Current Computer - Aided Drug Design - Volume 18, Issue 5, 2022

Background & Objective: Candida albicans (C.-P. Robin) Berkhout, the pathogenic yeasts’ ability to transform from yeast to hyphal forms in the bloodstream is essential during systemic infections. Among the several virulence factors studied, secreted aspartic proteinases (SAPs) involved in hyphal penetration are targets of putative hyphal inhibitors. Upregulation of SAP6 gene, (two-to 31- fold high) during budded to hyphal transition and lack of studies on its inhibition, prompted us to investigate this particular protein using in silico tools. Results: Hyphal inhibition of germinating yeast cells by melianone, a triterpenoid, from Swietenia mahagoni (L.) Jacq. (Meliaceae) was observed at 0.1 μM (IC50). One of the targets of putative hyphal inhibitors, SAP, was assayed and for the first time, 50 % of the biological SAP activity was found to be inhibited by melianone at 0.125 μM. This data on SAP inhibition led us to analyse the 3-dimensional structure for SAP6 protein that was constructed through a combination of homology modelling and ab-initio method (Phyre2) and validated before performing Induced Fit Docking (IFD). Melianone formed H-bond and hydrophobic interactions with the crucial residues (ASP108, TYR160, ALA161, ASP162, ASP294, THR297, ASP379) in the catalytic site of SAP6 with a glide energy (-)54.9327 kcal/mol upon Induced Fit Docking (IFD). Conclusion: We report here for the first time on the SAP inhibitory ability of melianone at 0.125 uM. Being a small molecular mass inhibitor, binding with high affinity to the S3 pocket sites of SAP proteins provides evidence for pre-clinical testing of such compounds against fungal pathogens. The study is a valuable insight for further research on novel and effective inhibitors targeting SAP.

Background: The COVID-19 pandemic emerged at the end of 2019 in China and spread rapidly all over the world. Scientists strive to find virus-specific antivirals against COVID-19 disease. This study aimed to assess some flavolignans as potential SARS-CoV-2 main protease (SARS-CoV-2 Mpro) inhibitors using molecular docking study, molecular dynamic simulations, and ADME analysis. Methods: The detailed interactions between the flavolignans and SARS-CoV-2 Mpro were determined using Autodock 4.2 software. SARS-CoV-2 Mpro was docked with selected flavolignans, and the docking results were analyzed by Autodock 4.2 and Biovia Discovery Studio 4.5. The SARS-CoV-2 Mpro-flavolignans’ complexes were subjected to molecular dynamic (MD) simulations for a period of 50 ns. To measure the stability, flexibility, and average distance between the SARS-CoV-2 Mpro and flavolignans, root mean square deviations (RMSD) and root mean square fluctuation (RMSF) were calculated, and the binding free energy calculations of SARS-CoV-2 Mpro-flavolignans complexes were found to using the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) method. SwissADME web tools were used to evaluate ADME properties and pharmacokinetic parameters of the flavolignans. Results: The binding energies of the SARS-CoV-2 Mpro- flavolignans’ complexes were identified from the molecular docking of SARS-CoV-2 Mpro. Sinaiticin was found to be the highest binding affinity of -9.4 kcal/mol and formed π-lone pair and pi-alkyl interactions with the catalytic binding residues Glu166 and Cys145. Silychristin, Dehydrosilybin, Hydrocarpin, Silydianin, and 5’- metoxyhydcaprin also showed high binding affinities of -9.3, -9.2, -9.0, -8.7 and -8.6 kcal/mol, respectively. The flavolignans demonstrated strong Carbon H bond interactions with the binding site residues of the Gln192, Gly143, Leu27, Glu166, and Tyr54, and thereby can act as potent inhibitors of the SARS-CoV 2 Mpro. Conclusion: The selected flavolignans obey Lipinski’s rule of five. According to the results obtained from molecular docking studies, molecular dynamic simulations, and ADME analysis, it can be proposed that the flavolignans, which can be used to design effective antiviral drug candidates against the SARS-CoV-2, can be tried for promising and effective inhibitors of the SARS-CoV-2 main protease in vitro and in vivo studies.

Background: The screening of effective ingredients is the bridge between the research of efficacy and the mechanism of traditional Chinese medicine. Although promising virtual screening has emerged as an attractive alternative, an ideal strategy is still urgently required due to the characteristics of multi-ingredients and multi-targets of traditional Chinese medicine. Objective: The aim of the study was to develop a methodological verification-based novel screening strategy capable of comprehensively assessing the ability of compounds to perturb disease networks, thereby identifying representative ingredients of traditional Chinese medicine interventions in complex diseases. Methods: In this article, we take astragalus interfering with cervical cancer as an example. First, a multifunctional clustering disease network model was constructed; second, the several drugs and their decoys were used for molecular docking with disease network clusters for methodological verification and determining the best scoring criteria. Third, the representative ingredients of astragalus were screened according to the best scoring criteria. Finally, the effects of the representative ingredients on cervical cancer SiHa cells were evaluated by CCK-8 assay, flow cytometry, and western blot analysis. Results: Three representative ingredients of astragalus were betulinic acid, hederagenin and methylnissolin, which perturbed the apoptosis, stabilization of p53, and G1/S transition cluster as a whole, respectively. CCK-8 assay showed that the IC50 value of betulinic acid, hederagenin and methylnissolin at 48 h was 28.84, 101.90, and 187.40 μM, respectively. Flow cytometry showed that these three representative ingredients could significantly induce early apoptosis and cell cycle arrest. Western blot analysis showed that betulinic acid treatment significantly increased p53 expression, while hederagenin and methylnissolin did not. Conclusion: This study has provided new ideas for the screening of effective ingredients in traditional Chinese medicine, and established a foundation for elucidating the overall mechanism of action of traditional Chinese medicine.

Background: Cyclin-dependent Kinase 9 as one of the serine/threonine protein kinases has become an important target for the treatment of cancer especially driven by transcriptional dysregulation. Objective: This thesis was conducted to elucidate the structure-activity relationship and interaction mode of coumarin compounds acting on CDK9. Methods: Three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulation were conducted to reveal the structural requirements for bioactivities. The 3D-QSAR model was constructed to find the features required for different substituents on the coumarin scaffold. Molecular docking and molecular dynamics simulation were employed to generate the binding mode and stability of CDK9. Results: The Q2 and R2 values of the CoMFA model were calculated as 0.52 and 0.999, while those for the CoMSIA model were 0.606 and 0.998. It is believed that the significant statistical parameters of CoMFA and CoMSIA models revealed high activity-descriptor relationship efficiency. Therefore, we considered the 3D-QSAR model to be robust and accurate. The contour maps provided a deep structure-activity relationship and valuable clues for rational modification. Based on the contour maps, 4 novel CDK9 inhibitors which were predicted to have satisfactory pharmacokinetic characteristics were designed and exhibited better-predicted activities. Subsequently, molecular docking was employed to generate the binding mode of CDK9. Furthermore, 50 ns MD simulation was of great help in verifying the accuracy of docking results and the stability of the complexes. Conclusion: The study is a valuable insight for further research on novel and effective inhibitors targeting CDK9.

Background: Carbonic anhydrases (CAs, EC 4.2.1.1) are metalloenzymes that contain zinc ions on the active side and convert carbon dioxide to bicarbonate in metabolism. Human CA-I and CA-II, which are the most abundant CA isozymes in erythrocytes, have been therapeutic targets in the treatment of glaucoma, hypertension, ulcer, osteoporosis, and, neurological disorders. Benzohydrazides are biologically active compounds, and their various pharmacological effects have been reported. Aim: In light of this, the objective of this study was to investigate the in vitro effects of benzohydrazide derivatives on the activities of hCA-I and hCA-II, determine the compounds as selective inhibitors for these isoenzymes, and estimate the inhibition mechanism through molecular docking studies. Methods: In this work, we synthesized the 10 different derivatives of benzohydrazide containing various functional group of different positions. Results: As a result, all benzohydrazide derivatives inhibited both isozymes in vitro and 2-amino 3- nitro benzohydrazide (10) was found to be the most efficient inhibitor of both hCA isozymes with the IC50 values of 0.030 and 0.047 μM, respectively. In the molecular docking studies, 3-amino 2- methyl benzohydrazide (3) had the lowest estimated free binding energies against hCA isozymes as -6.43 and -6.13 kcal/mol. Conclusion: In this study, hCA-I & II isozymes were isolate from human erythrocytes. CA isozymes are one of these target enzymes. WBC hope that the benzohydrazide derivatives, can guide remedies targeting carbonic anhydrase.

Background: Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor that is primarily treated with surgery. Targeted therapy is a promising approach to help reduce the high rate of recurrence. This study aims to identify the potential target genes and explore the candidate drugs acting on them effectively with computational methods. Methods: Identification of genes associated with DFSP was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-Protein Interaction (PPI) network was constructed by using the Search Tools for the Retrieval of Interacting (STRING) database and visualized in Cytoscape. The gene candidates were identified after a literature review. Drugs targeting these genes were selected from Pharmaprojects. The binding affinity scores of Drug-Target Interaction (DTI) were predicted by a deep learning algorithm Deep Purpose. Results: A total of 121 genes were found to be associated with DFSP by text mining. The top 3 statistically functionally enriched pathways of GO and KEGG analysis included 36 genes, and 18 hub genes were further screened out by constructing a PPI networking and literature retrieval. A total of 42 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 10 drugs with top affinity scores were predicted by DeepPurpose, including 3 platelet-derived growth factor receptor beta kinase (PDGFRB) inhibitors, 2 platelet-derived growth factor receptor alpha kinase (PDGFRA) inhibitors, 2 Erb-B2 receptor tyrosine kinase 2 (ErbB-2) inhibitors, 1 tumor protein p53 (TP53) stimulant, 1 vascular endothelial growth factor receptor (VEGFR) antagonist, and 1 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitor. Conclusion: Text mining and bioinformatics are useful methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the drug candidates.