Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches.

DNA methyl transferases (DNMTs) are one of the crucial epigenetic modulators associated with a wide variety of cancer conditions. Among the DNMT isoforms, DNMT1 is correlated with bladder, pancreatic, and breast cancer, as well as acute myeloid leukemia and esophagus squamous cell carcinoma. Therefore, the inhibition of DNMT1 could be an attractive target for combating cancers and other metabolic disorders. The disadvantages of the existing nucleoside and non-nucleoside DNMT1 inhibitors are the main motive for the discovery of novel promising inhibitors. Here, pharmacophore modeling, 3D-QSAR, and e-pharmacophore modeling of DNMT1 inhibitors were performed for the large fragment database screening. The resulting fragments with high dock scores were combined into molecules. The current study revealed several constitutional pharmacophoric features that can be essential for selective DNMT1 inhibition. The fragment docking and virtual screening identified 10 final hit molecules that exhibited good binding affinities in terms of docking score, binding free energies, and acceptable ADME properties. Also, the modified lead molecules (GL1b and GL2b) designed in this study showed effective binding with DNMT1 confirmed by their docking scores, binding free energies, 3D-QSAR predicted activities and acceptable drug-like properties. The MD simulation studies also suggested that leads (GL1b and GL2b) formed stable complexes with DNMT1. Therefore, the findings of this study can provide effective information for the development/identification of novel DNMT1 inhibitors as effective anticancer agents.

Developing Dynamic Structure-Based Pharmacophore and ML-Trained QSAR Models for the Discovery of Novel Resistance-Free RET Tyrosine Kinase Inhibitors Through Extensive MD Trajectories and NRI Analysis.

Activation of RET tyrosine kinase plays a critical role in the pathogenesis of various cancers, including non-small cell lung cancer, papillary thyroid cancers, multiple endocrine neoplasia type 2A and 2B (MEN2A, MEN2B), and familial medullary thyroid cancer. Gene fusions and point mutations in the RET proto-oncogene result in constitutive activation of RET signaling pathways. Consequently, developing effective inhibitors to target RET is of utmost importance. Small molecules have shown promise as inhibitors by binding to the kinase domain of RET and blocking its enzymatic activity. However, the emergence of resistance due to single amino acid changes poses a significant challenge. In this study, a structure-based dynamic pharmacophore-driven approach using E-pharmacophore modeling from molecular dynamics trajectories is proposed to select low-energy favorable hypotheses, and ML-trained QSAR models to predict pIC50 values of compounds. For this aim, extensive small molecule libraries were screened using developed ligand-based models, and potent compounds that are capable of inhibiting RET activation were proposed.

Identification of Potential Inhibitors Targeting GTPase-Kirsten RAt Sarcoma Virus (K-Ras) Driven Cancers via E-Pharmacophore-Based Virtual Screening and Drug Repurposing Approach.

BACKGROUND: Mutations in the K-Ras gene are among the most frequent genetic alterations in various cancers, and inhibiting RAS signaling has shown promising results in treating solid tumors. However, finding effective drugs that can bind to the RAS protein remains challenging. This drove us to explore new compounds that could inhibit tumor growth, particularly in cancers that harbor K-Ras mutations. METHODS: Our study used bioinformatic techniques such as E-pharmacophore virtual screening, molecular simulation, principal component analysis (PCA), extra precision (XP) docking, and ADMET analyses to identify potential inhibitors for K-Ras mutants G12C and G12D. RESULTS: In our study, we discovered that inhibitors such as afatinib, osimertinib, and hydroxychloroquine strongly inhibit the G12C mutant. Similarly, hydroxyzine, zuclopenthixol, fluphenazine, and doxapram were potent inhibitors for the G12D mutant. Notably, all six of these molecules exhibit a high binding affinity for the H95 cryptic groove present in the mutant structure. These molecules exhibited a unique affinity mechanism at the molecular level, which was further enhanced by hydrophobic interactions. Molecular simulations and PCA revealed the formation of stable complexes within switch regions I and II. This was particularly evident in three complexes: G12C-osimertinib, G12D-fluphenazine, and G12D-zuclopenthixol. Despite the dynamic nature of switches I and II in K-Ras, the interaction of inhibitors remained stable. According to QikProp results, the properties and descriptors of the selected molecules fell within an acceptable range compared to sotorasib. CONCLUSIONS: We have successfully identified potential inhibitors of the K-Ras protein, laying the groundwork for the development of targeted therapies for cancers driven by K-Ras mutations.

Identification of 1H-purine-2,6-dione derivative as a potential SARS-CoV-2 main protease inhibitor: molecular docking, dynamic simulations, and energy calculations.

The rapid spread of the coronavirus since its first appearance in 2019 has taken the world by surprise, challenging the global economy, and putting pressure on healthcare systems across the world. The introduction of preventive vaccines only managed to slow the rising death rates worldwide, illuminating the pressing need for developing effective antiviral therapeutics. The traditional route of drug discovery has been known to require years which the world does not currently have. In silico approaches in drug design have shown promising results over the last decade, helping to decrease the required time for drug development. One of the vital non-structural proteins that are essential to viral replication and transcription is the SARS-CoV-2 main protease (Mpro). Herein, using a test set of recently identified COVID-19 inhibitors, a pharmacophore was developed to screen 20 million drug-like compounds obtained from a freely accessible Zinc database. The generated hits were ranked using a structure based virtual screening technique (SBVS), and the top hits were subjected to in-depth molecular docking studies and MM-GBSA calculations over SARS-COV-2 Mpro. Finally, the most promising hit, compound (1), and the potent standard (III) were subjected to 100 ns molecular dynamics (MD) simulations and in silico ADME study. The result of the MD analysis as well as the in silico pharmacokinetic study reveal compound 1 to be a promising SARS-Cov-2 MPro inhibitor suitable for further development.

Structure-based pharmacophore modeling, virtual screening approaches to identifying the potent hepatitis C viral protease and polymerase novel inhibitors.

Hepatitis C is an infectious disease that leads to acute and chronic liver illnesses. Currently, there are no effective vaccines against this deadly virus. Direct acting antiviral (DAA) drugs are given in the combination with ribavirin and pegylated interferon which lead to adverse effects. Through in silico analysis, the structure-based docking study was performed against NS3/4A protease and NS5B polymerase proteins of HCV. In the current study, multiple e-pharmacophore-based virtual screening methods such as HTVS, SP, and XP were carried out to screen natural compounds and enamine databases. Our result outcomes revealed that CID AE-848/13196185 and CID AE-848/36959205 compounds show good binding interactions with protease protein. In addition, CID 15081408 and CID 173568 show better binding interactions with the polymerase protein. Further to validate the docking results, we performed molecular dynamics simulation for the top hit compounds bound with protease and polymerase proteins to illustrate conformational differences in the stability compared with the active site of the cocrystal inhibitor. Thus, the current study emphasizes these compounds could be an effective drug to treat HCV.

Identification of novel discoidin domain receptor 1 (DDR1) inhibitors using E-pharmacophore modeling, structure-based virtual screening, molecular dynamics simulation and MM-GBSA approaches.

Dysregulation of the discoidin domain receptor (DDR1), a collagen-activated receptor tyrosine kinase, has been linked to several human cancer diseases including non-small cell lung carcinoma (NSCLC), ovarian cancer, glioblastoma, and breast cancer, in addition to several inflammatory and neurological conditions. Although there are some selective DDR1 inhibitors that have been discovered during the last two decades, a combination of elevated cytotoxicity, kinome selectivity and/or poor DMPK profile has prevented more in-depth studies from being performed. As such, no DDR1 inhibitor has reached clinical investigation to date, forming an urgent need to develop specific DDR1 inhibitor(s) using various drug discovery means. However, the recent discovery of VU6015929, a potent and selective DDR1 kinase inhibitor, with enhanced physiochemical and DMPK properties in addition to its clean kinome profile marked a milestone in the development of DDR1 inhibitors. Herein, VU6015929 was used to construct a 3D e-pharmacophore model which was validated via calculating the difference of score between the active compounds and decoys. The validated e-pharmacophore model was then utilized to screen 20 million drug-like compounds obtained from the freely accessible Zinc database. The generated hits were ranked using high throughput virtual screening technique (HTVS), and the top 8 small molecules were subjected to a molecular docking study and MM-GBSA calculations. Protein-ligand complexes of compounds 1, 2, 3 and the standard compound (VU6015929) were performed for 100 ns and compared with the DDR1 unbound protein state and the DDR1 bound to a co-crystallized ligand. The molecular docking, MD and MM-GBSA outputs revealed compounds 1-3 as potential DDR1 inhibitors, with compound 2 displaying superior binding affinity, comparable binding stability and average binding free energy for the ligand-enzyme complex compared to VU6015929.

Discovery of methoxy-naphthyl linked N-(1-benzylpiperidine) benzamide as a blood-brain permeable dual inhibitor of acetylcholinesterase and butyrylcholinesterase.

The cholinesterase enzymes play a vital role in maintaining balanced levels of the neurotransmitter acetylcholine in the central nervous system. However, the overexpression of these enzymes results in hampered neurotransmission. Both the major forms of cholinesterase enzymes viz. acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) play a crucial role in blocking neurotransmission; therefore, in recent years, a strategy of dual cholinesterase inhibition is being explored. Herein, we developed an energy-optimized e-pharmacophore hypothesis AHHPRR from AChE-donepezil complex and screened a set of 15 scaffolds that were designed imaginarily. The ligand with N-(1-benzylpyridinium) benzamide framework has shown the highest fitness and volume score, which was chosen for synthesis and validation. A series of pyridinium benzamides were synthesized and screened for cholinesterase inhibition that led to the identification of 7b, a naphthalene containing N-(1-benzylpiperidine) benzamide as a potent dual AChE and BChE inhibitor with IC50 values of 0.176, and 0.47 µM, respectively. The kinetic study indicated that 7b inhibits AChE in a non-competitive manner with Ki value of 0.21 µM, and BChE in a mixed-fashion with Ki of 0.15 µM. The observed mode of inhibition was corroborated with molecular docking studies. The MD simulation studies pointed out that both AChE and BChE undergo low conformational changes in complex with 7b. The benzamide 7b displayed high BBB permeability in PAMPA assay, which indicates its potential for further exploration in preclinical studies for Alzheimer's disease.

Novel tumor necrosis factor-α (TNF-α) inhibitors from small molecule library screening for their therapeutic activity profiles against rheumatoid arthritis using target-driven approaches and binary QSAR models.

Tumor necrosis factor alpha (TNF-α) is a multifunctional cytokine that acts as a central biological mediator for critical immune functions, including inflammation, infection, and antitumor responses. It plays pivotal role in autoimmune diseases like rheumatoid arthritis (RA). The synthetic antibodies etanercept, infliximab, and adalimumab are approved drugs for the treatment of inflammatory diseases bind to TNF-α directly, preventing its association with the tumor necrosis factor receptor (TNFR). These biologics causes serious side effects such as triggering an autoimmune anti-antibody response or the weakening of the body's immune defenses. Therefore, alternative small-molecule based therapies for TNF-α inhibition is a hot topic both in academia and industry. Most of small-molecule inhibitors reported in the literature target TNF-α, indirectly. In this study, combined in silico approaches have been applied to better understand the important direct interactions between TNF-α and small inhibitors. Our effort executed with the extensive literature review to select the compounds that inhibit TNF-α. High-throughput structure-based and ligand-based virtual screening methods are applied to identify TNF-α inhibitors from 3 different small molecule databases (∼256.000 molecules from Otava drug-like green chemical collection, ∼ 500.000 molecules from Otava Tangible database, ∼2.500.000 Enamine small molecule database) and ∼240.000 molecules from ZINC natural products libraries. Moreover, therapeutic activity prediction, as well as pharmacokinetic and toxicity profiles are also investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information, uses binary QSAR models. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular Dynamics (MD) simulations were also performed for selected hits to investigate their detailed structural and dynamical analysis beyond docking studies. As a result, at least one hit from each database were identified as novel TNF-α inhibitors after comprehensive virtual screening, multiple docking, e-Pharmacophore modeling (structure-based pharmacophore modeling), MD simulations, and MetaCore/MetaDrug analysis. Identified hits show predicted promising anti-arthritic activity and no toxicity. Communicated by Ramaswamy H. Sarma.

Proposing novel TNFα direct inhibitor Scaffolds using fragment-docking based e-pharmacophore modeling and binary QSAR-based virtual screening protocols pipeline.

Tumor necrosis factor alpha (TNFα) is a homotrimer protein that plays a pivotal role for critical immune functions, including infection, inflammation and antitumor responses. It also plays a primary role in autoimmune diseases like rheumatoid arthritis (RA). So far, only biological therapeutics like infliximab, etanercept, and adalimumab are available as treatment of inflammatory diseases. They directly bind to TNFα and interrupt its binding to its receptor protein tumor necrosis factor receptor (TNFR). However, they may also cause serious side effects such as activating an autoimmune anti-antibody response or the weakening of the body's immune defenses. Thus, small molecule-based therapies can be considered as alternative methods. In this study, a novel method is applied to develop energetically optimized, structure-based pharmacophore models for rapid in silico drug screening. Fragment-based docking results were used in the construction of an universal e-pharmacophore model development. The developed model is then used for screening of small-molecule library Specs-screening compounds (Specs-SC) which includes more than 200.000 drug-like molecules. In another approach, binary QSAR-based models were used to screen Specs-SC, as well as Specs-natural products (NP) which has around 750 compounds, and a library of drugs registered or approved for use in humans NIH's NCGC pharmaceutical collection (NPC) which has around 7500 molecules. The MetaCore/MetaDrug platform was used for binary QSAR models for therapeutic activity prediction as well as pharmacokinetic and toxicity profile predictions of screening molecules. This platform is constructed based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Molecular docking and molecular dynamics (MD) simulations were performed for the selected hit molecules. As target protein, both homodimer and homotrimer forms of TNFα were considered. The screening results showed that indinavir and medroxalol from NPC chemical library and a set of compounds (AT-057/43115940, AP-970/42897107, AK-968/41925665, AI-204/31679053, AN-648/41666950, AN-698/42006940) from Specs-SC database were identified as safe and active direct inhibitors of TNFα.

Analysis of the Glutamate Agonist LY404,039 Binding to Nonstatic Dopamine Receptor D2 Dimer Structures and Consensus Docking.

Dopamine receptor D2 (D2R) plays an important role in the human central nervous system and is a focal target of antipsychotic agents. The D2HighR and D2LowR dimeric models previously developed by our group are used to investigate the prediction of binding affinity of the LY404,039 ligand and its binding mechanism within the catalytic domain. The computational data obtained using molecular dynamics simulations fit well with the experimental results. The calculated binding affinities of LY404,039 using MM/PBSA for the D2HighR and D2LowR targets were -12.04 and -9.11 kcal/mol, respectively. The experimental results suggest that LY404,039 binds to D2HighR and D2LowR with binding affinities (Ki) of 8.2 and 1640 nM, respectively. The high binding affinity of LY404,039 in terms of binding to [3H]domperidone was inhibited by the presence of a guanine nucleotide, indicating an agonist action of the drug at D2HighR. The interaction analysis demonstrated that while Asp114 was among the most critical amino acids for D2HighR binding, residues Ser193 and Ser197 were significantly more important within the binding cavity of D2LowR. Molecular modeling analyses are extended to ensemble docking as well as structure-based pharmacophore model (E-pharmacophore) development using the bioactive conformation of LY404,039 at the binding pocket as a template and screening of small-molecule databases with derived pharmacophore models.

Investigation of PDE5/PDE6 and PDE5/PDE11 selective potent tadalafil-like PDE5 inhibitors using combination of molecular modeling approaches, molecular fingerprint-based virtual screening protocols and structure-based pharmacophore development.

The essential biological function of phosphodiesterase (PDE) type enzymes is to regulate the cytoplasmic levels of intracellular second messengers, 3',5'-cyclic guanosine monophosphate (cGMP) and/or 3',5'-cyclic adenosine monophosphate (cAMP). PDE targets have 11 isoenzymes. Of these enzymes, PDE5 has attracted a special attention over the years after its recognition as being the target enzyme in treating erectile dysfunction. Due to the amino acid sequence and the secondary structural similarity of PDE6 and PDE11 with the catalytic domain of PDE5, first-generation PDE5 inhibitors (i.e. sildenafil and vardenafil) are also competitive inhibitors of PDE6 and PDE11. Since the major challenge of designing novel PDE5 inhibitors is to decrease their cross-reactivity with PDE6 and PDE11, in this study, we attempt to identify potent tadalafil-like PDE5 inhibitors that have PDE5/PDE6 and PDE5/PDE11 selectivity. For this aim, the similarity-based virtual screening protocol is applied for the "clean drug-like subset of ZINC database" that contains more than 20 million small compounds. Moreover, molecular dynamics (MD) simulations of selected hits complexed with PDE5 and off-targets were performed in order to get insights for structural and dynamical behaviors of the selected molecules as selective PDE5 inhibitors. Since tadalafil blocks hERG1 K channels in concentration dependent manner, the cardiotoxicity prediction of the hit molecules was also tested. Results of this study can be useful for designing of novel, safe and selective PDE5 inhibitors.