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.

Background: Due to the inherently unstable nature of HCV, various genotypes have been identified. Steatosis is a histological feature in the progression of HCV-associated liver disease and has been shown to alter the host lipid metabolism. Objective: Assess the distribution of HCV genotypes in the two provinces of Pakistan, and determine the association of hepatic steatosis with altered clinical and virological factors in chronic HCV patients. Methods: One hundred twenty six chronic HCV patients (steatosis in 49 patients) were enrolled for qualitative analysis by PCR. Out of 126 ELISA and PCR positive samples, 119 (48 with hepatic steatosis) chronic HCV patients (mean age 42.0±13.3 years, mean body mass index (BMI) 24.2±4.1) were proved positive after PCR-based detection. Biochemical and virological factors such as HCV genotype, or glucose, in 119 CHC patients were determined and compared between patients with and without hepatic steatosis. Results: Out of 126 samples, 119 were HCV positive, where 58 (48.7%) were genotype 3a, 24 (20.2%) were 3b, 12 (10.1%) were 1a, eight (6.7%) were 2a, six (5.0%) were 1b, and one (0.8%) was 4. Furthermore, seven (5.9%) had a co-infection and three (2.5%) were untypable. BMI (p=0.004), genotype 3a (p<0.001), and triglycerides (p=0.002) were significantly associated with steatosis. It is noteworthy that cholesterol (p=0.281), glucose (p=0.305), lowdensity lipoprotein (p=0.101), high-density lipoprotein (p=0.129), alanine amino transferase (p=0.099), aspartate transaminase (p=0.177), bilirubin (p= 0.882), and age (p=0.846) showed non-significant association. Conclusion: Genotype 3a is the predominant genotype in Pakistan. Hepatic steatosis is quite frequent feature in HCV patients and strongly correlates with BMI, genotype 3a, and triglyceride contents in patients infected with HCV.

Novel protein kinase C (nPKC) family member, Protein kinase C epsilon (PKCε) is an AKC kinase superfamily member. It is associated with neurological and metabolic diseases as well as human cancer. No study so far has been conducted to identify genetic variations and their effect on PKCε folding and functioning. The present study aimed to identify mutational hotspots in PKCε and disease-causing non-synonymous variants (nsSNPs) along with the investigation of nsSNPs impact on protein dynamics. Twenty-nine in silico tools were applied to determine nsSNPs’ deleteriousness, their impact on protein dynamics and disease association, along with the prediction of PKCε post-translational modification (PTM) sites. The present study’s outcomes indicated that most nsSNPs were concentrated in the PKCε hinge region and C-terminal tail. Most pathogenic variants mapped on the kinase domain. Regulatory domain variants influenced PKCε interaction with molecular players, whereas kinase domain variants were predicted to impact its phosphorylation pattern and protein–protein interactions. Most PTM sites were mapped on the hinge region. PKCε nsSNPs have an association with oncogenicity and its expression dysregulation is responsible for poor overall survival. Understanding nsSNPs structural impact is a primary step necessary for delineating the relationship of genetic level differences with protein phenotype. The obtained knowledge can eventually help in disease diagnosis and therapy design.

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.

Hypersensitivity of the immune system is caused by elevated immunoglobulin E (IgE) levels in the serum, in response to a discrete allergen leading to allergic reactions. IgE-mediated inflammation is regulated by the cascade of defense related signaling molecules including interleukin-6 (IL-6) that plays pivotal role in the survival and maturation of mast cells during an allergic reaction. IL-6 mediated defense responses are tightly regulated by Suppressor of Cytokine Signaling 3 (SOCS3), an inhibitory molecules of Janus Kinase-Signal Transducers and Activators of Transcription (JAK-STAT) signaling, in a negative feedback mechanism. The given study focuses on the assessment of crosstalk between SOCS3 and IL-6 to unravel the molecular significance of SOCS3 and IL-6 in the diagnosis and prognosis of allergy. The expression study of SOCS3 through real-time PCR analysis revealed, a 5.9 mean fold increase in SOCS3 expression in atopic cases in comparison to control cases. Moreover, IL-6 has, also, been found significantly enhanced in the serum level of atopic cases (26.4 pg/ml) as compared to control cases (3.686 pg/ml). Female population was found to be at a higher risk to develop atopic condition than male population as females exhibited higher expression of both SOCS3 and IL-6 than males. Furthermore, the polymorphic study of IL-6 promoter region (IL-6 174-G/C) in atopic population has reasserted the importance of SOCS3 and IL-6 in the diagnosis and prognosis of allergy. Expression of SOCS3 and IL-6 serum levels were found to be highly correlated. Therefore establishing the role of IL-6 (-174-G/C) polymorphism on the expression of SOCS3 and IL-6 in atopic cases. Notably, the study established SOCS3 and IL-6 as potential targets for the diagnosis/prognosis of allergy and for the development of reliable therapeutic strategies to control atopic conditions in the near future.