Identification of novel PHGDH inhibitors based on computational investigation: an all-in-one combination strategy to develop potential anti-cancer candidates.

Objective: Biological studies have elucidated that phosphoglycerate dehydrogenase (PHGDH) is the rate-limiting enzyme in the serine synthesis pathway in humans that is abnormally expressed in numerous cancers. Inhibition of the PHGDH activity is thought to be an attractive approach for novel anti-cancer therapy. The development of structurally diverse novel PHGDH inhibitors with high efficiency and low toxicity is a promising drug discovery strategy. Methods: A ligand-based 3D-QSAR pharmacophore model was developed using the HypoGen algorithm methodology of Discovery Studio. The selected pharmacophore model was further validated by test set validation, cost analysis, and Fischer randomization validation and was then used as a 3D query to screen compound libraries with various chemical scaffolds. The estimated activity, drug-likeness, molecular docking, growing scaffold, and molecular dynamics simulation processes were applied in combination to reduce the number of virtual hits. Results: The potential candidates against PHGDH were screened based on estimated activity, docking scores, predictive absorption, distribution, metabolism, excretion, and toxicity (ADME/T) properties, and molecular dynamics simulation. Conclusion: Finally, an all-in-one combination was employed successfully to design and develop three potential anti-cancer candidates.

Discovery of a novel DYRK1A inhibitor with neuroprotective activity by virtual screening and in vitro biological evaluation.

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is implicated in accumulation of amyloid ß-protein (Aß) and phosphorylation of Tau proteins, and thus represents an important therapeutic target for neurodegenerative diseases. Though many DYRK1A inhibitors have been discovered, there is still no marketed drug targeting DYRK1A. This is partly due to the lack of effective and safe chemotypes. Therefore, it is still necessary to identify new classes of DYRK1A inhibitors. By performing virtual screening with the workflow mainly composed of pharmacophore modeling and molecular docking as well as the following DYRK1A inhibition assay, we identified compound L9, ((Z)-1-(((5-phenyl-1H-pyrazol-4-yl)methylene)-amino)-1H-tetrazol-5-amine), as a moderately active DYRK1A inhibitor (IC50: 1.67 µM). This compound was structurally different from the known DYRK1A inhibitors, showed a unique binding mode to DYRK1A. Furthermore, compound L9 showed neuroprotective activity against okadaic acid (OA)-induced injury in the human neuroblastoma cell line SH-SY5Y by regulating the expression of Aß and phosphorylation of Tau protein. This compound was neither toxic to the SH-SY5Y cells nor to the human normal liver cell line HL-7702 (IC50: >100 µM). In conclusion, we have identified a novel DYRK1A inhibitor with neuroprotective activity through virtual screening and in vitro biological evaluation, which holds the promise for further study.

Integrated Computational Approaches for Drug Design Targeting Cruzipain.

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi.

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene.

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing.

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1 µM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14 kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Multistage in silico approach to identify novel quinoline derivatives as potential c-kit kinase inhibitors.

The type II-C-KIT signaling network has been extensively studied for its potential as a target for cancer treatment, leading to the investigation of quinoline derivatives as compounds with inhibitory effects on c-Kit kinase. In this study, a multistage approach was employed, including the creation of pharmacophore models, 3D QSAR analysis, virtual screening, docking investigations, and molecular dynamics stimulation. The pharmacophore evaluation included a data set of 29 ligands, which resulted in the generation of the ADDHR_1pharmacophore model as the most promising, with a survival score of 5.6812. The main objective was to utilize the atom-based 3D-QSAR approach for generating robust 3D-QSAR models aimed at identifying new TypeII-C-kit kinase inhibitors. The evaluations of these models have convincingly demonstrated their high predictive power (Q2 = 0.6547, R2 = 0.9947). Using atom-based 3D-QSAR data, a total of 7564 novel compounds were generated from R-group enumeration. Molecular docking and MM-GBSA study revealed that compound A1 exhibited the highest binding score of -9.30 kcal/mol and a Δ GBind value of -90.56 kcal/mol. The ZINC compounds were then screened using the pharmacophore model, followed by virtual screening, which identified ZINC65798256, ZINC09317958, ZINC73187176, and ZINC76176670 as potential candidates with promising docking scores. Among these, ZINC65798256 demonstrated the best binding interactions with amino acid residues, ASP810, LYS623, CYS673, and THR670 (PDB ID: 1T46). To further analyze the structural features and molecular interactions, molecular dynamics simulation was conducted for a time scale of 100 ns.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics simulation for identification of potential ERK inhibitors.

As the downstream component of the mitogen-activated protein kinases (MAPK) pathway, the extracellular signal-regulated kinase (ERK) is responsible for phosphorylating a broad range of substrates in cell proliferation, differentiation, and survival. Direct targeting the ERK proteins by the piperidinopyrimidine urea-based inhibitors has been demonstrated to be an effective way to block the MAPK signaling pathway in inhibiting tumor growth. In order to discover better inhibitors, a computer-aided drug design (CADD) approach was employed to reveal the pharmacological characteristics and mechanisms of action. The pharmacophore model was generated on the basis of the compounds with eight features, i.e., four hydrogen bond acceptor atoms, one hydrogen bond donor atom, and three hydrophobic centers. A total of 14 hit compounds were obtained through virtual screening. Two potential inhibitors, namely VS01 and VS02, have been identified by molecular docking and molecular dynamics simulations. Both compounds are capable of attaching to the ERK pocket precisely. The binding free energies of VS01 and VS02 are about 15 kJ/mol and 4 kJ/mol stronger than that of the clinic Ulixertinib because of the characteristic hydrogen bonding, electrostatic, and hydrophilic interactions. The present theoretical investigations shed new light on the rational design of the potential ERK inhibitors to stimulate further experimental tests.Communicated by Ramaswamy H. Sarma.

Identification of potentially high drug-like VEGFR2/c-Met dual-target type II kinase inhibitors with symmetric skeletons based on structural screening.

Vascular endothelial growth factor receptor 2 (VEGFR2) and c-Mesenchymal epithelial transition factor (c-Met) are tyrosine kinase receptors associated with the occurrence of malignant tumors. Studies have shown that inhibition of VEGFR2 promotes a feedback increase in c-Met, a mechanism linked to the emergence of resistance to VEGFR2 inhibitors. Therefore, treatment targeting both VEGFR2 and c-Met will have better application prospects. In this study, hierarchical virtual screening was performed on ZINC15, Molport and Mcule-ULTIMATE databases to identify potential VEGFR2/c-Met dual inhibitors. Firstly, the best pharmacophore model for each target was used to cross-screen the three databases, and the compounds that could match the two pharmacophore models were then retained based on the Fit Value of the respective crystal ligands. Compounds ZINC, MOL, and MLB named after their database sources were retained by binding pattern analysis and docking assessment. ADMET predictions indicated that ZINC had significantly higher oral bioavailability compared to the approved drug cabozantinib. This is likely due to ZINC's unique symmetrical backbone with less structure complexity, which may reduce the occurrence of adverse effects. Molecular dynamics simulations and binding free energy analysis showed that all three hit compounds were able to stably bind at the active site, but only ZINC could form high occupancy of hydrogen bonds with both VEGFR2 and c-Met, and also only ZINC had a higher binding free energy than crystal ligands, suggesting that ZINC was the most likely potential VEGFR2/c-Met dual-target inhibitor. This finding provides a promising starting point for the development of VEGFR2/c-Met dual-target inhibitors.Communicated by Ramaswamy H. Sarma.

In silico approaches for the identification of novel ULK1 inhibitors: pharmacophore model, molecular docking and molecular dynamics simulations.

The serine/threonine kinase unc-51-like autophagy activating kinase 1 (ULK1) has been regarded as an attractive target for tumor therapy. In this study, in silico approaches, such as the pharmacophore-based virtual screening strategy, molecular docking and molecular dynamics (MD) simulations, were applied to develop novel potential ULK1 inhibitors. The pharmacophore models based on known aminopyrimidine ULK1 inhibitors were constructed to screen the dataset of 1.68 million compounds, which were obtained via screening the 2.30 million compounds in ChEMBL database by Lipinski's rule of five. Seven novel compounds and 1 known ULK1 inhibitor stand out for the strong virtual biological activity by molecular docking, cluster analysis, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculation and Absorption Distribution Metabolism Excretion Toxicity (ADMET) prediction. Their results of MD included principal component analysis (PCA) and Free Energy Landscapes surface (FELs) indicated that the protein-ligand complex was stable in simulated trajectories of 100 ns. The binding free energy (BFE) calculations showed that a total of 6 novel compounds (CL130, CL834, CL961, CL966, CL163 and CL329) with the stable binding state and stronger BFE (-61.17 to -37.01 kcal/mol) than that of original ligand 3RF (-36.66 kcal/mol). With reference to the ULK1 inhibition of 3RF (IC50 = 160 nM), it can be inferred that these compounds could be used as a new type of potential ULK1 inhibitors and be worthy of further investigation for tumor treatments.Communicated by Ramaswamy H. Sarma.

Revolutionizing antiretroviral therapy for human immunodeficiency virus/AIDS: A computational approach using molecular docking, virtual screening, and 3D pharmacophore building to address therapeutic failure and propose highly effective candidates.

OBJECTIVES: In the context of human immunodeficiency virus (HIV) treatment, the emergence of therapeutic failures with existing antiretroviral drugs presents a significant challenge. This study aims to employ advanced molecular modeling techniques to identify potential alternatives to current antiretroviral agents. METHODS: The study focuses on three essential classes of antiretroviral drugs: nucleoside reverse transcriptase inhibitors (NRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), and protease inhibitors (PIs). Computational analyses were performed on a database of 3,343,652 chemical molecules to evaluate their binding affinities, pharmacokinetic properties, and interactions with viral reverse transcriptase and protease enzymes. Molecular docking, virtual screening, and 3D pharmacophore modeling were utilized to identify promising candidates. RESULTS: Molecular docking revealed compounds with high binding energies and strong interactions at the active sites of target enzymes. Virtual screening narrowed down potential candidates with favorable pharmacological profiles. 3D pharmacophore modeling identified crucial structural features for effective binding. Overall, two molecules for class 1, 7 molecules for class 2, and 2 molecules for class 3 were selected. These compounds exhibited robust binding affinities, interactions with target enzymes, and improved pharmacokinetic properties, showing promise for more effective HIV treatments in cases of therapeutic failures. CONCLUSION: The combination of molecular docking, virtual screening, and 3D pharmacophore modeling yielded lead compounds that hold potential for addressing HIV therapeutic failures. Further experimental investigations are essential to validate the efficacy and safety of these compounds, with the ultimate goal of advancing toward clinical applications in HIV management.

Identification of 1,3,4-oxadiazoles as tubulin-targeted anticancer agents: a combined field-based 3D-QSAR, pharmacophore model-based virtual screening, molecular docking, molecular dynamics simulation, and density functional theory calculation approach.

Cancer is one of the most prominent causes of death worldwide and tubulin is a crucial protein of cytoskeleton that maintains essential cellular functions including cell division as well as cell signalling, that makes an attractive drug target for cancer drug development. 1,3,4-oxadiazoles disrupt microtubule causing G2-M phase cell cycle arrest and provide anti-proliferative effect. In this study, field-based 3D-QSAR models were developed using 62 bioactive anti-tubulin 1,3,4-oxadiazoles. The best model characterized by PLS factor 7 was rigorously validated using various statistical parameters. Generated 3D-QSAR model having high degree of confidence showed favourable and unfavourable contours around 1,3,4-oxadiazole core that assisted in defining proper spatial positioning of desired functional groups for better bioactivity. A five featured pharmacophore model (AAHHR_1) was developed using same ligand library and validated through enrichment analysis (BEDROC160.9 value = 0.59, Average EF 1% = 27.05, and AUC = 0.74). Total 30,212 derivatives of 1,3,4-oxadiazole obtained from PubChem database was prefiltered through validated pharmacophore model and docked in XP mode on binding cavity of tubulin protein (PDB code: 1SA0) which led into the identification of 11 HITs having docking scores between -7.530 and -9.719 kcal/mol while the reference compound Colchicine exerted docking score of -7.046 kcal/mol. Following the analysis of MM-GBSA and ADME studies, HIT1 and HIT4 emerged as the two promising hits. To verify their thermodynamic stability at the target site, molecular dynamic simulations were carried out. Both HITs were further subjected to DFT analysis to determine their HOMO-LUMO energy gap for ensuring their biological feasibility. Finally, molecular docking based structural exploration for 1,3,4-oxadiazoles to set up a lead of Formula I for further advancements of tubulin polymerization inhibitors as anti-cancer agents.Communicated by Ramaswamy H. Sarma.

Broadening the scope of WEE1 inhibitors: identifying novel drug candidates via computational approaches and drug repurposing.

The protein kinase Wee1 plays a vital role in the G2/M cell cycle checkpoint activation, triggered by double-stranded DNA disruptions. It fulfills this task by phosphorylating and consequently deactivating the cyclin B linked to Cdk1/Cdc2 at the Tyr15 residue, leading to a G2 cell cycle halt and subsequent delay of mitosis post DNA damage. Despite advancements, only the Wee1 inhibitor MK1775 has made it to Phase II clinical trials, presenting a challenge in innovative chemical structure development for small molecule discovery. To navigate this challenge, we employed an e-pharmacophore model of the MK1775-WEE1 complex (PDB ID: 5V5Y), using in silico screening of FDA-approved drugs. We chose six drugs for analog creation, guided by docking scores, key residue interactions, and ligand occupancy. Utilizing the 'DrugSpaceX' database, we generated 2,776 analogues via expert-defined transformations. Our findings identified DE90612 as the top-ranked analogue, followed by DE363106, DE489678, DE395383, DE90548, DE689343, DE395019, and DE538066. These analogues introduced unique structures not found in other databases. A t-SNE structurally diversified distribution map unveiled promising transformations linked to Temozolomide for WEE1 inhibitor development. Simulations of the WEE1-DE90612 complex (a Temozolomide analogue) for 200 nanoseconds demonstrated stability, with DE90612 forging robust bonds with active site residues and sustaining vital contacts at ASN376 and CYS379. These results underscore DE90612's potential inhibitory properties at the WEE1 binding site, warranting additional in vitro and in vivo exploration for its anticancer activity. Our approach outlines a promising pathway for creating diverse WEE1 inhibitors with suitable biological properties for potential oncology therapeutics.Communicated by Ramaswamy H. Sarma.

Field-based 3D-QSAR for tyrosine protein kinase JAK-2 inhibitors.

The present work aimed to develop a Field-based 3D-QSAR model with existing JAK-2 inhibitors. The JAK-STAT pathway is known to play a role in the development of autoimmune diseases, including rheumatoid arthritis, ulcerative colitis, and Crohn's disease. Dysregulation of JAK-STAT is also linked to the development of myelofibrosis and other myeloproliferative diseases. JAK antagonists can be used in many areas of medicine. There are many compounds that already show inhibition of Jak-2. We have developed a Field-based 3D QSAR model which showed good correlation values (r2 0.884 and q2 0.67) with an external test set regression pred_r2 0.562. Various properties, such as electronegativity, electro positivity, hydrophobicity, and shape features, were studied under the activity atlas to determine the inhibitory potential of ligands. These were also identified as important structural features responsible for biological activity. We performed virtual screening based on the pharmacophore features of the co-crystal ligand (PDB ID: 3KRR) and a dataset of NPS was selected with a RMSD value less than 0.8. The developed 3D QSAR model was used to screen ligands and calculate the predicted JAK-2 inhibition activity (pKi). The results of the virtual screening were validated using molecular docking and molecular dynamics simulations. SNP1 (SN00154718) and SNP2 (SN00213825) showed binding affinity of -11.16 and -11.08 kcal/mol, respectively, which were very close to the crystal ligand of 3KRR, -11.67 kcal/mol. The RMSD plot of the protein-ligand complex of SNP1 and 3KRR showed stable interactions with an average RMSD of 2.89 Å. Thus, a statistically robust 3D QSAR model could reveal more inhibitors and aid in the design of novel JAK-2 inhibitors.

LSD1-Based Reversible Inhibitors Virtual Screening and Binding Mechanism Computational Study.

As one of the crucial targets of epigenetics, histone lysine-specific demethylase 1 (LSD1) is significant in the occurrence and development of various tumors. Although several irreversible covalent LSD1 inhibitors have entered clinical trials, the large size and polarity of the FAD-binding pocket and undesired toxicity have focused interest on developing reversible LSD1 inhibitors. In this study, targeting the substrate-binding pocket of LSD1, structure-based and ligand-based virtual screenings were adopted to expand the potential novel structures with molecular docking and pharmacophore model strategies, respectively. Through drug-likeness evaluation, ADMET screening, molecular dynamics simulations, and binding free energy screening, we screened out one and four hit compounds from the databases of 2,029,554 compounds, respectively. Generally, these hit compounds can be divided into two categories, amide (Lig2 and Comp2) and 1,2,4-triazolo-4,3-α-quinazoline (Comp3, Comp4, Comp7). Among them, Comp4 exhibits the strongest binding affinity. Finally, the binding mechanisms of the hit compounds were further calculated in detail by the residue free energy decomposition. It was found that van der Waals interactions contribute most to the binding, and FAD is also helpful in stabilizing the binding and avoiding off-target effects. We believe this work not only provides a solid theoretical foundation for the design of LSD1 substrate reversible inhibitors, but also expands the diversity of parent nucleus, offering new insights for synthetic chemists.

Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics.

Breast cancer is a silent killer malady among women and a serious economic burden in health care management. A case of breast cancer is diagnosed among women every 19 s, and every 74 s, a woman dies of breast cancer somewhere in the world. Despite the pop-up of progressive research, advanced treatment approaches, and preventive measures, breast cancer remains amplifying ailment. The nuclear factor kappa B (NF-κB) is a key transcription factor that links inflammation with cancer and is demonstrated as being involved in the tumorigenesis of breast cancer. The NF-κB transcription factor family in mammals consists of five proteins; c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52). The antitumor effect of NF-κB has also been explored in breast cancer, however, the actual treatment for breast cancer is yet to be discovered. This study is attributed to the identification of novel drug targets against breast cancer by targeting c-Rel, RelA(p65), RelB, NF-κB1(p50), and NF-κB2(p52) proteins. To identify the putative active compounds, a structure-based 3D pharmacophore model to the protein active site cavity was generated followed by virtual screening, molecular docking, and molecular dynamics (MD) simulation. Initially, a library of 45000 compounds were docked against the target protein and five compounds namely Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 were selected for further analysis. The relative binding affinity of Z56811101, Z653426226, Z1097341967, Z92743432, and Z464101066 with NF-κB1 (p50), NF-κB2 (p52), RelA (p65), RelB, and c-Rel proteins were -6.8, -8, -7.0, -6.9, and -7.2 kcal/mol, respectively which remained stable throughout the simulations of 200 ns. Furthermore, all of these compounds depict maximum drug-like properties. Therefore, the proposed compounds can be a potential candidate for patients with breast cancer, but, experimental validation is needed to ensure their safety.Communicated by Ramaswamy H. Sarma.

In silico analysis of marine natural product for protein arginine methyltransferase 5(PRMT5) inhibitors based on pharmacophore and molecular docking.

Over the past few decades, various inhibitors of PRMT5 have been developed because of its involvement in a variety of tumor development processes. As of now, no drugs targeting PRMT5 have been approved, and multiple drugs entering clinical trials have proven to have side effects. In this study, PRMT5 was used to perform virtual screening of 52119 marine natural compounds by combining various methods. We constructed 20 pharmacophore models based on multiple ligands. The best pharmacophore model AARR_2 was selected by analyzing the statistical parameters of the pharmacophore model and the binding characteristics of the ligand active site, and then 3552 compounds were screened out. Compared with the positive compound, 46 compounds were selected based on the molecular docking fraction and docking mode analysis. Then, 3D-QSAR was used to analyze the relationship between structure and activity of the compounds. Then, in addition to marine compounds 36404, 36405 and 14436, we selected compound 46 (the positive control compound) and used the CLC-Pred online Web server to predict their cytotoxicity to human cell lines, making cell experiments possible. Finally, we conducted the prediction of ADMET in order to better promote clinical trials. After comprehensive judgment, we screened out the marine natural compounds 36404 and 36405 as candidates for PRMT5 substrate competitive inhibitors.Communicated by Ramaswamy H. Sarma.

Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation.

Ribosomal protein S6 kinase beta-1 (S6K1) is considered a potential target for the treatment of various diseases, such as obesity, type II diabetes, and cancer. Development of novel S6K1 inhibitors is an urgent and important task for the medicinal chemists. In this research, an effective ensemble-based virtual screening method, including common feature pharmacophore model, 3D-QSAR pharmacophore model, naïve Bayes classifier model, and molecular docking, was applied to discover potential S6K1 inhibitors from BioDiversity database with 29,158 compounds. Finally, 7 hits displayed considerable properties and considered as potential inhibitors against S6K1. Further, carefully analyzing the interactions between these 7 hits and key residues in the S6K1 active site, and comparing them with the reference compound PF-4708671, it was found that 2 hits exhibited better binding patterns. In order to further investigate the mechanism of the interactions between 2 hits and S6K1 at simulated physiological conditions, the molecular dynamics simulation was performed. The ΔGbind energies for S6K1-Hit1 and S6K1-Hit2 were - 111.47 ± 1.29 and - 54.29 ± 1.19 kJ mol-1, respectively. Furthermore, deep analysis of these results revealed that Hit1 was the most stable complex, which can stably bind to S6K1 active site, interact with all of the key residues, and induce H1, H2, and M-loop regions changes. Therefore, the identified Hit1 may be a promising lead compound for developing new S6K1 inhibitor for various metabolic diseases treatment.

In silico study of natural antioxidants.

Antioxidants are the body's defense system against the damage of reactive oxygen species, which are usually produced in the body through various physiological processes. There are various sources of these antioxidants such as endogenous antioxidants in the body and exogenous food sources. This chapter provides important information on methods used to investigate antioxidant activity and sources of plant antioxidants. Over the past two decades, numerous studies have demonstrated the importance of in silico research in the development of novel natural and synthesized antioxidants. In silico methods such as quantitative structure-activity relationships (QSAR), pharmacophore, docking, and virtual screenings are play critical roles in designing effective antioxidants that may be synthesized and tested later. This chapter introduces the available in silico approaches for different classes of antioxidants. Many successful applications of in silico methods in the development and design of novel antioxidants are thoroughly discussed. The QSAR, pharmacophore, molecular docking techniques, and virtual screenings process summarized here would help readers to find out the proper mechanism for the interaction between the free radicals and antioxidant compounds. Furthermore, this chapter focuses on introducing new QSAR models in combination with other in silico methods to predict antioxidants activity and design more active antioxidants. In silico studies are essential to explore largely unknown plant tissue, food sources for antioxidant synthesis, as well as saving time and money in such studies.

Field and atom-based 3D-QSAR models of chromone (1-benzopyran-4-one) derivatives as MAO inhibitors.

Monoamine oxidases (MAOs) are flavo-enzymes that aid in the oxidative deamination of neurotransmitters like dopamine, serotonin, and epinephrine. MAO inhibitors are antidepressants that work by preventing the breakdown of brain neurotransmitters and regulating mood. MAO inhibitors that use the chromone (1-benzopyran-4-one) structure have been found to be quite effective in studies. The current study involves the creation of pharmacophore models, 3-D QSAR, virtual screening, and docking investigations, all of which are evaluated using various criteria. The investigation included 39 ligands that emerged pharmacophore AHRRR_1, as the best pharmacophore model with a survival score of 5.6485. The 3D QSAR investigation revealed a significant model with the values of R2 = 0.9064 and Q2 = 0.8239. Docking study revealed that compound 18 had the highest docking (-10.402 kcal/mol) score in the series and showed interactions with the essential amino acid TYR398 required for MAO inhibitory activity. ZINC compounds were screened using the created pharmacophore model, which was followed up with a virtual screening study. The ZINC compounds with the best XP docking scores are ZINC03113255, ZINC07777127, ZINC05166353 and ZINC09341502 (with docking scores -10.021, -9.486, -8.031 and -7.792 kcal/mol, respectively). ZINC03113255, which showed the best score, has binding interactions with amino acid residues, TYR326, TYR398 and LYS296 of monoamine oxidase B. The ADME analysis demonstrated the compound's drug-like characteristics. The findings of this study may be used in the development of chromone compounds that target the MAO inhibitor.Communicated by Ramaswamy H. Sarma.

Ligand-based in silico identification and biological evaluation of potential inhibitors of nicotinamide N-methyltransferase.

Nicotinamide N-methyltransferase (NNMT) is a protein coding gene, which methylates the nicotinamide (NA) (vitamin B3) to produce 1-methylnicotinamide (MNA). Several studies have suggested that the overexpression of NNMT is associated with different metabolic disorders like obesity and type-2 diabetes thereby making it an important therapeutic target for development of anti-diabetic agents. Here we describe a workflow for identification of new inhibitors of NNMT from a library of small molecules. In this study, we have hypothesized a four-point pharmacophore model based on the pharmacophoric features of reported NNMT inhibitors in the literature. The statistically significant pharmacophore hypothesis was used to explore the Maybridge compound library that resulted in mapping of 1330 hit compounds on the proposed hypothesis. Subsequently, a total of eight high scoring compounds, showing good protein-ligand interactions in the molecular docking study, were selected for biological evaluation of NNMT activity. Eventually, four compounds were found to show significant inhibitory activity for NNMT and can be further explored to design new derivatives around the identified scaffolds with improved activities as NNMT inhibitors.