Recombinant production of mussel foot proteins among marine-inspired proteinaceous adhesive materials has been attracted high attention for medical applications, due to their exceptional versatility potential of hierarchically arranged nanostructures. Various biochemical and proteinous factors such as amyloid CsgA curli protein have been used as a synergistic factor to enhance the constancy of obtained bio-adhesion but their mechanistic interactions have not yet been deeply investigated widely in different pH conditions. To this end, the present study has first sought to assess molecular simulation and prediction by using RosettaFold to predict the 3-dimensional structure of the fused CsgA subunit and the MFP3 protein followed by in vitro verification. It was developed an ensemble of quantitative structure-activity relationship models relying on simulations according to the surface area and molecular weight values of the fused proteins in acidic to basic situations using PlayMolecule (protein preparation app for MD simulations) online databases followed by molecular dynamic simulation at different pHs. It was found that acidic conditions positively affect adhesive strength throughout the chimeric structure based on comparative structure-based analyses along with those obtained in prevailing literature. Atomic force microscopy analysis was confirmed obtained in silico data which showed enhanced adhesive properties of fused protein after self-assembly in low pH conditions. In conclusion, the augmented model for reactivity predictions not only unravels the performance and explain ability of the adhesive proteins but in turn paves the way for the decision-making process for chimeric subunits modifications needed for future industrial production.

Ovarian cancer (OC) incidence and mortality rates are estimated to increase globally in the years to come. Early diagnosis of OC is still one of the most significant challenges facing researchers due to extensive metastases and the lack of recognition of biomarkers in advanced stages of high-grade primary tumors (HGPTs). In this study, OC cell lines were analyzed using bioinformatics study and gene expression screening, utilizing Gene Expression Omnibus (GEO) the microarray dataset with 53 HGPTs and 10 normal samples were analyzed by using GEO2r to figure out differently-expressed genes (DEGs). Subsequently, Gene set enrichment analysis (GSEA), ChIP enrichment analysis (ChEA), eXpression2Kinases (X2K), and Human Protein Atlas (HPA) were used to analyze signaling pathways, transcription factors (TFs), kinases, and proteome analysis, respectively. Protein-protein interaction (PPI) networks were made by using STRING and Cytoscape Software. Co-expression and hub genes were determined via STRING database and cytoHubba plug-in, and DEGs were confirmed by using gene expression profiling interactive analysis (GEPIA). KIAA0101, RAD51AP1, FAM83D, CEP55, PRC1, CKS2, CDCA5, NUSAP1, ECT2 and TRIP13 were found to be the top 10 hub genes. At the same time, SIN3A, VDR, TCF7L2, NFYA, and FOXM1 were identified as the top 5 TFs in HGPT. Ultimately, the clustering of hub genes was carried out using the available tools in the protein atlas server, based on which 5 out of 10 candidate genes, including CEP55, PRC1, CKS2, CDCA5 and NUSAP1, were chosen as markers. miRNAs analysis showed that hsa-miR-215-5p, hsa-miR-193b-3p, hsa-miR-192-5p and could target the largest number of HGPT genes. Collectively, HGPT-related genes, especially hub genes, TFs, miRNAs and pathways seem to have a great potential as novel biomarkers for HGPT as well as diagnosis and treatment strategies in OC.