In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.

Liver cancer is the second most common cause of cancer-induced deaths worldwide. Liver cirrhosis and cancer are a consequence of the abnormal angio-architecture formation of liver and formation of new blood vessels. This angiogenesis is driven by overexpression of hypoxia-inducible factor 1-alpha (Hif1-α) and vascular endothelial growth factor (VEGF). Apart from this, protein kinase B (Akt) is also impaired in liver cancer. Despite the advancement in conventional treatments, liver cancer remains largely incurable. Nowadays, the use of naturally occurring anticancer agents particularly flavonoids is subject to more attention due to their enhanced physicochemical properties. Therefore, this study underlines the use of a natural anticancer agent taxifolin in the treatment of liver cancer using hepatocellular carcinoma cell line HepG2 and Huh7. The aim of our study is to devise a natural and efficient solution for the disease prevalent in Pakistan. The study involved the assessment of binding of ligand taxifolin using molecular docking. The binding of taxifolin with the proteins (Hif1-α, VEGF and Akt) was calculated by docking using Vina and Chimera. Further evaluation was performed by cell viability assay (MTT 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) Assay), colony formation assay, cell migration assay, DNA ladder assay and flow cytometry. To see whether taxifolin directly affected expression levels, analysis of gene expression of Hif1-α, VEGF and Akt was performed using real-time polymerase chain reaction (qPCR) and western blotting. In silico docking experiments revealed that these proteins showed favorable docking scores with taxifolin. Treatment with taxifolin resulted in the inhibition of the liver cancer growth and migration, and induced apoptosis in HepG2 and Huh7 cell lines at an inhibitory concentration (IC50) value of 0.15 µM and 0.22 µM, respectively. The expression of HIF1-α, VEGF and Akt was significantly reduced in a dose- dependent manner. The inhibitory effect of taxifolin on hepatic cells suggested its chemopreventive and therapeutic potential. The studied compound taxifolin exhibited pronounced pro-apoptotic and hepatoprotective potential. Our study has confirmed the pro-apoptotic potential of taxifolin in liver cancer cell lines and will pave a way to the use of taxifolin as a chemotherapeutic agent after its further validation on the animal models and humans based epidemiological studies.

Breast cancer is the most prevailing disease among women. It actually develops from breast tissue and has heterogeneous and complex nature that constitutes multiple tumor quiddities. These features are associated with different histological forms, distinctive biological characteristics, and clinical patterns. The predisposition of breast cancer has been attributed to a number of genetic factors, associated with the worst outcomes. Unfortunately, their behavior with relevance to clinical significance remained poorly understood. So, there is a need to further explore the nature of the disease at the transcriptome level. The focus of this study was to explore the influence of Krüppel‐like factor 3 (KLF3), tumor protein D52 (TPD52), microRNA 124 (miR‐124), and protein kinase C epsilon (PKCε) expression on breast cancer. Moreover, this study was also aimed at predicting the tertiary structure of KLF3 protein. Expression of genes was analyzed through real‐time PCR using the delta cycle threshold method, and statistical significance was calculated by two‐way ANOVA in Graphpad Prism. For the construction of a 3D model, various bioinformatics software programs, Swiss Model and UCSF Chimera, were employed. The expression of KLF3, miR‐124, and PKCε genes was decreased (fold change: 0.076443, 0.06969, and 0.011597, respectively). However, there was 2‐fold increased expression of TPD52 with p value < 0.001 relative to control. Tertiary structure of KLF3 exhibited 80.72% structure conservation with its template KLF4 and was 95.06% structurally favored by a Ramachandran plot. These genes might be predictors of stage, metastasis, receptor, and treatment status and used as new biomarkers for breast cancer diagnosis. However, extensive investigations at the tissue level and in in vivo are required to further strengthen their role as a potential biomarker for prognosis of breast cancer.

Typhoid fever, caused by Salmonella enterica serovar typhi, presents a substantial global health threat, particularly in regions with limited healthcare infrastructure. The rise of multidrug-resistant strains of S. typhi exacerbates this challenge, severely compromising conventional treatment efficacy due to over activity of efflux pumps. In our study, a comprehensive exploration of two fundamental aspects to combat MDR in S. typhi is carried out; i.e. employing advanced bioinformatics analyses and AlphaFold AI, We successfully identified and characterised a putative homologue, ABC-TPA, reminiscent of the P-glycoprotein (P-gp) known for its role in multidrug resistance in diverse pathogens. This discovery provides a critical foundation for understanding the potential mechanisms driving antibiotic resistance in S. typhi. Furthermore, employing computational methodologies, We meticulously assessed the potential of lignans, specifically Schisandrin A, B, and C, as promising Efflux Pump Inhibitors (EPIs) against the identified P-gp homologue in S. typhi. Noteworthy findings revealed robust binding interactions of Schisandrin A and B with the target protein, indicating substantial inhibitory capabilities. In contrast, Schisandrin C exhibited instability, showing varied effectiveness among the evaluated lignans. Pharmacokinetics and toxicity predictions underscored the favourable attributes of Schisandrin A, including prolonged action duration. Furthermore, high systemic stability and demanished toxicity profile of SA and SB present their therapeutic efficacy against MDR. This comprehensive investigation not only elucidates potential therapeutic strategies against MDR strains of S. typhi but also highlights the relevance of computational approaches in identifying and evaluating promising candidates. These findings lay a robust foundation for future empirical studies to address the formidable challenges antibiotic resistance poses in this clinically significant infectious diseases.

Sepsis is blood poisoning disease that occurs when body shows dysregulated host response to an infection and cause organ failure or tissue damage which may increase the mortality rate in ICU patients. As it becomes major health problem, the hospital cost for treatment of sepsis is increasing every year. Different methods have been developed to monitor sepsis electronically, but it is necessary to predict sepsis as soon as possible before clinical reports or traditional methods, because delayed in treatment can increase the risk of mortality with every single hour. For the early detection of sepsis, specifically in ICU patients, different machine learning models i.e Linear learner, Multilayer perceptron neural networks, Random Forest, lightgbm and Xgboost has trained on the data set proposed by Physio Net/ Computing in Cardiology Challenge in 2019. This study shows that Machine learning algorithms can accurately predict sepsis at the admission time of patient in ICU by using six vitals signs extracted from patient records over the age of 18 years. After comparative analysis of machine learning models, Xgboost model achieved a highest accuracy of 0.98, precision of 0.97, and recall 0.98 under the precision-recall curve on the publicly available data. Early prediction of sepsis can help clinicians to implement supportive treatments and reduce the mortality rate as well as healthcare expenses.

Background. Breast cancer is the second leading cause of cancer-related deaths globally, and its prevalence rates are increasing daily. In the past, studies predicting therapeutic drug targets for cancer therapy focused on the assumption that one gene is responsible for producing one protein. Therefore, there is always an immense need to find promising and novel anti-cancer drug targets. Furthermore, proteases have an integral role in cell proliferation and growth because the proteolysis mechanism is an irreversible process that aids in regulating cellular growth during tumorigenesis. Therefore, an inactive rhomboid protease known as iRhom2 encoded by the gene RHBDF2 can be considered an important target for cancer treatment. Speculatively, previous studies on gene expression analysis of RHBDF2 showed heterogenous behaviour during tumorigenesis. Consistent with this, several studies have reported the antagonistic role of iRhom2 in tumorigenesis, i.e., either they are involved in negative regulation of EGFR ligands via the ERAD pathway or positively regulate EGFR ligands via the EGFR signalling pathway. Additionally, different opinions suggest iRhom2 mediated cleavage of EGFR ligands takes place TACE dependently or TACE independently. However, reconciling these seemingly opposing roles is still unclear and might be attributed to more than one transcript isoform of iRhom2. Methods. To observe the differences at isoform resolution, the current strategy identified isoform switching in RHBDF2 via differential transcript usage using RNA-seq data during breast cancer initiation and progression. Furthermore, interacting partners were found via correlation and enriched to explain their antagonistic role. Results. Isoform switching was observed at DCIS, grade 2 and grade 3, from canonical to the cub isoform. Neither EGFR nor ERAD was found enriched. However, pathways leading to TACE-dependent EGFR signalling pathways were more observant, specifically MAPK signalling pathways, GPCR signalling pathways, and toll-like receptor pathways. Nevertheless, it was noteworthy that during CTCs, the cub isoform switches back to the canonical isoform, and the proteasomal degradation pathway and cytoplasmic ribosomal protein pathways were significantly enriched. Therefore, it could be inferred that cub isoform functions during cancer initiation in EGFR signalling. In contrast, during metastasis, where invasion is the primary task, the isoform switches back to the canonical isoform.