In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling, molecular docking, and dynamics simulations

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Designing a novel drug-drug conjugate as a prodrug for breast cancer therapy: in silico insights

Breast cancer (BC) poses a significant global health threat, necessitating innovative therapeutic approaches. The ribosomal s6 kinase 2 (RSK2) has emerged as a promising target due to its roles in cell proliferation and survival. This study proposes a drug-drug conjugate prodrug comprising Methotrexate (hydrophobic) and Capecitabine (hydrophilic) for BC treatment. In silico approaches, including Molecular Docking, Molecular Dynamics Simulations, MM-PBSA, ADME, and DFT calculations were employed to evaluate the prodrug's potential. The designed MET-CAP ligand exhibits a robust docking score (-8.980 kcal/mol), superior binding affinity (-53.16 kcal/mol), and stable dynamic behavior (0.62 nm) compared to native ligands. The DFT results reveal intramolecular charge transfer in MET-CAP (HLG = 0.09 eV), indicating its potential as a BC inhibitor. ADME analysis suggests satisfactory pharmaceutically relevant properties. The results indicate that the conjugated MET-CAP ligand exhibits favorable binding characteristics, stability, and pharmaceutically relevant properties, making it a potential RSK2 inhibitor for BC therapy. The multifaceted approach provides insights into binding interactions, stability, and pharmacokinetic properties, laying the foundation for further experimental validation and potential clinical development.

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

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

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND

The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses.

METHODS

In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα.

RESULTS

Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity.

CONCLUSION

Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis

Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R2train = 0.8646, R2test = 0.8864, Q2 = 0.5773) and the predicted inhibitory activity (pIC50) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules.Communicated by Ramaswamy H. Sarma.

Data mining and molecular dynamics analysis to detect HIV-1 reverse transcriptase RNase H activity inhibitor

HIV-1 is a deadly virus that affects millions of people worldwide. In this study, we aimed to inhibit viral replication by targeting one of the HIV-1 proteins and identifying a new drug candidate. We used data mining and molecular dynamics methods on HIV-1 genomes. Based on MAUVE analysis, we selected the RNase H activity of the reverse transcriptase (R.T) enzyme as a potential target due to its low mutation rate and high conservation level. We screened about 94,000 small molecule inhibitors by virtual screening. We validated the hit compounds' stability and binding free energy through molecular dynamics simulations and MM/PBSA. Phomoarcherin B, known for its anticancer properties, emerged as the best candidate and showed potential as an HIV-1 reverse transcriptase RNase H activity inhibitor. This study presents a new target and drug candidate for HIV-1 treatment. However, in vitro and in vivo tests are required. Also, the effect of RNase H activity on viral replication and the interaction of Phomoarcherin B with other HIV-1 proteins should be investigated.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

Identification of potential andrographolide-based drug candidate against Keap1-Nrf2 pathway through rigorous cheminformatics screening

The Keap1-Nrf2 [Kelch-like ECH-associated protein-1-Nuclear factor erythroid-2-related factor-2] regulatory pathway plays a vital role in the protection of cells by regulating transcription of antioxidant and detoxification genes. Andrographolide (AGP) regulates the Keap1-Nrf2 pathway by inhibiting the Keap1 protein. To identify a more potent AGP analog as a therapeutic agent against Keap1 protein, in this work, cheminformatics analysis of 237 AGP analogs was carried out. Amongst these, five AGP analogs were screened through virtual screening followed by their molecular docking analysis against Keap1 protein, which revealed greater binding affinities (binding energy =  - 4.15 to - 5.59 kcal/mol) for the shortlisted AGP analogs compared to AGP (binding energy =  - 4.02 kcal/mol). Pharmacophore mapping indicated 14 spatial features, including 3 hydrogen bond acceptors and 11 hydrophobic, while ADME analysis established the potential of all five analogs as orally-active drug-like candidates based on Lipinski's rule of five. We also examined the chemical reactivity of AGP and the shortlisted AGP analogs using DFT analysis, which revealed that except for one analog (AGP_A2) all are more chemically reactive than AGP. Further, molecular dynamics simulation analysis and MM/GBSA evidenced that AGP_A1 (PubchemID-123361152), AGP_A3 (PubchemID-58209855) and AGP_A4 (PubchemID-101362374) are the best drug like candidates compared to AGP and have greater potential to activate the Keap1-Nrf2 pathway by inhibiting the Keap1 protein.

Andrographolide-based potential anti-inflammatory transcription inhibitors against nuclear factor NF-kappa-B p50 subunit (NF-κB p50): an integrated molecular and quantum mechanical approach

The unregulated activation of nuclear factor-κB (NF-κB) is a critical event in the progression of various inflammatory diseases such as ulcerative colitis, asthma, rheumatoid arthritis, bacterial induced gastritis, etc. Hence, blocking the transcriptional activity of NF-κB is a promising strategy towards the development of an anti-inflammatory agent. In this study, an integrated molecular and quantum mechanical approach was carried out to find a new potent andrographolide (AGP)-based analog that can inhibit DNA binding to NF-κB p50 and manifest anti-inflammatory activity. Our approach includes multiple sequence alignment, virtual screening, molecular docking (protein-ligand and protein-DNA), in silico site-directed mutagenesis, ADMET prediction, DFT (HOMO, LUMO, HLG, and EPM energy) analysis, MD simulation, and MM/GBSA rescoring. The virtual screening analysis of 237 AGP analogs yielded the five lead compounds based on the binding affinity. Further, molecular interactive docking and ADMET prediction of hit analogs revealed that Ana2 ((3Z,4S)-3-[2-[(4aR,6aS,7R,10aS,10bR)-3,3,6a,10b-tetramethyl-8-methylidene-1,4a,5,6,7,9,10,10a-octahydronaphtho[2,1-d][1,3]dioxin-7-yl]ethylidene]-4-hydroxyoxolan-2-one) is the most potent moiety as it displays the strongest binding affinity and better molecular/pharmacokinetic features. Moreover, DFT, MD simulation, and MM/GBSA studies corroborated the docking results and demonstrated better chemical and dynamic stability with the least binding free energy (- 29.99 kcal/mol) for the Ana2. Site-directed mutagenesis investigation (Cys62Ala) establishes the importance of the Cys62 amino acid residue towards the binding interaction and stability of Ana2 with NF-κB p50. Overall, the identified NF-κB p50 inhibitor opens up a new research horizon towards the development of plant-based anti-inflammatory drugs to combat progressive inflammatory diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03431-9.

Influence of cation-π interactions to the structural stability of phycocyanin proteins: A computational study

The influences of cation-π interactions in phycocyanin proteins and their environmental preferences were analyzed. The number of interactions formed by arginine showed to be higher than those formed by the lysine in the cationic group, while histidine is comparatively higher than phenylalanine and N-terminal residue in the π group. Arg-Tyr and Arg-Phe interacting pairs are predominant among the various pairs analyzed. Cation-π interactions are distance-dependent and can be realized above a wider area above the π ring. We analyzed the energy contribution resulting from cation-π interactions using ab initio calculations. The energy contribution resulting from the most frequent cation-π interactions was in the lower range of strong hydrogen bonds. The results showed that, while most of their interaction energies lay ranged from - 2 to - 8 kcal/mol, those energies could be up to -12- 12 kcal/mol. Stabilization centers for these proteins showed that all residues found in cation-π interactions are important in locating one or more of such centers. In the cation-π interacting residues, 54% of the amino acid residues involved in these interactions might be conserved in phycocyanins. From this study, we infer that cation-π forming residues play an important role in the stability of the multiply commercially used phycocyanin proteins and could help structural biologists and medicinal chemists to design better and safer drugs.

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.