Mouse S100G protein exhibits properties characteristic of a calcium sensor

D-SCRIPT translates genome to phenome with sequence-based, structure-aware, genome-scale predictions of protein-protein interactions

We combine advances in neural language modeling and structurally motivated design to develop D-SCRIPT, an interpretable and generalizable deep-learning model, which predicts interaction between two proteins using only their sequence and maintains high accuracy with limited training data and across species. We show that a D-SCRIPT model trained on 38,345 human PPIs enables significantly improved functional characterization of fly proteins compared with the state-of-the-art approach. Evaluating the same D-SCRIPT model on protein complexes with known 3D structure, we find that the inter-protein contact map output by D-SCRIPT has significant overlap with the ground truth. We apply D-SCRIPT to screen for PPIs in cow (Bos taurus) at a genome-wide scale and focusing on rumen physiology, identify functional gene modules related to metabolism and immune response. The predicted interactions can then be leveraged for function prediction at scale, addressing the genome-to-phenome challenge, especially in species where little data are available.

S100 family members: potential therapeutic target in patients with hepatocellular carcinoma: A STROBE study

Proteins in S100 family exhibit different expressions patterns and perform different cytological functions, playing substantial roles in certain cancers, carcinogenesis, and disease progression. However, the expression and role of S100 family members in the prognosis of hepatocellular carcinoma (HCC) remains unclear. To investigate the effect of S100 family members for the prognosis of liver cancer, we assessed overall survival (OS) using a Kaplan-Meier plotter (KM plotter) in liver cancer patients with different situation. Our results showed that 15 members of the S100 family exhibited high levels of expression and these levels were correlated with OS in liver cancer patients. The higher expression of S100A5, S100A7, S100A7A, S100A12, S100Z, and S100G was reflected with better survival in liver cancer patients. However, worse prognosis was related to higher levels of expression of S100A2, S100A6, S100A8, S100A9, S100A10, S100A11, S10013, S100A14, and S100P. We then evaluated the prognostic values of S100 family members expression for evaluating different stages of AJCC-T, vascular invasion, alcohol consumption, and the presence of hepatitis virus in liver cancer patients. Lastly, we studied the prognostic values of S100 family members expression for patients after sorafenib treatment. In conclusion, our findings show that the proteins of S100 family members exhibit differential expression and may be useful as targets for liver cancer, facilitating novel diagnostic and therapeutic strategies in cancer.