Moreover, attention is paid to ways to analyze such proteins using freely available bioinformatic tools and, more importantly, to bring these proteins alive by looking at them on a computer/laptop screen with the easy-to-use but highly performant and interactive molecular graphics program DeepView. It is hoped that this paper will stimulate non-bioinformaticians and non-specialists in structural biology to scrutinize these and other macromolecules and as such will contribute to establishing procedures to fight these and maybe other forthcoming viruses.The novel coronavirus pneumonia COVID-19 is characterized by all age susceptibility, which imposes a dramatic threat to the human species all over the world. According to current available data, the cytokine storm appears to be the most life-threatening symptom of severe COVID-19 cases accompanied with lung fibrosis. Galectin-3 (Gal-3), a member of soluble β-galactoside-binding lectin families, has been implicated as a key regulator in various inflammation conditions in addition to its well-documented roles in cancer. The pro-inflammatory activity of Gal-3 in the inflammatory response and lung fibrosis of COVID-19 has been proposed by emerging studies, which suggested that inhibition of Gal-3 may represent a novel treatment approach for COVID-19 patients. Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with poor prognosis. ICC accounts for 10-25% of primary liver cancers with limited therapeutic options, which has higher incidence in Asian countries, particularly in China. Cancer patients, ie the ways for further exploring the possibility of Gal-3 as a potential therapeutic target for treating ICC patients or those with COVID-19-related conditions.Platelet-derived growth factor C (PDGF-C) is a member of the PDGF/VEGF (vascular endothelial growth factor) family, which includes proteins that are well known for their mitogenic effects on multiple cell types. Glycosylation is one of the most important forms of posttranslational modification that has a significant impact on secreted and membrane proteins. Glycosylation has many well-characterized roles in facilitating protein processing and contributes to appropriate folding, conformation, distribution, and stability of proteins that are synthesized intracellularly in the endoplasmic reticulum (ER) and Golgi apparatus. Although the general process and functions of glycosylation are well documented, there are most likely others yet to be discovered, as the glycosylation of many potential substrates has not been characterized. In this study, we report that the PDGF-C protein is glycosylated at three sites, including Asn25, Asn55, and Asn254. However, we found that mutations at any of these sites do not affect the protein expression or secretion. Similarly, disruption of PDGF-C glycosylation had no impact on its progression through the ER and Golgi apparatus. However, the introduction of a mutation at Asn254 (N254 A) prevents the activation of full-length PDGF-C and its capacity for signaling via the PDGF receptor. Our findings reveal that glycosylation affects PDGF-C activation rather than the protein synthesis or processing. This study characterizes a crucial modification of the PDGF-C protein, and may shed new light on the process and function of glycosylation.The interaction between two proteins may involve local movements, such as small side-chains re-positioning or more global allosteric movements, such as domain rearrangement. see more We studied how one can build a precise and detailed protein-protein interface using existing protein-protein docking methods, and how it can be possible to enhance the initial structures using molecular dynamics simulations and data-driven human inspection. We present how this strategy was applied to the modeling of RHOA-ARHGEF1 interaction using similar complexes of RHOA bound to other members of the Rho guanine nucleotide exchange factor family for comparative assessment. In parallel, a more crude approach based on structural superimposition and molecular replacement was also assessed. Both models were then successfully refined using molecular dynamics simulations leading to protein structures where the major data from scientific literature could be recovered. We expect that the detailed strategy used in this work will prove useful for other protein-protein interface design. The RHOA-ARHGEF1 interface modeled here will be extremely useful for the design of inhibitors targeting this protein-protein interaction (PPI).The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.