A Computational Approach for the Discovery of Novel DNA Methyltransferase Inhibitors

Nowadays, the explosion of knowledge in the field of epigenetics has revealed new pathways toward the treatment of multifactorial diseases, rendering the key players of the epigenetic machinery the focus of today's pharmaceutical landscape. Among epigenetic enzymes, DNA methyltransferases (DNMTs) are first studied as inhibition targets for cancer treatment. The increasing clinical interest in DNMTs has led to advanced experimental and computational strategies in the search for novel DNMT inhibitors. Considering the importance of epigenetic targets as a novel and promising pharmaceutical trend, the present study attempted to discover novel inhibitors of natural origin against DNMTs using a combination of structure and ligand-based computational approaches. Particularly, a pharmacophore-based virtual screening was performed, followed by molecular docking and molecular dynamics simulations in order to establish an accurate and robust selection methodology. Our screening protocol prioritized five natural-derived compounds, derivatives of coumarins, flavones, chalcones, benzoic acids, and phenazine, bearing completely diverse chemical scaffolds from FDA-approved "Epi-drugs". Their total DNMT inhibitory activity was evaluated, revealing promising results for the derived hits with an inhibitory activity ranging within 30-45% at 100 µM of the tested compounds.

Structural Models for a Series of Allosteric Inhibitors of IGF1R Kinase

The allosteric inhibition of Insulin-like Growth Factor Receptor 1 Kinase (IGF1RK) is a potential strategy to overcome selectivity barriers in targeting receptor tyrosine kinases. We constructed structural models of a series of 12 indole-butyl-amine derivatives which have been reported as allosteric inhibitors of IGF1RK. We further studied dynamics and interactions of each inhibitor in the allosteric pocket via all-atom explicit-solvent molecular dynamics (MD) simulations. We discovered that a bulky carbonyl substitution at the R1 indole ring is structurally unfavorable for inhibitor binding in the IGF1RK allosteric pocket. Moreover, we found that the most potent derivative (termed C11) acquires a distinct conformation, forming an allosteric pocket channel with better shape complementarity and interactions with the receptor. In addition to a hydrogen bonding interaction with V1063, the cyano derivative C11 forms a stable hydrogen bond with M1156, which is responsible for its unique binding conformation in the allosteric pocket. Our findings show that the position of chemical substituents at the R1 indole ring with different pharmacophore features influences molecular interactions and binding conformations of the indole-butyl-amine derivatives, hence dramatically affecting their potencies. Our results provide a structural framework for the design of allosteric inhibitors with improved affinities and specificities against IGF1RK.

In silico analysis of small molecule compounds based on pharmacophore- and backbone-leaping peroxiredoxin 1 (PRDX1) inhibitors

Abnormal expression of PRDX has been found to play a significant role in the growth of colorectal cancer and other types of tumors. Despite the identification of several PRDX1 inhibitory compounds in recent years, none of them have been utilized in clinical treatments. Therefore, we conducted a virtual screening of 210,331 small molecules from the SPECS library using PRDX1 and multiple methods. From this screening, we identified 13 compounds with the highest scores from the molecular docking analysis. To further validate the accuracy of our pharmacophore model, we constructed a structure-based pharmacophore model and analyzed the receiver operating characteristic curve (ROC curve). Through this process, we selected nine compounds using skeleton jumping and virtual screening based on the highest pharmacophore model scores. Subsequently, we examined the ADMET properties of these nine compounds to assess their drug-forming potential, resulting in three compounds with the best drug properties. Finally, we assessed the binding stability of these three candidate molecules to proteins using molecular dynamics and MM-PBSA calculations. After a comprehensive evaluation, we found that compounds 6 and 9 formed stable complexes with PRDX1 proteins and could potentially serve as competitive inhibitors of PRDX1 substrates.Communicated by Ramaswamy H. Sarma.

The discovery of a novel IκB kinase β inhibitor based on pharmacophore modeling, virtual screening and biological evaluation

Background: IκB kinase β (IKKβ) plays a pivotal role in the NF-κB signaling pathway and is considered a promising therapeutic target for various diseases. Materials & methods: The authors developed and validated a 3D pharmacophore model of IKKβ inhibitors via the HypoGen algorithm in Discovery Studio 2019, then performed virtual screening, molecular docking and kinase assays to identify hit compounds from the ChemDiv database. The compound with the highest inhibitory activity was further evaluated in adjuvant-induced arthritis rat models. Results: Among the four hit compounds, Hit 4 had the highest IKKβ inhibitory activity (IC50 = 30.4 ± 3.8), and it could significantly ameliorate joint inflammation and damage in vivo. Conclusion: The identified compound, Hit 4, can be optimized as a therapeutic agent for inflammatory diseases.

Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics

Pseudomonas aeruginosa is a clinically challenging pathogen due to its high resistance to antibiotics. Quorum sensing inhibitors (QSIs) have been proposed as a promising strategy to overcome this resistance by interfering with the bacterial communication system. Among the potential targets of QSIs, PqsR is a key regulator of quorum sensing in Pseudomonas aeruginosa. However, the current research on PqsR inhibitors is limited by the lack of diversity in the chemical structures and the screening methods. Therefore, this study aims to develop a multidimensional screening model for PqsR inhibitors based on both ligand- and receptor-based approaches. First, a pharmacophore model was constructed from a training set of PqsR inhibitors to identify the essential features and spatial arrangement for the activity. Then, molecular docking and dynamics simulations were performed to explore the core interactions between PqsR inhibitors and their receptor. The results indicate that an effective PqsR inhibitor should possess two aromatic rings, one hydrogen bond acceptor, and two hydrophobic groups and should form strong interactions with the following four amino acid residues: TYR_258, ILE_236, LEU_208, and GLN_194. Moreover, the docking score and the binding free energy should be lower than -8 kcal/mol and -40 kcal/mol, respectively. Finally, the validity of the multidimensional screening model was confirmed by a test set of PqsR inhibitors, which showed a higher accuracy than the existing screening methods based on single characteristics. This multidimensional screening model would be a useful tool for the discovery and optimization of PqsR inhibitors in the future.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

Corrigendum: Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

[This corrects the article DOI: 10.3389/fphar.2023.1265573.].

Identifying novel therapeutic inhibitors to target FMS-like tyrosine kinase-3 (FLT3) against acute myeloid leukemia: a molecular docking, molecular dynamics, and DFT study

Around 30% of acute myeloid leukemia (AML) patients have triggering mutations in Feline McDonough Sarcoma (FMS)-like tyrosine kinase 3 (FLT3), which has been suggested as a possible therapeutic candidate for AML therapy. Many tyrosine kinase inhibitors are available and have a wide variety of applications in the treatment of cancer by inhibiting subsequent steps of cell proliferation. Therefore, our study aims to identify effective antileukemic agents against FLT3 gene. Initially, well-known antileukemic drug candidates have been chosen to generate a structure-based pharmacophore model to assist the virtual screening of 217,77,093 compounds from the Zinc database. The final hits compounds were retrieved and evaluated by docking against the target protein, where the top four compounds have been selected for the analysis of ADMET. Based on the density functional theory (DFT), the geometry optimization, frontier molecular orbital (FMO), HOMO-LUMO, and global reactivity descriptor values have been evaluated that confirming a satisfactory profile and reactivity order for the selected candidates. In comparison to control compounds, the docking results revealed that the four compounds had substantial binding energies (-11.1 to -11.5 kcal/mol) with FLT3. The physicochemical and ADMET (adsorption, distribution, metabolism, excretion, toxicity) prediction results corresponded to the bioactive and safe candidates. Molecular dynamics (MD) confirmed the better binding affinity and stability compared to gilteritinib as a potential FLT3 inhibitor. In this study, a computational approach has been performed that found a better docking and dynamics score against target proteins, indicating potent and safe antileukemic agents, furthermore in-vivo and in-vitro investigations are recommended.Communicated by Ramaswamy H. Sarma.

Pharmacophore-Based Screening, Molecular Docking, and Dynamic Simulation of Fungal Metabolites as Inhibitors of Multi-Targets in Neurodegenerative Disorders

Neurodegenerative disorders, such as Alzheimer's disease (AD), negatively affect the economic and psychological system. For AD, there is still a lack of disease-altering treatments and promising cures due to its complex pathophysiology. In this study, we computationally screened the natural database of fungal metabolites against three known therapeutic target proteins of AD. Initially, a pharmacophore-based, drug-likeness category was employed for screening, and it filtered the 14 (A-N) best hits out of 17,544 fungal metabolites. The 14 best hits were docked individually against GSK-3β, the NMDA receptor, and BACE-1 to investigate the potential of finding a multitarget inhibitor. We found that compounds B, F, and L were immuno-toxic, whereas E, H, I, and J had a higher LD50 dose (5000 mg/kg). Among the examined metabolites, the Bisacremine-C (compound I) was found to be the most active molecule against GSK-3β (ΔG: -8.7 ± 0.2 Kcal/mol, Ki: 2.4 × 106 M-1), NMDA (ΔG: -9.5 ± 0.1 Kcal/mol, Ki: 9.2 × 106 M-1), and BACE-1 (ΔG: -9.1 ± 0.2 Kcal/mol, Ki: 4.7 × 106 M-1). It showed a 25-fold higher affinity with GSK-3β, 6.3-fold higher affinity with NMDA, and 9.04-fold higher affinity with BACE-1 than their native ligands, respectively. Molecular dynamic simulation parameters, such as RMSD, RMSF, Rg, and SASA, all confirmed that the overall structures of the targeted enzymes did not change significantly after binding with Bisacremine-C, and the ligand remained inside the binding cavity in a stable conformation for most of the simulation time. The most significant hydrophobic contacts for the GSK-3β-Bisacremine-C complex are with ILE62, VAL70, ALA83, and LEU188, whereas GLN185 is significant for H-bonds. In terms of hydrophobic contacts, TYR184 and PHE246 are the most important, while SER180 is vital for H-bonds in NMDA-Bisacremine-C. THR232 is the most crucial for H-bonds in BACE-1-Bisacremine-C and ILE110-produced hydrophobic contacts. This study laid a foundation for further experimental validation and clinical trials regarding the biopotency of Bisacremine-C.

In-silico design, pharmacophore-based screening, and molecular docking studies reveal that benzimidazole-1,2,3-triazole hybrids as novel EGFR inhibitors targeting lung cancer

Lung cancer is a complex and heterogeneous disease, which has been associated with various molecular alterations, including the overexpression and mutations of the epidermal growth factor receptor (EGFR). In this study, designed a library of 1843 benzimidazole-1,2,3-triazole hybrids and carried out pharmacophore-based screening to identify potential EGFR inhibitors. The 164 compounds were further evaluated using molecular docking and molecular dynamics simulations to understand the binding interactions between the compounds and the receptor. In-si-lico ADME and toxicity studies were also conducted to assess the drug-likeness and safety of the identified compounds. The results of this study indicate that benzimidazole-1,2,3-triazole hybrids BENZI-0660, BENZI-0125, BENZI-0279, BENZI-0415, BENZI-0437, and BENZI-1110 exhibit dock scores of -9.7, -9.6, -9.6, -9.6, -9.6, -9.6 while referencing molecule -7.9 kcal/mol for EGFR (PDB ID: 4HJO), respectively. The molecular docking and molecular dynamics simulations revealed that the identified compounds formed stable interactions with the active site of EGFR, indicating their potential as inhibitors. The in-silico ADME and toxicity studies showed that the compounds had favorable drug-likeness properties and low toxicity, further supporting their potential as therapeutic agents. Finally, performed DFT studies on the best-selected ligands to gain further insights into their electronic properties. The findings of this study provide important insights into the potential of benzimidazole-1,2,3-triazole hybrids as promising EGFR inhibitors for the treatment of lung cancer. This research opens up a new avenue for the discovery and development of potent and selective EGFR inhibitors for the treatment of lung cancer.Communicated by Ramaswamy H. Sarma.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

mPGES-1 Inhibitor Discovery Based on Computer-Aided Screening: Pharmacophore Models, Molecular Docking, ADMET, and MD Simulations

mPGES-1 is an enzyme, which, when activated by inflammatory factors, can cause prostaglandin E synthesis. Traditional non-steroidal anti-inflammatory drugs are capable of inhibiting prostaglandin production, yet they can also cause gastrointestinal reactions and coagulation disorders. mPGES-1, the enzyme at the conclusion of prostaglandin production, does not cause any adverse reactions when inhibited. Numerous studies have demonstrated that mPGES-1 is more abundant in cancerous cells than in healthy cells, indicating that decreasing the expression of mPGES-1 could be a potential therapeutic strategy for cancer. Consequently, the invention of mPGES-1 inhibitors presents a fresh avenue for the treatment of inflammation and cancer. Incorporating a database of TCM compounds, we collected a batch of compounds that had an inhibitory effect on mPGES-1 and possessed IC50 value. Firstly, a pharmacophore model was constructed, and the TCM database was screened, and the compounds with score cut-off values of more than 1 were retained. Then, the compounds retained after being screened via the pharmacodynamic model were screened for docking at the mPGES-1 binding site, followed by high-throughput virtual screening [HTVS] and standard precision [SP] and super-precision [XP] docking, and the compounds in the top 20% of the XP docking score were selected to calculate the total free binding energy of MM-GBSA. The best ten compounds were chosen by comparing their score against the reference ligand 4U9 and the MM-GBSA_dG_Bind score. ADMET analysis resulted in the selection of ten compounds, three of which had desirable medicinal properties. Finally, the binding energy of the target protein mPGES-1 and the candidate ligand compound was analyzed using a 100 ns molecular dynamics simulation of the reference ligand 4U9 and three selected compounds. After a gradual screening study and analysis, we identified a structure that is superior to the reference ligand 4U9 in all aspects, namely compound 15643. Taken together, the results of this study reveal a structure that can be used to inhibit mPGES-1 compound 15643, thereby providing a new option for anti-inflammatory and anti-tumor drugs.

High-throughput virtual screening of phenylpyrimidine derivatives as selective JAK3 antagonists using computational methods

In this study, we used phenylpyrimidine derivatives with known biological activity against JAK3, a critical tyrosine kinase enzyme involved in signaling pathways, to find similar compounds as potential treatments for rheumatoid arthritis. These inhibitors inhibited JAK3 activity by forming a covalent bond with the Cys909 residue, which resulted in a strong inhibitory effect. Phenylpyrimidine is considered a promising therapeutic target. For pharmacophore modeling, 39 phenylpyrimidine derivatives with high pIC50 (Exp) values were chosen. The best pharmacophore model produced 28 molecules, and the five-point common pharmacophore hypothesis from P HASE (DHRRR_1) revealed the requirement for a hydrogen bond donor feature, a hydrophobic group feature, and three aromatic ring features for further design. The validation of the pharmacophore model phase was performed through 3D-QSAR using partial least squares (P LS). The 3D-QSAR study produced two successful models, an atom-based model (R2 = 0.95; Q2 = 0.67) and a field-based model (R2 = 0.93; Q2 = 0.76), which were used to predict the biological activity of new compounds. The pharmacophore model successfully distinguished between active and inactive medications, discovered potential JAK3 inhibitors, and demonstrated validity with a ROC of 0. 77. ADME-Tox was used to eliminate compounds that might have adverse effects. The best pharmacokinetics and affinity derivatives were selected for covalent docking. A molecular dynamics simulation of the selected molecules and the protein complex was performed to confirm the stability of the interaction with JAK3, whereas MM/GBSA simulations further confirmed their binding affinity. By using the principle of retrosynthesis, we were able to map out a pathway for synthesizing these potential drug candidates. This study has the potential to offer valuable and practical insights for optimizing novel derivatives of phenylpyrimidine.Communicated by Ramaswamy H. Sarma.

Virtual Screening Strategy and In Vitro Tests to Identify New Inhibitors of the Immunoproteasome

Immunoproteasome inhibition is a promising strategy for the treatment of hematological malignancies, autoimmune diseases, and inflammatory diseases. The design of non-covalent inhibitors of the immunoproteasome β1i/β5i catalytic subunits could be a novel approach to avoid the drawbacks of the known covalent inhibitors, such as toxicity due to off-target binding. In this work, we report the biological evaluation of thirty-four compounds selected from a commercially available collection. These hit compounds are the outcomes of a virtual screening strategy including a dynamic pharmacophore modeling approach onto the β1i subunit and a pharmacophore/docking approach onto the β5i subunit. The computational studies were first followed by in vitro enzymatic assays at 100 μM. Only compounds capable of inhibiting the enzymatic activity by more than 50% were characterized in detail using Tian continuous assays, determining the dissociation constant (K_i) of the non-covalent complex where K_i is also the measure of the binding affinity. Seven out of thirty-four hits showed to inhibit β1i and/or β5i subunit. Compound 3 is the most active on the β1i subunit with K_i = 11.84 ± 1.63 µM, and compound 17 showed K_i = 12.50 ± 0.77 µM on the β5i subunit. Compound 2 showed inhibitory activity on both subunits (K_i = 12.53 ± 0.18 and K_i = 31.95 ± 0.81 on the β1i subunit and β5i subunit, respectively). The induced fit docking analysis revealed interactions with Thr1 and Phe31 of β1i subunit and that represent new key residues as reported in our previous work. Onto β5i subunit, it interacts with the key residues Thr1, Thr21, and Tyr169. This last hit compound identified represents an interesting starting point for further optimization of β1i/β5i dual inhibitors of the immunoproteasome.

Identification of Anti-Influenza A Compounds Inhibiting the Viral Non-Structural Protein 1 (NS1) Using a Type I Interferon-Driven Screening Strategy

There is an urgent need to identify efficient antiviral compounds to combat existing and emerging RNA virus infections, particularly those related to seasonal and pandemic influenza outbreaks. While inhibitors of the influenza viral integral membrane proton channel protein (M2), neuraminidase (NA), and cap-dependent endonuclease are available, circulating influenza viruses acquire resistance over time. Thus, the need for the development of additional anti-influenza drugs with novel mechanisms of action exists. In the present study, a cell-based screening assay and a small molecule library were used to screen for activities that antagonized influenza A non-structural protein 1 (NS1), a highly conserved, multifunctional accessory protein that inhibits the type I interferon response against influenza. Two potential anti-influenza agents, compounds 157 and 164, were identified with anti-NS1 activity, resulting in the reduction of A/PR/8/34(H1N1) influenza A virus replication and the restoration of IFN-β expression in human lung epithelial A549 cells. A 3D pharmacophore modeling study of the active compounds provided a glimpse of the structural motifs that may contribute to anti-influenza virus activity. This screening approach is amenable to a broader analysis of small molecule compounds to inhibit other viral targets.

Lactiplantibacillus plantarum and Saussurea costus as Therapeutic Agents against a Diabetic Rat Model-Approaches to Investigate Pharmacophore Modeling of Human IkB Kinase and Molecular Interaction with Dehydrocostus Lactone of Saussurea costus

Lactic acid bacteria is well-known as a vital strategy to alleviate or prevent diabetes. Similarly, the plant Saussurea costus (Falc) Lipsch is a preventive power against diabetes. Here, we aimed to determine whether lactic acid bacteria or Saussurea costus is more effective in treating a diabetic rat model in a comparative study manner. An in vivo experiment was conducted to test the therapeutic activity of Lactiplantibacillus plantarum (MW719476.1) and S. costus plants against an alloxan-induced diabetic rat model. Molecular, biochemical, and histological analyses were investigated to evaluate the therapeutic characteristics of different treatments. The high dose of S. costus revealed the best downregulated expression for the IKBKB, IKBKG, NfkB1, IL-17A, IL-6, IL-17F, IL-1β, TNF-α, TRAF6, and MAPK genes compared to Lactiplantibacillus plantarum and the control groups. The downregulation of IKBKB by S. costus could be attributed to dehydrocostus lactone as an active compound with proposed antidiabetic activity. So, we performed another pharmacophore modeling analysis to test the possible interaction between human IkB kinase beta protein and dehydrocostus lactone as an antidiabetic drug. Molecular docking and MD simulation data confirmed the interaction between human IkB kinase beta protein and dehydrocostus lactone as a possible drug. The target genes are important in regulating type 2 diabetes mellitus signaling, lipid and atherosclerosis signaling, NF-κB signaling, and IL-17 signaling pathways. In conclusion, the S. costus plant could be a promising source of novel therapeutic agents for treating diabetes and its complications. Dehydrocostus lactone caused the ameliorative effect of S. costus by its interaction with human IkB kinase beta protein. Further, future studies could be conducted to find the clinical efficacy of dehydrocostus lactone.

In Silico Drug Design of Anti-Breast Cancer Agents

Cancer is a condition marked by abnormal cell proliferation that has the potential to invade or indicate other health issues. Human beings are affected by more than 100 different types of cancer. Some cancer promotes rapid cell proliferation, whereas others cause cells to divide and develop more slowly. Some cancers, such as leukemia, produce visible tumors, while others, such as breast cancer, do not. In this work, in silico investigations were carried out to investigate the binding mechanisms of four major analogs, which are marine sesquiterpene, sesquiterpene lactone, heteroaromatic chalcones, and benzothiophene against the target estrogen receptor-α for targeting breast cancer using Schrödinger suite 2021-4. The Glide module handled the molecular docking experiments, the QikProp module handled the ADMET screening, and the Prime MM-GB/SA module determined the binding energy of the ligands. The benzothiophene analog BT_ER_15f (G-score -15.922 Kcal/mol) showed the best binding activity against the target protein estrogen receptor-α when compared with the standard drug tamoxifen which has a docking score of -13.560 Kcal/mol. TRP383 (tryptophan) has the highest interaction time with the ligand, and hence it could act for a long time. Based on in silico investigations, the benzothiophene analog BT_ER_15f significantly binds with the active site of the target protein estrogen receptor-α. Similar to the outcomes of molecular docking, the target and ligand complex interaction motif established a high affinity of lead candidates in a dynamic system. This study shows that estrogen receptor-α targets inhibitors with better potential and low toxicity when compared to the existing market drugs, which can be made from a benzothiophene derivative. It may result in considerable activity and be applied to more research on breast cancer.

Targeting Olokizumab-Interleukin 6 interaction interface to discover novel IL-6 inhibitors

The IL-6/IL-6R or IL-6/GP130 protein-protein interactions play a significant role in controlling the development of chronic inflammatory diseases, such as rheumatoid arthritis, Castleman disease, psoriasis, and, most recently, COVID-19. Modulating or antagonizing protein-protein interactions of IL6 binding to its receptors by oral drugs promises similar efficacy to biological therapy in patients, namely monoclonal antibodies. In this study, we used a crystal structure of the Fab part of olokizumab in a complex with IL-6 (PDB ID: 4CNI) to uncover starting points for small molecule IL-6 antagonist discovery. Firstly, a structure‑based pharmacophore model of the protein active site cavity was generated to identify possible candidates, followed by virtual screening with a significant database Drugbank. After the docking protocol validation, a virtual screening by molecular docking was carried out and a total of 11 top hits were reported. Detailed analysis of the best scoring molecules was performed with ADME/T analysis and molecular dynamics simulation. Furthermore, the Molecular Mechanics-Generalized Born Surface Area (MM/GBSA) technique has been utilized to evaluate the free binding energy. Based on the finding, one newly obtained compound in this study, namely DB15187, may serve as a lead compound for the discovery of IL-6 inhibitors.Communicated by Ramaswamy H. Sarma.

Structure-based small inhibitors search combined with molecular dynamics driven energies for human programmed cell death-1 (PD-1) protein

Human immune system is specialized in distinguishing normal cells from foreign particles mainly through proteins expressed on immune cells called 'checkpoints'. Immune checkpoints work as a switch to activate and deactivate immune responses. T cells express one of the immune checkpoint, human programmed cell death-1 (PD-1), which normally operates as an off-switch function to protect the normal cell from T-cell attack. Binding of PD-1 to its ligand, the programmed cell death ligand (PD-L1/2) expressed on myeloid/cancer cells, induce downstream inhibitory signals, leading to tumor immune evasion. Targeting PD-1 or PD-L1 can boost the immune response against cancer cells. To design novel small molecule inhibitors for the PD-1, in silico structure-based screening on pharmacophoric points and molecular docking were performed. Based on the docking score and significant binding interaction with the crucial residues of PD-1 (Thr59, Glu61, Ser62, Glu84, Arg86 and Ala132), compounds were selected from the ZINC20 database, and their dynamic behavior and conformational stability were examined through molecular dynamic simulations. Besides, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method was used to calculate the binding strength of each selected inhibitor complexed with PD-1. The binding energy calculations revealed that these selected inhibitors show a considerable affinity for PD-1. The selected novel inhibitors exhibit excellent drug-like and pharmacokinetic properties (absorption, distribution, metabolism, excretion and toxicity). In conclusion, the identified novel compounds (ZINC1443480030, ZINC1002854123, ZINC988238128, ZINC1481242350, ZINC1001739421, ZINC1220816434 and ZINC1167786692) from the current study can be validated in-vitro as potential PD-1 inhibitors and for discovery of novel drugs against PD-1 in the future.Communicated by Ramaswamy H. Sarma.

A new set of KNIME nodes implementing the QPhAR algorithm

Dissemination of novel research methods, especially in the form of chemoinformatics software, depends heavily on their ease of applicability for non-expert users with only a little or no programming skills and knowledge in computer science. Visual programming has become widely popular over the last few years, also enabling researchers without in-depth programming skills to develop tailored data processing pipelines using elements from a repository of predefined standard procedures. In this work, we present the development of a set of nodes for the KNIME platform implementing the QPhAR algorithm. We show how the developed KNIME nodes can be included in a typical workflow for biological activity prediction. Furthermore, we present best-practice guidelines that should be followed to obtain high-quality QPhAR models. Finally, we show a typical workflow to train and optimise a QPhAR model in KNIME for a set of given input compounds, applying the discussed best practices.

Identification of novel human nicotinamide N-methyltransferase inhibitors: a structure-based pharmacophore modeling and molecular dynamics approach

Human nicotinamide N-methyltransferase (hNNMT) is a cytosolic enzyme associated in the phase-II metabolism, belonging to the S-adenosyl-L-methionine (SAM)-dependent methyltransferases family. Overexpression of hNNMT was observed in diseases such as metabolic disorders and different types of cancers, which suggest NNMT as a prospective therapeutic target. In this study we propose a structure-based pharmacophore model to understand the structural features responsible for the pharmacological activity. The generated model was validated using the ROC curve (AUC), goodness of hit score (GH), specificity, sensitivity and enrichment factor (EF). The pharmacophore was employed to retrieve active molecules from the ZINC database, followed by virtual-screening and molecular docking. Six molecules with the best pharmfit score, binding energy and ADMET properties were identified in this study. A 150 ns molecular dynamics simulation was performed on the selected molecules complexed with hNNMT protein to validate the results. The molecules ZINC35464499, ZINC13311192, ZINC31159282, ZINC14650833, ZINC14819515 and ZINC00303881 were identified, which could be act as the potential hNNMT inhibitors and can also be used as direct hits for developing novel hNNMT antagonists.Communicated by Ramaswamy H. Sarma.

Computational investigation of potent inhibitors against YsxC: structure-based pharmacophore modeling, molecular docking, molecular dynamics, and binding free energy

In S. aureus, ribosome biogenesis GTP-binding (YsxC), a GTPase interacts with 50S subunit and 30S subunit of ribosome, and β' subunit of RNA polymerase and played an important role in protein synthesis. For the identification of potent lead molecules, we have conducted pharmacophore modeling by consideration of pharmacophore features of GTP among YsxC-GTP complex. Virtual screening and molecular docking results displayed that five pharmacokinetic and ADMET filtered molecules-ZINC000006424138, ZINC000095502032, ZINC000225415132, ZINC000095475800, and ZINC000012990761-had higher binding affinities than GTP with YsxC. All the identified molecules shared similar pharmacophore features of GTP and were stabilized via hydrogen bonds and hydrophobic interactions with YsxC. Molecular dynamics analysis revealed that YsxC-inhibitor(s) complexes were lesser dynamics and higher stable than YsxC-GTP complex. Molecular Mechanics/Poisson-Boltzmann Surface Area (MMPBSA) results confirmed that identified molecules bound at the active site (Arg33, Ser34, Asn35, Val36, Lys38, Ser39, Thr40, Thr54, Ser55, Pro58, Lys60, Thr61, Thr144, Lys145, Ser178, and Ile179) of YsxC and formed the lower energy (-190.32 ± 3.46 to -217.03 ± 2.55 kJ/mol) complexes than YsxC-GTP (-157.16 ± 2.89 kJ/mol) complex. The identified molecules in this study can be further tested and utilized to design novel antimicrobial agents for S. aureus.Communicated by Ramaswamy H. Sarma.

Comparative molecular docking analysis for analyzing the inhibitory effect of Anakinra and Ustekinumab against IL17F

Interleukin 17 F is a member of IL-17 cytokine family with a 50% structural homology to IL-17A and plays a significant role either alone or in combination with IL-17A towards inflammation in Rheumatoid arthritis (RA). A growing number of drugs targeting IL-17 pathway are being tested against population specific disease markers. The major objective of this research was to investigate the anti-inflammatory effect of Anakinra (an IL-1 R1 inhibitor) and Ustekinumab (an IL-12 and IL-23 inhibitor) by targeting IL17F. The three dimensional structures of IL17F was taken from PDB while structures of drugs were taken from PubChem database. Docking was performed using MOE and Schrodinger ligand docking software and binding energies, including s-score using London-dG fitness function and glide score using glide internal energy function, between drug and targets were compared. Furthermore, Protein-Drug complex were subjected to 150 ns Molecular Dynamics (MD) Simulations using Schrodinger's Desmond Module. Docking and MD simulation results suggest anakinra as a more potent IL17F inhibitor and forming a more structurally stable complex.Communicated by Ramaswamy H. Sarma.

Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches

BACKGROUND

Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were designed and discovered in the previous decade, but none have reached the clinic. Potent and selective ACK1 inhibitors are urgently needed.

METHODS

In the present investigation, the pharmacophore model (PM) was rationally built utilizing two distinct inhibitors coupled with ACK1 crystal structures. The generated PM was utilized to screen the drug-like database generated from the four chemical databases. The binding mode of pharmacophore-mapped compounds was predicted using a molecular docking (MD) study. The selected hit-protein complexes from MD were studied under all-atom molecular dynamics simulations (MDS) for 500 ns. The obtained trajectories were ranked using binding free energy calculations (ΔG kJ/mol) and Gibb's free energy landscape.

RESULTS

Our results indicate that the three hit compounds displayed higher binding affinity toward ACK1 when compared with the known multi-kinase inhibitor dasatinib. The inter-molecular interactions of Hit1 and Hit3 reveal that compounds form desirable hydrogen bond interactions with gatekeeper T205, hinge region A208, and DFG motif D270. As a result, we anticipate that the proposed scaffolds might help in the design of promising selective ACK1 inhibitors.

Structure-based pharmacophore modeling, virtual screening, and molecular dynamics simulation studies for identification of Plasmodium falciparum 5-aminolevulinate synthase inhibitors

Plasmodium falciparum (Pf) 5-aminolevulinic acid synthase (5-ALAS) is an essential enzyme with high selectivity during liver stage development, signifying its potential as a prophylactic antimalarial drug target. The aim of this study was to identify important potential lead compounds which can serve as inhibitors of Pf 5-ALAS using pharmacophore modeling, virtual screening, qualitative structural assessment, in silico ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) evaluation and molecular dynamics simulation. The best model of the tertiary structure of Pf 5-ALAS was obtained using MolProbity, while the following databases were explored for the pharmacophore-based virtual screening: CHEMBL, ChemDiv, ChemSpace, MCULE, MCULE-ULTIMATE, MolPort, NCI Open Chemical Repository, LabNetwork and ZINC databases. 2,621 compounds were screened against the modeled Pf 5-ALAS using AutoDock vina. The post-screening analysis was carried out using Discovery Studio while molecular dynamics simulation was performed on the best hits using NAMD-VMD and Galaxy Europe platform. Compound CSMS00081585868 was observed as the best hit with a binding affinity of -9.9 kcal/mol and predicted Ki of 52.10 nM, engaging in seven hydrogen bonds with the target's active site amino acid residues. The in silico ADMET prediction showed that all ten best hits possessed relatively good pharmacokinetic properties. The qualitative structural assessment of the best hit, CSMS00081585868, revealed that the presence of two pyridine scaffolds bearing hydroxy and fluorine groups linked by a pyrrolidine scaffold contributed significantly to its ability to have a strong binding affinity with the receptor. The best hit also showed stability in the active site of Pf 5-ALAS as confirmed from the RMSD obtained during the MD simulation.

NRF2 in dermo-cosmetic: From scientific knowledge to skin care products

The skin is the organ that is most susceptible to the impact of the exposome. Located at the interface with the external environment, it protects internal organs through the barrier function of the epidermis. It must adapt to the consequences of the harmful effects of solar radiation, the various chemical constituents of atmospheric pollution, and wounds associated with mechanical damage: oxidation, cytotoxicity, inflammation, and so forth. In this biological context, a capacity to adapt to the various stresses caused by the exposome is essential; otherwise, more or less serious conditions may develop accelerated aging, pigmentation disorders, atopy, psoriasis, and skin cancers. Nrf2-controlled pathways play a key role at this level. Nrf2 is a transcription factor that controls genes involved in oxidative stress protection and detoxification of chemicals. Its involvement in UV protection, reduction of inflammation in processes associated with healing, epidermal differentiation for barrier function, and hair regrowth, has been demonstrated. The modulation of Nrf2 in the skin may therefore constitute a skin protection or care strategy for certain dermatological stresses and disorders initiated or aggravated by the exposome. Nrf2 inducers can act through different modes of action. Keap1-dependent mechanisms include modification of the cysteine residues of Keap1 by (pro)electrophiles or prooxidants, and disruption of the Keap1-Nrf2 complex. Indirect mechanisms are suggested for numerous phytochemicals, acting on upstream pathways, or via hormesis. While developing novel and safe Nrf2 modulators for skin care may be challenging, new avenues can arise from natural compounds-based molecular modeling and emerging concepts such as epigenetic regulation.

Amomum subulatum: A treasure trove of anti-cancer compounds targeting TP53 protein using in vitro and in silico techniques

Cancer is a primary global health concern, and researchers seek innovative approaches to combat the disease. Clinical bioinformatics and high-throughput proteomics technologies provide powerful tools to explore cancer biology. Medicinal plants are considered effective therapeutic agents, and computer-aided drug design (CAAD) is used to identify novel drug candidates from plant extracts. The tumour suppressor protein TP53 is an attractive target for drug development, given its crucial role in cancer pathogenesis. This study used a dried extract of Amomum subulatum seeds to identify phytocompounds targeting TP53 in cancer. We apply qualitative tests to determine its phytochemicals (Alkaloid, Tannin, Saponin, Phlobatinin, and Cardic glycoside), and found that alkaloid composed of 9.4% ± 0.04% and Saponin 1.9% ± 0.05% crude chemical constituent. In the results of DPPH Analysis Amomum subulatum Seeds founded antioxidant activity, and then we verified via observing methanol extract (79.82%), BHT (81.73%), and n-hexane extract (51.31%) found to be positive. For Inhibition of oxidation, we observe BHT is 90.25%, and Methanol (83.42%) has the most significant proportion of linoleic acid oxidation suppression. We used diverse bioinformatics approaches to evaluate the effect of A. subulatum seeds and their natural components on TP53. Compound-1 had the best pharmacophore match value (53.92), with others ranging from 50.75 to 53.92. Our docking result shows the top three natural compounds had the highest binding energies (-11.10 to -10.3 kcal/mol). The highest binding energies (-10.9 to -9.2 kcal/mol) compound bonded to significant sections in the target protein's active domains with TP53. Based on virtual screening, we select top phytocompounds for targets which highly fit based on pharmacophore score and observe these compounds exhibited potent antioxidant activity and inhibited cancer cell inflammation in the TP53 pathway. Molecular Dynamics (MD) simulations indicated that the ligand was bound to the protein with some significant conformational changes in the protein structure. This study provides novel insights into the development of innovative drugs for the treatment of cancer disorders.

Ligand-based pharmacophore modeling and QSAR approach to identify potential dengue protease inhibitors

The viral disease dengue is transmitted by the Aedes mosquito and is commonly seen to occur in the tropical and subtropical regions of the world. It is a growing public health concern. To date, other than supportive treatments, there are no specific antiviral treatments to combat the infection. Therefore, finding potential compounds that have antiviral activity against the dengue virus is essential. The NS2B-NS3 dengue protease plays a vital role in the replication and viral assembly. If the functioning of this protease were to be obstructed then viral replication would be halted. As a result, this NS2B-NS3 proves to be a promising target in the process of anti-viral drug design. Through this study, we aim to provide suggestions for compounds that may serve as potent inhibitors of the dengue NS2B-NS3 protein. Here, a ligand-based pharmacophore model was generated and the ZINC database was screened through ZINCPharmer to identify molecules with similar features. 2D QSAR model was developed and validated using reported 4-Benzyloxy Phenyl Glycine derivatives and was utilized to predict the IC50 values of unknown compounds. Further, the study is extended to molecular docking to investigate interactions at the active pocket of the target protein. ZINC36596404 and ZINC22973642 showed a predicted pIC50 of 6.477 and 7.872, respectively. They also showed excellent binding with NS3 protease as is evident from their binding energy of -8.3and -8.1 kcal/mol, respectively. ADMET predictionsofcompounds have shown high drug-likeness. Finally, the molecular dynamic simulations integrated with MM-PBSA binding energy calculations confirmedboth identified ZINC compounds as potential hit moleculeswith good stability.

Potential inhibitors for FKBP51: an in silico study using virtual screening, molecular docking and molecular dynamics simulation

Over the years, FK506-binding proteins have been targeted for different pharmaceutical interests. The FK506-binding protein, encoded by the FKBP5 gene, is responsible for stress and metabolic-related disorders, including cancer. In addition, the FKBD-I domain of the protein is a potential target for endocrine-related physiological diseases. In the present study, a set of natural compounds from the ZINC database was screened against FKBP51 protein using in silico strategy, namely pharmacophore modeling, molecular docking, and molecular dynamic simulation. A protein-ligand-based pharmacophore model workflow was employed to identify small molecules. The resultant compounds were then assessed for their toxicity using ADMET prediction. Based on ADMET prediction, 4768 compounds were selected for molecular docking to elucidate their binding mode. Based on the binding energy, 857 compounds were selected, and their Similarity Tanimoto coefficient was calculated, followed by clustering according to Jarvis-Patrick clustering methods (Jarp). The clustered singletons resulted in 14 hit compounds. The top 05 hit compounds and 05 known compounds were then subjected to 100 ns MD simulation to check the stability of complexes. The study revealed that the selected complexes are stable throughout the 100 ns simulation; for FKBD-I (4TW6), crystal structure compared with FKBP-51 (1KT0) crystal structure. Finally, the binding free energies of the hit complexes were calculated using molecular mechanics energies combined with Poisson-Boltzmann. The data reveal that all the complexes show negative BFEs, indicating a good affinity of the hit compounds to the protein. The top five compounds are, therefore, potential inhibitors for FKBP51. Communicated by Ramaswamy H. Sarma.

Structure-based pharmacophore modeling, virtual screening and simulation studies for the identification of potent anticancerous phytochemical lead targeting cyclin-dependent kinase 2

Cyclin-dependent kinases are of critical importance in directing various cell cycle phases making them as potential tumor targets. Cyclin-dependent kinase 2 (CDK2) in particular plays a significant part during cell cycle events and its imbalance roots out tumorogenic environment. Herein, we built a structure-based pharmacophore model complementing the ATP pocket site of CDK2 with four pharmacophoric features, using a series of structures obtained from cluster analysis during MD simulation assessment. This was followed by its validation and further database screening against Taiwan indigenous plants database (5284 compounds). The screened compounds were subjected toward Lipinski's rule (RO5) and ADMET filter followed by docking analysis and simulation study. In filtering hits (10 compounds) via molecular docking against CDK2, Schinilenol with -8.1 kcal/mol fetched out as a best lead phytoinhibitor in the presence of standard drug (Dinaciclib). Additionally, pharmacophore mapping analysis also indicated relative fit values of dinaciclib and schinilenol as 2.37 and 2.31, respectively. Optimization, flexibility prediction and the stability of CDK2 in complex with the ligands were also ascertained by means of molecular dynamics for 50 ns, which further proposed schinilenol having better binding stability than dinaciclib with RMSD values ranging from 0.31 to 0.34 nm. Reactivity site, biological activity detection and cardiotoxicity assessment also proposed schinilenol as a better phytolead inhibitor than the existing dinaciclib. Abbreviations: CDK2: Cyclin dependent kinase2; ATP: Adenosine triphosphate; MD: Molecular dynamics, RO5: Rule of five; ADMET: Absorption, distribution, metabolism, and excretion; RMSD: Root mean square deviation; DS: Discovery Studio; SOM: Site of metabolism; RBPM: receptor based pharmacophore model; TIP: Schinilenol; hERG: human Ether-à-go-go - Related GeneCommunicated by Ramaswamy H. Sarma.

Identification of potential inhibitors for LLM of Staphylococcus aureus: structure-based pharmacophore modeling, molecular dynamics, and binding free energy studies

Staphylococcus aureus causes various life-threatening diseases in humans and developed resistance to several antibiotics. Lipophilic membrane (LLM) protein regulates bacterial lysis rate and methicillin resistance level in S. aureus. To identify potential lead molecules, we performed a structure-based pharmacophore modeling by consideration of pharmacophore properties from LLM-tunicamycin complex. Further, virtual screening of ZINC database against the LLM was conducted and compounds were assessed for Lipinski and ADMET properties. Based on pharmacokinetic, and molecular docking, five potential inhibitors (ZINC000072380005, ZINC000257219974, ZINC000176045471, ZINC000035296288, and ZINC000008789934) were identified. Molecular dynamics simulation (MDS) of these five molecules was performed to evaluate the dynamics and stability of protein after binding of the ligands. Several MDS analysis like RMSD, RMSF, Rg, SASA, and PCA confirm that identified compounds exhibit higher binding affinity as compared to tunicamycin for LLM. The binding free energy analysis reveals that five compounds exhibit higher binding energy in the range of -218.76 to -159.52 kJ/mol, which is higher as compared to tunicamycin (-116.13 kJ/mol). Individual residue decomposition analysis concludes that Asn148, Asp151, Asp208, His271, and His272 of LLM play a significant role in the formation of lower energy LLM-inhibitor(s) complexes. These predicted molecules displayed pharmacological and structural properties and may be further used to develop novel antimicrobial compounds against S. aureus.Communicated by Ramaswamy H. Sarma.

Azoles containing naphthalene with activity against Gram-positive bacteria: in vitro studies and in silico predictions for flavohemoglobin inhibition

Azoles are first-line drugs used in fungal infections. Topical antifungals, such as miconazole and econazole, are known to be active against Gram-positive bacteria, which was reported to result from bacterial flavohemoglobin (flavoHb) inhibition. Dual antibacterial/antifungal action is believed to have benefits for antimicrobial chemotherapy. In this study, we tested antibacterial effects of an in-house library of naphthalene-bearing azoles, some of which were reported as potent antifungals, in an attempt to find dual-acting hits. Several potent derivatives were obtained against the Gram-positive bacteria, Enterococcus faecalis and Staphylococcus aureus. 9 was active at a minimum inhibitor concentration (MIC) less than 1 µg/ml against E. faecalis and S. aureus, and 10 against S. aureus. 16 was also potent against E. faecalis and S. aureus (MIC = 1 and 2 µg/ml, respectively). Six more were active against S. aureus with MIC ≤ 4 µg/ml. In vitro cytotoxicity studies showed that the active compounds were safe for healthy cells within their MIC ranges. According to the calculated descriptors, the library was found within the drug-like chemical space and free of pan-assay interference compounds (PAINS). Molecular docking studies suggested that the compounds might be bacterial flavohemoglobin (flavoHb) inhibitors and the azole and naphthalene rings were important pharmacophores, which was further supported by pharmacophore modeling study. As a result, the current study presents several non-toxic azole derivatives with antibacterial effects. In addition to their previously reported antifungal properties, they could set a promising starting point for the future design of dual acting antimicrobials. Communicated by Ramaswamy H. Sarma.

Pharmacophore-Oriented Identification of Potential Leads as CCR5 Inhibitors to Block HIV Cellular Entry

Cysteine-cysteine chemokine receptor 5 (CCR5) has been discovered as a co-receptor for cellular entry of human immunodeficiency virus (HIV). Moreover, the role of CCR5 in a variety of cancers and various inflammatory responses was also discovered. Despite the fact that several CCR5 antagonists have been investigated in clinical trials, only Maraviroc has been licensed for use in the treatment of HIV patients. This indicates that there is a need for novel CCR5 antagonists. Keeping this in mind, the present study was designed. The active CCR5 inhibitors with known IC₅₀ value were selected from the literature and utilized to develop a ligand-based common feature pharmacophore model. The validated pharmacophore model was further used for virtual screening of drug-like databases obtained from the Asinex, Specs, InterBioScreen, and Eximed chemical libraries. Utilizing computational methods such as molecular docking studies, molecular dynamics simulations, and binding free energy calculation, the binding mechanism of selected inhibitors was established. The identified Hits not only showed better binding energy when compared to Maraviroc, but also formed stable interactions with the key residues and showed stable behavior throughout the 100 ns MD simulation. Our findings suggest that Hit1 and Hit2 may be potential candidates for CCR5 inhibition, and, therefore, can be considered for further CCR5 inhibition programs.

Structure- and ligand-based drug design methods for the modeling of antimalarial agents: a review of updates from 2012 onwards

Malaria still persists as one of the deadliest infectious disease having a huge morbidity and mortality affecting the higher population of the world. Structure and ligand-based drug design methods like molecular docking and MD simulations, pharmacophore modeling, QSAR and virtual screening are widely used to perceive the accordant correlation between the antimalarial activity and property of the compounds to design novel dominant and discriminant molecules. These modeling methods will speed-up antimalarial drug discovery, selection of better drug candidates for synthesis and to achieve potent and safer drugs. In this work, we have extensively reviewed the literature pertaining to the use and applications of various ligand and structure-based computational methods for the design of antimalarial agents. Different classes of molecules are discussed along with their target interactions pattern, which is responsible for antimalarial activity. Communicated by Ramaswamy H. Sarma.

Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B

Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure-activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R²_tr = 0.815, Q²_LMO = 0.808, R²_ex = 0.814, CCC_ex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups-especially the H-bond capable groups-must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.

Identification of Active Compounds against Melanoma Growth by Virtual Screening for Non-Classical Human DHFR Inhibitors

Antifolates such as methotrexate (MTX) have been largely known as anticancer agents because of their role in blocking nucleic acid synthesis and cell proliferation. Their mechanism of action lies in their ability to inhibit enzymes involved in the folic acid cycle, especially human dihydrofolate reductase (hDHFR). However, most of them have a classical structure that has proven ineffective against melanoma, and, therefore, inhibitors with a non-classical lipophilic structure are increasingly becoming an attractive alternative to circumvent this clinical resistance. In this study, we conducted a protocol combining virtual screening (VS) and cell-based assays to identify new potential non-classical hDHFR inhibitors. Among 173 hit compounds identified (average logP = 3.68; average MW = 378.34 Da), two-herein, called C1 and C2-exhibited activity against melanoma cell lines B16 and A375 by MTT and Trypan-Blue assays. C1 showed cell growth arrest (39% and 56%) and C2 showed potent cytotoxic activity (77% and 51%) in a dose-dependent manner. The effects of C2 on A375 cell viability were greater than MTX (98% vs 60%) at equivalent concentrations and times. Our results indicate that the integrated in silico/in vitro approach provided a benchmark to identify novel promising non-classical DHFR inhibitors showing activity against melanoma cells.

Targeting the SARS-CoV-2 HR1 with Small Molecules as Inhibitors of the Fusion Process

The rapid and global propagation of the novel human coronavirus that causes severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced an immediate urgency to discover promising targets for the treatment of this virus. In this paper, we studied the spike protein S2 domain of SARS-CoV-2 as it is the most conserved component and controls the crucial fusion process of SARS-CoV-2 as a target for different databases of small organic compounds. Our in silico methodology, based on pharmacophore modeling, docking simulation and molecular dynamics simulations, was first validated with ADS-J1, a potent small-molecule HIV fusion inhibitor that has already proved effective in binding the HR1 domain and inhibiting the fusion core of SARS-CoV-1. It then focused on finding novel small molecules and new peptides as fusion inhibitors. Our methodology identified several small molecules and peptides as potential inhibitors of the fusion process. Among these, NF 023 hydrate (MolPort-006-822-583) is one of the best-scored compounds. Other compounds of interest are ZINC00097961973, Salvianolic acid, Thalassiolin A and marine_160925_88_2. Two interesting active peptides were also identified: AP00094 (Temporin A) and AVP1227 (GBVA5). The inhibition of the spike protein of SARS-CoV-2 is a valid target to inhibit the virus entry in human cells. The discussed compounds reported in this paper led to encouraging results for future in vitro tests against SARS-CoV-2.

Phytochemical Characterization, Functional Nutrition, and Anti-Diabetic Potentials of Leptadenia hastata (pers) Decne Leaves: In Silico and In Vitro Studies

The geometrical increase in diabetes mellitus (DM) and the undesirable side effects of synthetic drugs have intensified efforts to search for an effective and safe anti-diabetic therapy. This study aimed to identify the antioxidant and anti-diabetic agents in the ethanol extract of Leptadenia hastata (EELH). The phytochemicals, antioxidant vitamins, and minerals present in EELH were determined using standard procedures to achieve this aim. Gas chromatography coupled with mass spectroscopy and flame ionization detector (GC-MS/GC-FID) was employed to identify bioactive compounds. An e-pharmacophore model was generated from the extra precision, and energy-minimized docked position of standard inhibitor, acarbose onto human pancreatic amylase (HPA, PDB-6OCN). It was used to screen the GC-MS/GC-FID library of compounds. The top-scoring compounds were subjected to glide XP-docking and prime MM-GBSA calculation with the Schrodinger suite-v12.4. The Adsorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction of the best-fit compounds was made using SwissADME and PROTOX-II webservers. Further validation of the docking results was performed with the in vitro analysis of the α-amylase and α-glucosidase inhibitory activities. EELH contains appreciable amounts of antioxidant and anti-diabetic phytoconstituents. The top-4 scoring compounds (rutin, epicatechin, kaempferol, and naringenin) from the EELH phytochemical library interacted with amino acid residues within and around the HPA active site. The ADMET prediction shows that epicatechin, kaempferol, and naringenin had favorable drug-likeness, pharmacokinetic properties, and a good safety profile. EELH demonstrated good inhibitory actions against α-amylase and α-glucosidase with 1C₅₀ values of 14.14 and 4.22 µg/mL, respectively. Thus, L hastata phytoconstituents are promising novel candidates for developing an anti-diabetic drug.

Sulfonamide Moiety as "Molecular Chimera" in the Design of New Drugs

BACKGROUND

The -SO₂NH- group is of great significance in modern pharmaceutical use since, in sulfa-drugs, it is possible to introduce easily chemical modifications, and even small changes may lead to an improved version of an already existing drug.

OBJECTIVE

This paper aims to describe updated information in the sulfonamide field with a particular focus on new mechanisms of action, especially if discovered by employing computational approaches.

METHODS

Research articles that focused on the use of the sulfonamide moiety for the design, synthesis, and in vitro/in vivo tests of various diseases were collected from various search engines like PubMed, Science Direct, Google Scholar, and Scopus, using keywords like sulfonamide moiety, aryl/heteroary lsulfonamides, alkyl sulfonamides, in silico drug design, etc. Conclusion: The more relevant reports highlighting the prominent role of sulfonamide moiety in drug discovery have been critically analyzed. Sulfonamides can be considered as "molecular chimera", which are found to form hydrogen bonds as well as interact with unipolar environments within proteins. Therefore, based on the analysis reported herein, it is strongly foresight that new entities can be developed easily to improve the available machinery helpful in the fight against new and emerging diseases.

Network analysis and ligand-based pharmacophore modeling for discovery of small molecule against glioblastoma multiforme

Aim: This study uses network pharmacology to design a c-Src inhibitor followed by pharmacophore modeling to combat glioblastoma multiforme. These in silico approaches are suitable for designing and developing new molecules of interest. Materials & methods: The authors performed virtual screening, pharmacophore analysis and validation of results using various in silico tools and reliable data from different types of literature and databases. Results: The in silico pipeline the authors followed produced reliable chemical information to combat glioblastoma. The authors identified a chemical template against the c-Src protein, which was validated statistically and computationally. Conclusion: The authors have successfully identified a chemical template against c-Src, which will be developed into a promising inhibitor in future studies.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Discovery of novel natural products as rhodesain inhibitors for human African trypanosomiasis using in silico techniques

Human African Trypanosomiasis (HAT) or sleeping sickness is caused by the Trypanosoma brucei rhodesiense, a subspecies of the Trypanosomatide family. The parasite is associated with high morbidity and mortality rate in both animals and humans, claimed to be more fatal than other vector-transmitted diseases such as malaria. The majority of existing medications are highly toxic, not effective in the late chronic phase of the disease, and require maximum dosages to fully eradicate the parasite. In this study, we used computational methods to find out natural products that inhibit the Rhodesain, a parasitic enzyme that plays an important role in the parasite's pathogenicity, multiplication, and ability to pass through the host's blood-brain barrier. A library of 270540 natural products from ZINC databases was processed by using e-pharmacophore hypnosis and screening procedures, molecular docking, ADMET processes, and MM-GBSA calculations. This led to the identification of 3 compounds (ZINC000096269390, ZINC000035485292, and ZINC000035485242) which were then subjected to molecular dynamics. The findings of this study showed excellent binding affinity and stability toward the Rhodesain and suggest they may be a hopeful treatment for HAT in the future if further clinical trials were performed.Communicated by Ramaswamy H. Sarma.

Structural insights into the discrepant synergistic activity of Codlemone and (Z)-8-dodecenol towards Grapholita molesta pheromones

BACKGROUND

Insect pheromone synergists have been widely used to produce potent pheromone products for environment-friendly pest control. Codlemone (Cod) and (Z)-8-dodecenol (Dod) are two major Grapholita molesta pheromone synergists, with Cod having greater synergism and affinity for G. molesta pheromone binding protein 2 (GmolPBP2). Uncovering structural information key to the different binding affinity of Cod and Dod to GmolPBP2 would gain insights into what causes their synergy activity discrepancy.

RESULTS

Binding modes of the two synergists in the binding pocket of GmolPBP2 were analyzed and compared by molecular dynamics-based approaches. Although Cod and Dod were stabilized in a similar hydrophobic pocket, their interaction details with GmolPBP2 were divergent due to the extra double bond (C10═C11) in Cod. The C10═C11 improved the hydrophobic interactions of Cod with around residues. Such hydrophobic interaction improvement was also reflected in the raised importance of Phe11 in the GmolPBP2-Cod interaction. Not only that, the increased hydrophobic forces introduced by the C10═C11 changed the CH2-OH orientation in the GmolPBP2-Cod complex, which improved the H-bond interaction. Electrostatic complementarity analysis further indicated the positive role of C10═C11 in optimizing GmolPBP2-Cod interaction.

CONCLUSION

The C10═C11 is thought to contribute greatly to Cod's stronger synergy as a group key to the higher GmolPBP2-affinity, based on which the improvement directions for Cod and Dod were addressed as well. Our findings will aid in the development and optimization of more effective pheromone synergists, resulting in more effective pheromone-based pest management.

Computational studies reveal potential dolichyl-phosphate N-acetylglucosaminephosphotransferase inhibitors amidst existing drugs

Dolichyl-phosphate N-acetylglucosaminephosphotransferase (dpagt1) inhibition is reported to kill tumor cells whose growth progression requires increased branching of N-linked glycans. Available dpagt1 inhibitors are grossly limited and are faced with problems of heamolytic effect and aqueous solubility thereby necessitating the search for new, safe and effective dpagt1 inhibitors. We employed computational methods to screen a dataset of ∼1300 FDA approved drugs in order to obtain theoretical dpagt1 inhibitors which could be repurposed as chemotherapeutic drugs. Top six better performing drugs, binding affinity for dpagt1 at the range of -17.63 to -20.40 kcal/mol, than the reference ligand (tunicamycin; -14.86 kcal/mol) were obtained at the end of structure-based-pharmacophore- and virtual-screening and 'induced fit' docking calculations. Analysis of their binding poses identified essential pharmacophores involved in target-ligand complexation that could be targeted in chemical modification to develop more effective and safe dpagt1 inhibitors.Communicated by Ramaswamy H. Sarma.

Combined machine learning and pharmacophore based virtual screening approaches to screen for antibiofilm inhibitors targeting LasR of Pseudomonas aeruginosa

Pseudomonas aeruginosa, a virulent pathogen affects patients with cystic fibrosis and nosocomial infections. Quorum sensing (QS) mechanism plays a crucial role in causing these ailments by mediating biofilm formation and expressing virulent genes. A novel approach to circumvent this bacterial infection is by hindering its QS network. Targeting LasR of las system serves beneficial as it holds the top position in QS system cascade. Here, we have integrated machine learning, pharmacophore based virtual screening, molecular docking and simulation studies to look for new leads as inhibitors for LasR. Support vector machine (SVM) learning algorithm was used to generate QSAR models from 66 antagonist dataset. The top three models resulted in correlation coefficient (R²) values of 0.67, 0.86, and 0.91, respectively. The correlation coefficient (R²_test) values on external test set were found to be 0.62, 0.57, and 0.55, respectively. A four-point pharmacophore model was developed. The pharmacophore hypothesis AAAD_1 was used to screen for potential leads against MolPort database in ZincPharmer. The leads which showed predicted pIC50 value of >8.00 by SVM models were subjected to docking analysis that reranked the compounds based on docking scores. Four top leads namely ZINC3851967 N-[3,5-bis(trifluoromethyl)phenyl]-5-tert-butyl-6-chloropyrazine-2-carboxamide, ZINC4024175 4-Amino-1-[(2R,3S,4S,5S)-3,4-dihydroxy-5-(hydroxymethyl)oxolan-2-yl]-2-oxopyrimidine-5-carbonitrile, ZINC2125703 N-[(5-Methoxy-4,7-dimethyl-2-oxo-2H-chromen-3-yl)acetyl]-beta-alanine, and ZINC3851966 N-[3,5-Bis(trifluoromethyl)phenyl]5-tert-butylpyrazine-2-carboxamide were selected. These compounds were checked for its stability by performing a molecular dynamics simulation for a period of 100 ns. The ADME properties of the leads were also determined. Hence, the compounds identified in this study can be used as possible leads for developing a novel inhibitor for LasR.Communicated by Ramaswamy H. Sarma.

Systematic Review of Voltage-Gated Calcium Channel α2δ Subunit Ligands for the Treatment of Chronic Neuropathic Pain and Insight into Structure-Activity Relationship (SAR) by Pharmacophore Modeling

BACKGROUND

Neuropathic pain (NP) is a complex symptom related to the nerve damage. The discovery of new drugs for treating chronic NP has been continuing for several decades, while more progress is still needed to be made because of the unsatisfactory efficacy and the side effects of the currently available drugs. Among all the approved drugs for chronic NP, voltage-gated calcium channel (VGCC) α2δ subunit ligands, also known as gabapentinoids, are among the first-line treatment and represent a class of efficacious and relatively safe therapeutic agents. However, new strategies are still needed to be explored due to the unsatisfied response rate.

OBJECTIVES

To review the latest status of the discovery and development of gabapentinoids for the treatment of chronic NP by covering both the marketed and the preclinical/clinical ones. To analyze the structure-activity relationship (SAR) of gabapentinoids to facilitate the future design of structurally novel therapeutic agents targeting VGCC α2δ subunit.

METHODS

We searched PubMed Central, Embase, Cochrane Library, Web of Science, Scopus and Espacenet for the literature and patents of diabetic peripheral neuropathic pain, postherpetic neuralgia, fibromyalgia, voltage-gated calcium channel α2δ subunit and related therapeutic agents from incipient to June 10, 2021. The SAR of gabapentinoids were analyzed by pharmacophore modeling using Phase module in Schrödinger suite.

RESULTS

A variety of gabapentinoids were identified as VGCC α2δ ligands that have ever been under development for the treatment of chronic NP. Among them, four gabapentinoids are marketed, one is at the active late clinical trials, and eight have been discontinued. Pharmacophore models were generated by using Phase module in Schrödinger suite, and common pharmacophores were predicted based on pharmacophoric features and analyzed.

CONCLUSION

The latest progress of the discovery and development of gabapentinoids for the treatment of chronic NP was reviewed. Moreover, the structure-activity relationship (SAR) of gabapentinoids is analyzed by pharmacophore modeling, which will be valuable for the future design of structurally novel therapeutic agents targeting VGCC α2δ subunit.

Discovery of Novel and Highly Potent Inhibitors of SARS CoV-2 Papain-Like Protease Through Structure-Based Pharmacophore Modeling, Virtual Screening, Molecular Docking, Molecular Dynamics Simulations, and Biological Evaluation

Background and Objective: COVID-19 has struck our society as a great calamity, and the need for effective anti-viral drugs is more urgent than ever. Papain-like protease (PLpro) of SARS CoV-2 plays important roles in virus maturation, dysregulation of host inflammation, and antiviral immune responses, which is being regarded as a promising druggable target for the treatment of COVID-19. Here, we carried out a combined screening approach to identify novel and highly potent PLpro inhibitors for the treatment of COVID-19.

Methods: We used a combined screening approach of structure-based pharmacophore modeling and molecular docking to screen an in-house database containing 35,000 compounds. SARS CoV-2 PLpro inhibition assay was used to carry out the biological evaluation of hit compounds. Molecular dynamics (MD) simulations were conducted to check the stability of the PLpro-hit complexes predicted by molecular docking.

Results: We found that four hit compounds showed excellent inhibitory activities against PLpro with IC₅₀ values ranging from 0.6 to 2.4 μM. Among them, the most promising compound, hit 2 is the best PLpro inhibitor and its inhibitory activity was about 4 times higher than that of the positive control (GRL0617). The study of MD simulations indicated that four hits could bind stably to the active site of PLpro. Further study of interaction analysis indicated that hit 2 could form hydrogen-bond interactions with the key amino acids such as Gln269 and Asp164 in the PLpro-active site.

Conclusion: Hit 2 is a novel and highly potent PLpro inhibitor, which will open the way for the development of clinical PLpro inhibitors for the treatment of COVID-19.

Molecular Modeling Study on the Interaction Mechanism between the LRRK2 G2019S Mutant and Type I Inhibitors by Integrating Molecular Dynamics Simulation, Binding Free Energy Calculations, and Pharmacophore Modeling

Leucine-rich repeat kinase 2 (LRRK2) has been reported in the pathogenesis of Parkinson's disease (PD). G2019S mutant is the most common pathogenic mutation in LRRK2-related PD patients. Inhibition of LRRK2 kinase activity is proposed to be a new therapeutic approach for PD treatment. Therefore, understanding the molecular basis of the interaction between LRRK2 and its inhibitors will be valuable for the discovery and design of LRRK2 inhibitors. However, the structure of human LRRK2 in complex with the inhibitor has not been determined, and the inhibitory mechanism underlying LRRK2 still needs to be further investigated. In this study, molecular dynamics (MD) simulation combined with the molecular mechanics generalized born surface area (MM-GBSA) binding free energy calculation and pharmacophore modeling methods was employed to explore the critical residues in LRRK2 for binding of inhibitors and to investigate the general structural features of the inhibitors with diverse scaffolds. The results from MD simulations suggest that the hinge region residues Glu1948 and Ala1950 play a significant role in maintaining the intermolecular hydrogen bond interaction with the G2019S LRRK2 protein and inhibitor. The strong hinge hydrogen bond with an occupancy rate of more than 95% represents the high activity of LRRK2 inhibitors, and the hydrogen bond interaction with the kinase catalytic loop region could compromise selectivity. Further pharmacophore modeling reveals that the high activity LRRK2 inhibitor should have one aromatic ring, one hydrogen bond acceptor, and one hydrogen bond donor. Hence, the obtained results can provide valuable information to understand the interactions of LRRK2 inhibitors at the atomic level that will be helpful in designing potent inhibitors of LRRK2.

Identification of Novel Mitochondrial Pyruvate Carrier Inhibitors by Homology Modeling and Pharmacophore-Based Virtual Screening

The mitochondrial pyruvate carrier (MPC) is an inner-mitochondrial membrane protein complex that has emerged as a drug target for treating a variety of human conditions. A heterodimer of two proteins, MPC1 and MPC2, comprises the functional MPC complex in higher organisms; however, the structure of this complex, including the critical residues that mediate binding of pyruvate and inhibitors, remain to be determined. Using homology modeling, we identified a putative substrate-binding cavity in the MPC dimer. Three amino acid residues (Phe66 (MPC1) and Asn100 and Lys49 (MPC2)) were validated by mutagenesis experiments to be important for substrate and inhibitor binding. Using this information, we developed a pharmacophore model and then performed a virtual screen of a chemical library. We identified five new non-indole MPC inhibitors, four with IC₅₀ values in the nanomolar range that were up to 7-fold more potent than the canonical inhibitor UK-5099. These novel compounds possess drug-like properties and complied with Lipinski's Rule of Five. They are predicted to have good aqueous solubility, oral bioavailability, and metabolic stability. Collectively, these studies provide important information about the structure-function relationships of the MPC complex and for future drug discovery efforts targeting the MPC.

Ligand-Based Modeling of CXC Chemokine Receptor 4 and Identification of Inhibitors of Novel Chemotypes as Potential Leads towards New Anti-COVID-19 Treatments

BACKGROUND

Chemokines are involved in several human diseases and in different stages of COVID-19 infection and play critical role in the pathophysiology of the associated acute respiratory disease syndrome, a major complication leading to death among COVID-19 patients. In particular, CXC chemokine receptor 4 (CXCR4) was found to be highly expressed in COVID-19 patients.

METHODS

We herein describe a computational workflow based on combining pharmacophore modeling and QSAR analysis towards the discovery of novel CXCR4 inhibitors. Subsequent virtual screening identified two promising CXCR4 inhibitors from the National Cancer Institute (NCI) list of compounds. The most active hit showed in vitro IC50 value of 24.4 µM.

RESULTS AND CONCLUSION

These results prove the validity of the QSAR model and associated pharmacophore models as means to screen virtual databases towards new CXCR4 inhibitors as leads for the development of new COVID-19 therapies.