Khalid Raza

Probing the role of Coniferin and Tetrahydrocurcumin from Traditional Chinese medicine against PSAT1 in early-stage ovarian cancer: An in silico study

Ovarian cancer, a formidable gynaecological malignancy, poses a significant global health challenge, and it is characterised by late-stage diagnosis and a high mortality rate. Even in its early stages, when treatment choices are scarce, ovarian cancer is still a complicated cancer to treat. In this work, we used computational approaches to find putative Traditional Chinese Medicine (TCM) inhibitors that target Phosphoserine Aminotransferase 1 (PSAT1), a crucial enzyme linked to the development of early-stage ovarian cancer. Using a methodical screening approach, we chose a panel of TCM compounds and prepared them, expected to interact with PSAT1. We next evaluated the binding affinities using molecular docking, which helped to identify Coniferin and Tetrahydrocurcumin compounds as potent inhibitors with the docking score of -8.8 kcal/mol and -8.9 Kcal/mol, respectively, and compared to the native ligand. The inhibitory effects of Coniferin and Tetrahydrocurcumin compounds were evaluated with the Pharmacokinetic studies and compared with the standard values, which resulted in an utter performance against each descriptor of the QikProp and performed the Molecular Interaction Fingerprints that resulted in the most interaction residues with counts were 4GLY, 4ASN, 4HIE, 4SER, 4THR, 3ARG and many more. Further, 100ns MD Simulation was performed in neutralised water, resulting in entirely stable deviations, fluctuations, and many intermolecular interactions, and the MM\GBSA studies on all 1000 trajectories have supported the complex’s stability. The computational studies have shown a completely stable performance that supports Coniferin, and Tetrahydrocurcumin can be a potent inhibitor of PSAT1. However, further experimental works are needed to confirm this study.

In silico approach to understand epigenetics of POTEE in ovarian cancer

Abstract Ovarian cancer is the third leading cause of cancer-related deaths in India. Epigenetics mechanisms seemingly plays an important role in ovarian cancer. This paper highlights the crucial epigenetic changes that occur in POTEE that get hypomethylated in ovarian cancer. We utilized the POTEE paralog mRNA sequence to identify major motifs and also performed its enrichment analysis. We identified 6 motifs of varying lengths, out of which only three motifs, including CTTCCAGCAGATGTGGATCA, GGAACTGCC, and CGCCACATGCAGGC were most likely to be present in the nucleotide sequence of POTEE. By enrichment and occurrences identification analyses, we rectified the best match motif as CTTCCAGCAGATGT. Since there is no experimentally verified structure of POTEE paralog, thus, we predicted the POTEE structure using an automated workflow for template-based modeling using the power of a deep neural network. Additionally, to validate our predicted model we used AlphaFold predicted POTEE structure and observed that the residual stretch starting from 237-958 had a very high confidence per residue. Furthermore, POTEE predicted model stability was evaluated using replica exchange molecular dynamic simulation for 50 ns. Our network-based epigenetic analysis discerns only 10 highly significant, direct, and physical associators of POTEE. Our finding aims to provide new insights about the POTEE paralog.

Phytochemicals from Ayurvedic plants as potential medicaments for ovarian cancer: an in silico analysis

Ovarian cancer is one of the highly prominent gynecological malignancies after breast cancer. Although myriad literature is available, there is no specific biomarker available for the personalized treatment strategy. The unavailability of effective drug therapy for ovarian cancer calls for an urgent push in its development from the multidisciplinary scientific community. Indian Ayurvedic medicine pharmacology is widely appreciated and accepted for its immense healthcare benefits. Bioinformatics and cheminformatics approaches can be effectively used to screen phytochemicals present in the Indian Ayurvedic plants against ovarian cancer target receptors. Recent studies discern that POTE, a cancer-testis antigen (CTA) family, plays a crucial role in the proliferation and progression of cancers including ovarian cancer. Specifically, POTEE paralog has been observed to be hypermethylated in ovarian cancer. This study undertakes an in silico analysis of Indian Ayurvedic plants for their anticancer efficacy against ovarian cancer proliferation target receptor POTEE. Structures of 100 phytochemicals from 11 Ayurvedic plants were screened with ADME criteria, and qualified phytochemicals were subjected to molecular docking and interaction analysis. Only 6 phytochemicals having a high affinity to the target receptor (POTEE) were then subjected to an all-atom replica exchange molecular dynamics simulation for 50 ns. Binding affinities of 6 phytochemicals cedeodarin, deodarin, hematoxylin, matairesinol, quercetin, and taxifolin with POTEE were -8.1, -7.7, -7.7, -7.9, -8.0, and - 7.7 kcal/mol, respectively, and their RMSD were recorded as zero. This study concludes that phytochemicals present in Indian Ayurvedic plants namely Cedrus deodara and Asparagus racemosus possess inhibitory effects against ovarian cancer proliferation receptor POTEE.

In-silico analysis reveals Quinic acid as a multitargeted inhibitor against Cervical Cancer

The cervix is the lowermost part of the uterus that connects to the vagina, and cervical cancer is a malignant cervix tumour. One of this cancer's most important risk factors is HPV infection. In the approach to finding an effective treatment for this disease, various works have been done around genomics and drug discovery. Finding the major altered genes was one of the most significant studies completed in the field of cervical cancer by TCGA (The Cancer Genome Atlas), and these genes are TGFBR2, MED1, ERBB3, CASP8, and HLA-A. The greatest genomic alterations were found in the PI3K/MAPK and TGF-Beta signalling pathways, suggesting that numerous therapeutic targets may come from these pathways in the future. We, therefore, conducted a combined enrichment analysis of genes gathered from various works of literature for this study. The final six key genes from the list were obtained after enrichment analysis using GO, KEGG, and Reactome methods. The six proteins against the identified genes were then subjected to a docking-based screening against a library of 6,87,843 prepared natural compounds from the ZINC15 database. The most stable compound was subsequently discovered through virtual screening to be the natural substance Quinic acid, which also had the highest binding affinity for all six proteins and a better docking score. To examine their stability, the study was extended to MM/GBSA and MD simulations on the six docked proteins, and comparative docking-based calculations led us to identify the Quinic Acid as a multitargeted compound. The overall deviation of the compound was less than 2 Å for all the complexes considered best for the biological molecules, and the simulation interaction analysis reveals a huge web of interaction during the simulation.Communicated by Ramaswamy H. Sarma.

FDA-approved Levophed as an alternative multitargeted therapeutic against cervical cancer transferase, cell cycle, and regulatory proteins

Despite the availability of Pap tests and HPV vaccines, Cervical Cancer continues to be a significant factor contributing to women's deaths. It poses severe consequences to women's health. The disease's severity lies in its potential to progress silently in its early stages, mainly detected in its advanced stage, and clinical treatment is challenging due to drug resistance. This study aims to identify multitargeted lead molecules based on the interactome of Cervical Cancer-related crucial genes, which can help develop drug-resistant therapies. We have considered 9 crucial Cervical Cancer genes, namely BUBR1, CCNB1, FEN1, MAD2, MCM10, MCM6, ITGB8, POLE, and TPX2, to perform gene network analysis and Gene Ontology enrichment studies to identify the potential hub genes and their role. Further, we performed multitarget screening using multisampling algorithms HTVS, SP, and XP to screen the protein products of the 9 genes for their binding affinity for the FDA-approved drugs library. The binding affinities of the compounds were evaluated using MM\GBSA that identified multitargeted potential inhibitor as a Levophed for Cervical Cancer, and the docking results showed a range of MM/GBSA scores, varying from -8.35 to -5.38 kcal/mol for docking, and -43.41 to -19.37 kcal/mol for MM/GBSA scoring. The protein residues that interact the most with Levophed are ALA, THR, ILE, ASN, GLY, ASP, LEU, LYS, VAL, GLN, PRO, CYS, GLU, and TYR. The pharmacokinetic properties and WaterMap computations also support the idea that the compound can potentially become a drug candidate. Furthermore, all 9 complexes were simulated for 100ns, resulting in cumulative deviation and fluctuation of <2 Å, with many intermolecular interactions and binding free energy computations supporting the studies. The study shows that Levophed could treat Cervical Cancer without encountering drug resistance- however, experimental studies are needed to confirm the accuracy.