 (2), 5-[4-(benzyloxy)benzylidene]-2-thioxothiazolidin-4-one, C17H13NO2S2 (3), 4-{5-[4-(benzyloxy)benzylidene]-4-oxo-2-thioxothiazolidin-3-yl}butanoic acid, C21H19NO4S2 (4), and 5-[4-(diphenylamino)benzylidene]-2-thioxothiazolidin-4-one, C22H16N2OS2 (5). Compounds 1 and 3-5 crystallize in the triclinic space group P-1, while 2 crystallizes in the monoclinic space group P21/n, where the biphenyl moiety is observed in two positions (A and B). Two molecules are present in the asymmetric unit of 5 and, for the other four compounds, there is only one molecule; moreover, 1 crystallizes with one dimethyl sulfoxide molecule. The packing of the molecules containing a carboxyl group (1, 2 and 4) is determined by O-H...O hydrogen bonds, while in the other two compounds (3 and 5), the packing is determined by N-H...O hydrogen bonds. Additionally, induced-fit docking studies have been performed for the active compounds to investigate their putative binding mode inside the human glycoprotein P (P-gp) binding pocket.

Mitigating hERG Liability of Toll-Like Receptor 9 and 7 Antagonists through Structure-Based Design

hERG is considered to be a primary anti-target in the drug development process, as the K+ channel encoded by hERG plays an important role in cardiac re-polarization. It is desirable to address the hERG safety liability during early-stage development to avoid the expenses of validating leads that will eventually fail at a later stage. We have previously reported the development of highly potent quinazoline-based TLR7 and TLR9 antagonists for possible application against autoimmune disease. Initial experimental hERG assessment showed that most of the lead TLR7 and TLR9 antagonists suffer from hERG liability rendering them ineffective for further development. The present study herein describes a coordinated strategy to integrate the understanding from structure-based protein-ligand interaction to develop non- hERG binders with IC50 >30 μM with retention of TLR7/9 antagonism through a single point change in the scaffold. This structure-guided strategy can serve as a prototype for abolishing hERG liability during lead optimization.

Tuning the Biological Activity of PI3Kδ Inhibitor by the Introduction of a Fluorine Atom Using the Computational Workflow

As a member of the class I PI3K family, phosphoinositide 3-kinase δ (PI3Kδ) is an important signaling biomolecule that controls immune cell differentiation, proliferation, migration, and survival. It also represents a potential and promising therapeutic approach for the management of numerous inflammatory and autoimmune diseases. We designed and assessed the biological activity of new fluorinated analogues of CPL302415, taking into account the therapeutic potential of our selective PI3K inhibitor and fluorine introduction as one of the most frequently used modifications of a lead compound to further improve its biological activity. In this paper, we compare and evaluate the accuracy of our previously described and validated in silico workflow with that of the standard (rigid) molecular docking approach. The findings demonstrated that a properly fitted catalytic (binding) pocket for our chemical cores at the induced-fit docking (IFD) and molecular dynamics (MD) stages, along with QM-derived atomic charges, can be used for activity prediction to better distinguish between active and inactive molecules. Moreover, the standard approach seems to be insufficient to score the halogenated derivatives due to the fixed atomic charges, which do not consider the response and indictive effects caused by fluorine. The proposed computational workflow provides a computational tool for the rational design of novel halogenated drugs.

Descriptor-free QSAR: effectiveness in screening for putative inhibitors of FGFR1

The long short-term memory (LSTM) algorithm has provided solutions to the limitations of the descriptors-utilizing QSAR models in drug design. However, the direct application of LSTM remains scarce. The effectiveness of a descriptor-free QSAR (LSTM-SM) in modeling the FGFR1 inhibitors dataset while comparing with two conventional QSAR using descriptors (126 bits Morgan fingerprint and 2 D descriptors respectively) as a baseline model was investigated in this study. The validated descriptor-free QSAR model was thereafter used to screen for active FGFR1 inhibitors in the ChemDiv database and subjected to molecular docking, induced-fit docking, QM-MM optimization, and molecular dynamics simulations to filter for compounds with high binding affinity and suggest the putative mechanism of inhibition and specificity. The LSTM-SM model performed better than conventional QSAR; having accuracy, specificity, and sensitivity of 0.92, model loss of 0.025, and AUC of 0.95. Fifteen thousand compounds were predicted as actives from the ChemDiv database and four compounds were finally selected. Of the four, two showed putatively effective binding interactions with key active site residues. Molecular dynamics simulations on these compounds in complex with the receptor further give insight into the conformational dynamics of each compound bounded to the receptor. The complexes formed are stable and exhibit a similar degree of compactness. Our findings predicted the advent of self-feature extracting machine learning algorithms of compounds, and have provided the possibility of better predictive model quality that is not necessarily limited by compound descriptors. The putative FGFR1 inhibitors, with their mechanism of inhibition and specificity, were elucidated using this approachCommunicated by Ramaswamy H. Sarma.

Multi-Targeting Approach in Selection of Potential Molecule for COVID-19 Treatment

The coronavirus disease (COVID-19) is a pandemic that started in the City of Wuhan, Hubei Province, China, caused by the spread of coronavirus (SARS-CoV-2). Drug discovery teams around the globe are in a race to develop a medicine for its management. It takes time for a novel molecule to enter the market, and the ideal way is to exploit the already approved drugs and repurpose them therapeutically. We have attempted to screen selected molecules with an affinity towards multiple protein targets in COVID-19 using the Schrödinger suit for in silico predictions. The proteins selected were angiotensin-converting enzyme-2 (ACE2), main protease (MPro), and spike protein. The molecular docking, prime MM-GBSA, induced-fit docking (IFD), and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins. The ligand-binding stability for the proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro), and 6M1D (ACE2), was in the order of nintedanib > quercetin, nintedanib > darunavir, nintedanib > baricitinib, respectively. The MM-GBSA, IFD, and MD simulation studies imply that the drug nintedanib has the highest binding stability among the shortlisted. Nintedanib, primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing for us against COVID-19.

Identifying Dopamine D3 Receptor Ligands through Virtual Screening and Exploring the Binding Modes of Hit Compounds

The dopamine D3 receptor (D3R) is an important central nervous system target for treating various neurological diseases. D3R antagonists modulate the improvement of psychostimulant addiction and relapse, while D3R agonists can enhance the response to dopaminergic stimulation and have potential applications in treating Parkinson’s disease, which highlights the importance of identifying novel D3R ligands. Therefore, we performed auto dock Vina-based virtual screening and D3R-binding-affinity assays to identify human D3R ligands with diverse structures. All molecules in the ChemDiv library (>1,500,000) were narrowed down to a final set of 37 molecules for the binding assays. Twenty-seven compounds exhibited over 50% inhibition of D3R at a concentration of 10 μM, and 23 compounds exhibited over 70% D3R inhibition at a concentration of 10 μM. Thirteen compounds exhibited over 80% inhibition of D3R at a concentration of 10 μM and the IC50 values were measured. The IC50 values of the five compounds with the highest D3R-inhibition rates ranged from 0.97 μM to 1.49 μM. These hit compounds exhibited good structural diversity, which prompted us to investigate their D3R-binding modes. After trial and error, we combined unbiased molecular dynamics simulation (MD) and molecular mechanics generalized Born surface area (MM/GBSA) binding free-energy calculations with the reported protein−ligand-binding pose prediction method using induced-fit docking (IFD) and binding pose metadynamics (BPMD) simulations into a self-consistent and computationally efficient method for predicting and verifying the binding poses of the hit ligands to D3R. Using this IFD-BPMD-MD-MM/GBSA method, we obtained more accurate and reliable D3R−ligand-binding poses than were obtained using the reported IFD-BPMD method. This IFD-BPMD-MD-MM/GBSA method provides a novel paradigm and reference for predicting and validating other protein−ligand binding poses.

Synthesis, biological evaluation and molecular dynamics studies of oxadiazine derivatives as potential anti-hepatotoxic agents

Generally, herbal medicines having remarkable popularity for treating liver ailments, but they are still unacceptable because of the deprivation of herbal drug standardization. Therefore, there is a need for promising synthetic drugs to overcome the critical liver problem. We introduce 1, 3, 4-oxadiazine ring in this study to identify better anti-hepatotoxic agents via a suitable synthetic route. These oxadiazine-based derivatives were structurally confirmed by analytical and spectral data and evaluated for their anti-hepatotoxic potential. Further, in vitro hepatotoxicity studies have been done to check the toxicity level in the synthesized compound. Compounds 5a, 5b, 5c and 9d were selected for further biological evaluation according to in vitro results. After that, CCl₄-induced animal model was used to evaluate in vivo anti-hepatotoxicity activity. Compound 5a with 52.99%, 59.3%, 79.34% and 5b with 52.16%, 57.65%, 75.10% revealed to be most promising for reduction in level of SGPT, SGOT and ALKP, respectively. Moreover, it was also observed that the compound 5a with 411.01%, 53.39% and 5b with 378.63%, 48.9% level of albumin and total protein were respectively. The induced-fit docking results of the compounds 5a and 5b reveal some essential binding information and exhibited desirable ADME properties, and obeyed Lipinski's rule of five. In addition, molecular dynamics studies for 100 ns further confirm the protein-ligand complex's stability, supporting the in vitro and in vivo data, and help in establishing the SAR of synthesized compounds. Two compounds, 5a and 5 b, exhibited higher anti-hepatotoxic activity than the standard drug silymarin.

Computational-Based Discovery of the Anti-Cancer Activities of Pyrrole-Based Compounds Targeting the Colchicine-Binding Site of Tubulin

Despite the tubulin-binding agents (TBAs) that are widely used in the clinic for cancer therapy, tumor resistance to TBAs (both inherited and acquired) significantly impairs their effectiveness, thereby decreasing overall survival (OS) and progression-free survival (PFS) rates, especially for the patients with metastatic, recurrent, and unresectable forms of the disease. Therefore, the development of novel effective drugs interfering with the microtubules' dynamic state remains a big challenge in current oncology. We report here about the novel ethyl 2-amino-1-(furan-2-carboxamido)-5-(2-aryl/tert-butyl-2-oxoethylidene)-4-oxo-4,5-dihydro-1H-pyrrole-3-carboxylates (EAPCs) exhibiting potent anti-cancer activities against the breast and lung cancer cell lines in vitro. This was due to their ability to inhibit tubulin polymerization and induce cell cycle arrest in M-phase. As an outcome, the EAPC-treated cancer cells exhibited a significant increase in apoptosis, which was evidenced by the expression of cleaved forms of PARP, caspase-3, and increased numbers of Annexin-V-positive cells. By using the in silico molecular modeling methods (e.g., induced-fit docking, binding metadynamics, and unbiased molecular dynamics), we found that EAPC-67 and -70 preferentially bind to the colchicine-binding site of tubulin. Lastly, we have shown that the EAPCs indicated above and colchicine utilizes a similar molecular mechanism to inhibit tubulin polymerization via targeting the T7 loop in the β-chain of tubulin, thereby preventing the conformational changes in the tubulin dimers required for their polymerization. Collectively, we identified the novel and potent TBAs that bind to the colchicine-binding site and disrupt the microtubule network. As a result of these events, the compounds induced a robust cell cycle arrest in M-phase and exhibited potent pro-apoptotic activities against the epithelial cancer cell lines in vitro.

Piperazine sulfonamides as DPP-IV inhibitors: Synthesis, induced-fit docking and in vitro biological evaluation

Diabetes mellitus is a chronic illness that needs persistent medical attention and continuous patient self-management to avoid acute complications. Dipeptidyl peptidase-IV (DPP-IV) inhibitors minimize glucagon and blood glucose levels by increasing the incretin levels, glucagon-like peptide (GLP-1) and glucose-dependent insulinotropic poly-peptide (GIP), leading to insulin secretion from pancreatic beta cells. In the present study, nine 1,4-bis(phenylsulfonyl) piperazine derivatives 1a-i were synthesized and identified using ¹H NMR, ¹³C NMR, MS and IR spectroscopies. These compounds were tested in vitro and showed inhibitory activity ranging from 11.2 to 22.6 % at 100 µmol L^-1 concentration. Piperazine sulfonamide derivatives were found to be promising DPP-IV inhibitors, where the presence of electron-withdrawing groups such as Cl (1a-c) improved the activity of the compounds more than electron-donating groups such as CH_{3 (} 1d-f) at the same position. Additionally, meta-substitution is disfavored (1b, 1e, 1g). Induced-fit docking studies suggested that the targeted compounds 1a-i occupy the binding domain of DPP-IV and form H-bonding with the backbones of R125, E205, E206, F357, K554, W629, Y631, Y662 and R669.

Computer-Aided Drug Discovery (CADD) Approaches for the Management of Neuropathic Pain

Neuropathic pain occurs due to physical damage, injury, or dysfunction of neuronal fibers. The pathophysiology of neuropathic pain is too complex. Therefore, an accurate and reliable prediction of the appropriate hits/ligands for the treatment of neuropathic pain is a challenging process. However, computer-aided drug discovery approaches contributed significantly to discovering newer hits/ligands for the treatment of neuropathic pain. The computational approaches like homology modeling, induced-fit molecular docking, structure-activity relationships, metadynamics, and virtual screening were cited in the literature for the identification of potential hit molecules against neuropathic pain. These hit molecules act as inducible nitric oxide synthase inhibitors, FLAT antagonists, TRPA1 modulators, voltage-gated sodium channel binder, cannabinoid receptor-2 agonists, sigma-1 receptor antagonists, etc. Sigma-1 receptor is a distinct type of opioid receptor and several patents were obtained for sigma-1 receptor antagonists for the treatment of neuropathic pain. These molecules were found to have a profound role in the management of neuropathic pain. The present review describes the validated therapeutic targets, potential chemical scaffolds, and crucial protein-ligand interactions for the management of neuropathic pain based on the recently reported computational methodologies of the present and past decades. The study can help the researcher to discover newer drugs/drug-like molecules against neuropathic pain.

Synthesis and Molecular Modeling of Novel 3,5-Bis(trifluoromethyl)benzylamino Benzamides as Potential CETP Inhibitors

BACKGROUND

There is an alarming spread of cases of lipid-disorders in the world that occur due to harmful lifestyle habits, hereditary risk influences, or as a result of other illnesses or medicines. Cholesteryl ester transfer protein (CETP) is a 476-residue lipophilic glycoprotein that helps in the transport of cholesteryl ester and phospholipids from the atheroprotective HDL to the proatherogenic LDL and VLDL. Inhibition of CETP leads to elevation of HDL cholesterol and reduction of LDL cholesterol and triglycerides, so it's considered a good target for the treatment of hyperlipidemia and its comorbidities.

OBJECTIVES

In this research synthesis, characterization, molecular modeling and biological evaluation of eight 3,5-bis(trifluoromethyl)benzylamino benzamides 9a-d and 10a-d were carried out.

METHODS

The synthesized molecules were characterized using ¹H-NMR, ¹³C-NMR, IR and HR-MS. They were in vitro biologically tested to estimate their CETP inhibitory activity.

RESULTS

These compounds offered inhibitory effectiveness ranging from 42.2% to 100% at a concentration of 10 µM. Compounds bearing unsubstituted three aromatic rings (9a) or ortho-CF₃ substituted (9b) were the most effective compounds among their analogs and showed IC₅₀ values of 1.36 and 0.69 μM, respectively. The high docking scores of 9a-d and 10a-d against 4EWS imply that they might be possible CETP inhibitors. Pharmacophore mapping results demonstrate that the series approves the fingerprint of CETP active inhibitors and therefore explains their high binding affinity against CETP binding site.

CONCLUSION

This work concludes that 3,5-bis(trifluoromethyl)benzylamino benzamides can serve as a promising CETP inhibitors lead compounds.

Crystallographic studies of piperazine derivatives of 3-methyl-5-spirofluorenehydantoin in search of structural features of P-gp inhibitors

5-Spirofluorenehydantoin derivatives show efflux modulating, cytotoxic and antiproliferative effects in sensitive and resistant mouse T-lymphoma cells. In order to extend the knowledge available about the pharmacophoric features responsible for the glycoprotein P (P-gp) inhibitory properties of arylpiperazine derivatives of 3-methyl-5-spirofluorenehydantoin, we have performed crystal structure analyses for 1-[3-(3'-methyl-2',4'-dioxospiro[fluorene-9,5'-imidazolidin]-1'-yl)propyl]-4-phenylpiperazine-1,4-diium dichloride monohydrate, C₂₉H₃₂N₄O₂²⁺·2Cl^-·H₂O (1), 3'-methyl-1'-{3-[4-(4-nitrophenyl)piperazin-1-yl]propyl}spiro[fluorene-9,5'-imidazolidine]-2',4'-dione, C₂₉H₂₉N₅O₄·H₂O (2), 3'-methyl-1'-{5-[4-(4-nitrophenyl)piperazin-1-yl]pentyl}spiro[fluorene-9,5'-imidazolidine]-2',4'-dione, C₃₁H₃₃N₅O₄ (3), and 1-benzyl-4-[5-(3'-methyl-2',4'-dioxospiro[fluorene-9,5'-imidazolidin]-1'-yl)pentyl]piperazine-1,4-diium dichloride 0.613-hydrate, C₃₂H₃₈N₄O₂²⁺·2Cl^-·0.613H₂O (4). Structure 3 is anhydrous but the other three structures crystallize with water present. The investigated compounds crystallize in the monoclinic crystal system, with the space group P2₁/n for 1 and 3, and P2₁/c for 2 and 4. The cations of salts 1 and 4 are doubly protonated, with the protons located on the N atoms of the piperazine rings. The packing of 1 and 4 in the crystals is dominated by intermolecular N-H...Cl and O-H...Cl hydrogen bonds. In the crystal structure of 2, the intermolecular interactions are dominated by O-H...O and O-H...N hydrogen bonds, while in 3, which is lacking in classic hydrogen-bond donors, it is C-H...O contacts that dominate. Additionally, we have performed induced-fit docking studies for the investigated compounds docked to the P-gp human homology model.

Immunoproteasome and Non-Covalent Inhibition: Exploration by Advanced Molecular Dynamics and Docking Methods

The selective inhibition of immunoproteasome is a valuable strategy to treat autoimmune, inflammatory diseases, and hematologic malignancies. Recently, a new series of amide derivatives as non-covalent inhibitors of the β1i subunit with K_i values in the low/submicromolar ranges have been identified. Here, we investigated the binding mechanism of the most potent and selective inhibitor, N-benzyl-2-(2-oxopyridin-1(2H)-yl)propanamide (1), to elucidate the steps from the ligand entrance into the binding pocket to the ligand-induced conformational changes. We carried out a total of 400 ns of MD-binding analyses, followed by 200 ns of plain MD. The trajectories clustering allowed identifying three representative poses evidencing new key interactions with Phe31 and Lys33 together in a flipped orientation of a representative pose. Further, Binding Pose MetaDynamics (BPMD) studies were performed to evaluate the binding stability, comparing 1 with four other inhibitors of the β1i subunit: N-benzyl-2-(2-oxopyridin-1(2H)-yl)acetamide (2), N-cyclohexyl-3-(2-oxopyridin-1(2H)-yl)propenamide (3), N-butyl-3-(2-oxopyridin-1(2H)-yl)propanamide (4), and (S)-2-(2-oxopyridin-1(2H)-yl)-N,4-diphenylbutanamide (5). The obtained results in terms of free binding energy were consistent with the experimental values of inhibition, confirming 1 as a lead compound of this series. The adopted methods provided a full dynamic description of the binding events, and the information obtained could be exploited for the rational design of new and more active inhibitors.

Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery

The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q² > 0.63 and Q² > 0.67 for GLP-1R and GCGR, respectively). In addition, we performed a ligand annotation using recent cryogenic-electron microscopy (cryo-EM) and X-ray crystallographic data on small-molecule complexes of GCGR and GLP-1R. As a result, we assigned GLP-1R and GCGR actives deposited in ChEMBL to four small-molecule binding sites occupied by positive and negative allosteric modulators and a full agonist. Annotated compounds were added to our recently released repository of GPCR data.

Screening of potential drug for Alzheimer's disease: a computational study with GSK-3 β inhibition through virtual screening, docking, and molecular dynamics simulation

The global impact of Alzheimer's disease (AD) necessitates intensive research to find appropriate and effective drugs. Many studies in AD suggested beta-amyloid plaques and neurofibrillary tangles-associated tau protein as the key targets for drug development. On the other hand, it is proved that triggering of Glycogen Synthase Kinase-3β (GSK-3β) also cause AD, therefore, GSK-3β is a potential drug target to combat AD. We, in this study, investigated the ability of small molecules to inhibit GSK-3β through virtual screening, Absorption, Distribution, Metabolism, and Excretion (ADME), induced-fit docking (IFD), molecular dynamics simulation, and binding free energy calculation. Besides, molecular docking was performed to reveal the binding and interaction of the ligand at the active site of GSK-3β. We found two compounds such as 6961 and 6966, which exhibited steady-state interaction with GSK-3β for 30 ns in molecular dynamics simulation. The compounds (6961 and 6966) also achieved a docking score of -9.05 kcal/mol and -8.11 kcal/mol, respectively, which is relatively higher than the GSK-3β II inhibitor (-6.73 kcal/mol). The molecular dynamics simulation revealed that the compounds have a stable state during overall simulation time, and lesser root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) values compared with co-crystal. In conclusion, we suggest the two compounds (6966 and 6961) as potential leads that could be utilized as effective inhibitors of GSK-3β to combat AD.

Peramivir binding affinity with influenza A neuraminidase and research on its mutations using an induced-fit docking approach

Influenza A virus (IAV) has caused epidemic infections worldwide, with many strains resistant to inhibitors of a surface protein, neuraminidase (NA), due to point mutations on its structure. A novel NA inhibitor named peramivir was recently approved, but no exhaustive computational research regarding its binding affinity with wild-type and mutant NA has been conducted. In this study, a thorough investigation of IAV-NA PDB entries of 9 subtypes is described, providing a list of residues constituting the protein-ligand binding sites. The results of induced-fit docking approach point out key residues of wild-type NA participating in hydrogen bonds and/or ionic interactions with peramivir, among which Arg 368 is responsible for a peramivir-NA ionic interaction. Mutations on this residue greatly reduced the binding affinity of peramivir with NA, with 3 mutations R378Q, R378K and R378L (NA6) capable of deteriorating the docking performance of peramivir by over 50%. 200 compounds from 6-scaffolds were docked into these 3 mutant versions, revealing 18 compounds giving the most promising results. Among them, CMC-2012-7-1527-56 (benzoic acid scaffold, IC50 = 32 nM in inhibitory assays with IAV) is deemed the most potential inhibitor of mutant NA resisting both peramivir and zanamivir, and should be further investigated.

In Silico Insights towards the Identification of NLRP3 Druggable Hot Spots

NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) activation has been linked to several chronic pathologies, including atherosclerosis, type-II diabetes, fibrosis, rheumatoid arthritis, and Alzheimer’s disease. Therefore, NLRP3 represents an appealing target for the development of innovative therapeutic approaches. A few companies are currently working on the discovery of selective modulators of NLRP3 inflammasome. Unfortunately, limited structural data are available for this target. To date, MCC950 represents one of the most promising noncovalent NLRP3 inhibitors. Recently, a possible region for the binding of MCC950 to the NLRP3 protein was described but no details were disclosed regarding the key interactions. In this communication, we present an in silico multiple approach as an insight useful for the design of novel NLRP3 inhibitors. In detail, combining different computational techniques, we propose consensus-retrieved protein residues that seem to be essential for the binding process and for the stabilization of the protein–ligand complex.

An insight into selective and potent inhibition of histone deacetylase 8 through induced-fit docking, pharmacophore modeling and QSAR studies

Histone deacetylase 8 (HDAC8) has emerged as an important therapeutic target due to its involvement in various cancerous and neurodegenerative disease states. Since pan HDAC inhibition has been linked to various side effects, the need of the hour is to develop inhibitors truly selective for one isoform. This work attempts to explore the structural basis of selective HDAC8 inhibition by docking, pharmacophore and 3 D QSAR studies of 53 highly potent and highly selective triazol-based hydroxamic acid inhibitors. The binding modes of these novel inhibitors have been explored via Glide XP (Extra Precision) and induced-fit docking (IFD) strategies. The IFD poses of highly active and selective inhibitors showed conformational changes in active site residues like Trp141, Phe152 and Phe208, which were further verified by molecular dynamics simulations. A new CH-π interaction, which is atypical of HDAC inhibitors, was also observed in case of some highly selective inhibitors. Two pharmacophore models have been proposed; one highlights the structural basis of potency of these inhibitors and the other focuses on the selectivity. The corresponding QSAR models, obtained from alignment of the inhibitors as per the proposed pharmacophore models, are highly statistically significant. These models highlight the importance of size of the hydrophobic and aromatic groups present in the inhibitors and their contribution to activity of the inhibitors. The ADMET properties of the ligand library have also been analyzed and the predicted descriptors have been correlated with activity using principal components analysis to gain insight into the effect of pharmacokinetic properties on the activity.Communicated by Ramaswamy H. Sarma.

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

The number of solved G-protein-coupled receptor (GPCR) crystal structures has expanded rapidly, but most GPCR structures remain unsolved. Therefore, computational techniques, such as homology modeling, have been widely used to produce the theoretical structures of various GPCRs for structure-based drug design (SBDD). Due to the low sequence similarity shared by the transmembrane domains of GPCRs, accurate prediction of GPCR structures by homology modeling is quite challenging. In this study, angiotensin II type I receptor (AT1R) was taken as a typical case to assess the reliability of class A GPCR homology models for SBDD. Four homology models of angiotensin II type I receptor (AT1R) at the inactive state were built based on the crystal structures of CXCR4 chemokine receptor, CCR5 chemokine receptor, and δ-opioid receptor, and refined through molecular dynamics (MD) simulations and induced-fit docking, to allow for backbone and side-chain flexibility. Then, the quality of the homology models was assessed relative to the crystal structures in terms of two criteria commonly used in SBDD: prediction accuracy of ligand-binding poses and screening power of docking-based virtual screening. It was found that the crystal structures outperformed the homology models prior to any refinement in both assessments. MD simulations could generally improve the docking results for both the crystal structures and homology models. Moreover, the optimized homology model refined by MD simulations and induced-fit docking even shows a similar performance of the docking assessment to the crystal structures. Our results indicate that it is possible to establish a reliable class A GPCR homology model for SBDD through the refinement by integrating multiple molecular modeling techniques.

Molecular Features Underlying Selectivity in Chicken Bitter Taste Receptors

Chickens sense the bitter taste of structurally different molecules with merely three bitter taste receptors (Gallus gallus taste 2 receptors, ggTas2rs), representing a minimal case of bitter perception. Some bitter compounds like quinine, diphenidol and chlorpheniramine, activate all three ggTas2rs, while others selectively activate one or two of the receptors. We focus on bitter compounds with different selectivity profiles toward the three receptors, to shed light on the molecular recognition complexity in bitter taste. Using homology modeling and induced-fit docking simulations, we investigated the binding modes of ggTas2r agonists. Interestingly, promiscuous compounds are predicted to establish polar interactions with position 6.51 and hydrophobic interactions with positions 3.32 and 5.42 in all ggTas2rs; whereas certain residues are responsible for receptor selectivity. Lys^3.29 and Asn^3.36 are suggested as ggTas2r1-specificity-conferring residues; Gln^6.55 as ggTas2r2-specificity-conferring residue; Ser^5.38 and Gln^7.42 as ggTas2r7-specificity conferring residues. The selectivity profile of quinine analogs, quinidine, epiquinidine and ethylhydrocupreine, was then characterized by combining calcium-imaging experiments and in silico approaches. ggTas2r models were used to virtually screen BitterDB compounds. ~50% of compounds known to be bitter to human are likely to be bitter to chicken, with 25, 20, 37% predicted to be ggTas2r1, ggTas2r2, ggTas2r7 agonists, respectively. Predicted ggTas2rs agonists can be tested with in vitro and in vivo experiments, contributing to our understanding of bitter taste in chicken and, consequently, to the improvement of chicken feed.

Structure-based QSAR, molecule design and bioassays of protease-activated receptor 1 inhibitors

Quantitative structure-activity relationship (QSAR) studies were performed on a series of protease-activated receptor 1 (PAR1) inhibitors to identify the key structural features responsible for their biological activity. Induced-fit docking (IFD) was used to explore the active mechanisms of all PAR1 inhibitors at the active pocket of PAR1, and the best plausible conformation was determined by IFD for further QSAR studies. Based on the best plausible conformation, structure-based descriptors and ligand descriptors incorporating the ligand-receptor interaction were calculated. The random forest method was used to select important descriptors and build the 2D-QSAR model. The results of the 2D-QSAR model gave a squared correlation coefficient (R²) of 0.937, a prediction squared correlation coefficient (R²_pred) of 0.845 and a mean square error (MSE) of 0.056. Furthermore, a 3D-QSAR model was developed via topomer comparative molecular field analysis (Topomer CoMFA), resulting in an R² of 0.938, a cross-validated Q² of 0.503 and a R²_pred of 0.758. Based on the developed QSAR model, Topomer search was used for virtual screening of the R2 fragment in lead-like inhibitors from the National Cancer Institute (NCI) database, which contains 260,000 molecules. Eighty-two compounds were designed with different R2 fragments, and four of these compounds were selected for further biological testing. All four compounds showed inhibitory potency against PAR1.

Interrogating the Molecular Basis for Substrate Recognition in Serotonin and Dopamine Transporters with High-Affinity Substrate-Based Bivalent Ligands

The transporters for the neurotransmitters serotonin and dopamine (SERT and DAT, respectively) are targets for drugs used in the treatment of mental disorders and widely used drugs of abuse. Studies of prokaryotic homologues have advanced our structural understanding of SERT and DAT, but it still remains enigmatic whether the human transporters contain one or two high-affinity substrate binding sites. We have designed and employed 24 bivalent ligands possessing a highly systematic combination of substrate moieties (serotonin and/or dopamine) and aliphatic or poly(ethylene glycol) spacers to reveal insight into substrate recognition in SERT and DAT. An optimized bivalent ligand comprising two serotonin moieties binds SERT with 3,800-fold increased affinity compared to that of serotonin, suggesting that the human transporters have two distinct substrate binding sites. We show that the bivalent ligands are inhibitors of SERT and an experimentally validated docking model suggests that the bivalent compounds bind with one substrate moiety in the central binding site (the S1 site), whereas the other substrate moiety binds in a distinct binding site (the S2 site). A systematic study of nonconserved SERT/DAT residues surrounding the proposed binding region showed that nonconserved binding site residues do not contribute to selective recognition of substrates in SERT or DAT. This study provides novel insight into the molecular basis for substrate recognition in human transporters and provides an improved foundation for the development of new drugs targeting SERT and DAT.

An in silico approach towards the identification of novel inhibitors of the TLR-4 signaling pathway

Precise functioning and fine-tuning of Toll-like receptor 4 (TLR4) signaling is a critical requirement for the smooth functioning of the innate immune system, since aberrant TLR4 activation causes excessive production of pro-inflammatory cytokines and interferons. This can result in life threatening conditions such as septic shock and other inflammatory disorders. The TRIF-related adaptor molecule (TRAM) adaptor protein is unique to the TLR4 signaling pathway and abrogation of TRAM-mediated TLR4 signaling is a promising strategy for developing therapeutics aimed at disrupting TRAM interactions with other components of the TLR4 signaling complex. The VIPER motif from the vaccinia virus-producing protein, A46 has been reported to disrupt TRAM-TLR4 interactions. We have exploited this information, in combination with homology modeling and docking approaches, to identify a potential binding site on TRAM lined by the BB loop and αC helix. Virtual screening of commercially available small molecules targeting the binding site enabled to short-list 12 small molecules to abrogate TRAM-mediated TLR4 signaling. Molecular dynamics and molecular mechanics calculations have been performed for the analysis of these receptor-ligand interactions.

Computational studies on the resistance of penicillin-binding protein 2B (PBP2B) of wild-type and mutant strains of Streptococcus pneumoniae against β-lactam antibiotics

Mutations within transpeptidase domain of penicillin-binding protein 2B of the strains of Streptococcus pneumoniae leads to resistance against β-lactam antibiotics. To uncover the important residues responsible for sensitivity and resistance, the recently determined three dimensional structures of penicillin-binding protein 2B of both wild-type R6 (sensitive) and mutant 5204 (resistant) strains along with the predicted structures of other mutant strains G54, Hungary19A-6 and SP195 were considered for the interaction study with β-lactam antibiotics using induced-fit docking of Schrödinger. Associated binding energies of the complexes and their intermolecular interactions in the binding site clearly show that the wild-type R6 as sensitive, mutant strains 5204 and G54 as highly resistant, and the mutant strains Hungary19A-6 and SP195 as intermediate resistant. The study also reveals that the mutant strains Hungary19A-6 and SP195 exhibit intermediate resistant because of the existence of mutations till the intermediate 538th and 516th positions, respectively, and not till the end of the C-terminus. Furthermore, our investigations show that if the mutations are extended till the end of the C terminus, then the antibiotic resistance of induced-mutated strains increases from intermediate to high as in the strains 5204 and G54. The binding patterns obtained in the study are useful in designing potential inhibitors against multidrug resistant S. pneumoniae.