The prediction of affinity between TCRs and peptides is crucial for the further development of TIL (Tumor-Infiltrating Lymphocytes) immunotherapy. Inspired by the broader research of drug-protein interaction (DPI), we propose an atom-level peptide-TCR interaction (PTI) affinity prediction model APTAnet using natural language processing methods. APTAnet model achieved an average ROC-AUC and PR-AUC of 0.893 and 0.877, respectively, in ten-fold cross-validation on 25,675 pairs of PTI data. Furthermore, experimental results on an independent test set from the McPAS database showed that APTAnet outperformed the current mainstream models. Finally, through the validation on 11 cases of real tumor patient data, we found that the APTAnet model can effectively identify tumor peptides and screen tumor-specific TCRs.
Tumor metastasis, responsible for approximately 90% of cancer-associated mortality, remains poorly understood. Here in this study, we employed a melanoma lung metastasis model to screen for metastasis-related genes. By sequential tail vein injection of mouse melanoma B16F10 cells and the subsequently derived cells from lung metastasis into BALB/c mice, we successfully obtained highly metastatic B16F15 cells after five rounds of in vivo screening. RNA-sequencing analysis of B16F15 and B16F10 cells revealed a number of differentially expressed genes, some of these genes have previously been associated with tumor metastasis while others are novel discoveries. The identification of these metastasis-related genes not only improves our understanding of the metastasis mechanisms, but also provides potential diagnostic biomarkers and therapeutic targets for metastatic melanoma.
G protein-coupled receptors (GPCRs) are a large family of cell membrane proteins that are important targets for drug discovery. Nanobodies, also known as VHH (variable domains of heavy chain-only antibodies, HcAbs) antibodies, are small antibody fragments derived from camelids that have gained significant attention as potential therapeutics for targeting GPCRs due to their advantages over conventional antibodies. However, there are challenges in developing nanobodies targeting GPCRs, among which epitope accessibility is the most significant because the cell membrane partially shields the GPCR surface. We developed a universal protocol for making nanobodies targeting GPCRs using the cell membrane extract of GPCR-overexpressing HEK293 cells as the llama/alpaca immunization antigen. We constructed an immune VHH library and identified nanobodies by phage display bio-panning. The monoclonal nanobodies were recombinantly expressed in Escherichia coli (E. coli) and purified to characterize their binding potency.
Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNAER) family proteins are the engines of most intra-cellular and exocytotic membrane fusion pathways (Jahn and Scheller
Substrate stiffness is a microenvironment with a certain stiffness constructed by the extracellular matrix and adjacent cells, which plays an important role in the growth and development of cells and tissue formation. Studies have indicated that the stiffness of the brain is about 0.1–1 kPa. The physiological and pathological processes of the nervous system are mediated by the substrate stiffness that the neurons suffer. However, how substrate stiffness regulates these processes remains to be studied. Culturing neurons on substrates with different stiffness in vitro is one of the best methods to study the role of stiffness in regulating neuronal development and activity. In this study, by changing the preparation time and the activation time of polyacrylamide gel, we provide an improved method that achieves a low toxic substrate environment for better primary neuron adhesion and development. Hope that this method is convenient for those studying the role of substrate stiffness in neurons.
Optical tweezers have elucidated numerous biological processes, particularly by enabling the precise manipulation and measurement of tension. One question concerns the biological relevance of these experiments and the generalizability of these experiments to wider biological systems. Here, we categorize the applicability of the information garnered from optical tweezers in two distinct categories: the direct relevance of tension in biological systems, and what experiments under tension can tell us about biological systems, while these systems do not reach the same tension as the experiment, still, these artificial experimental systems reveal insights into the operations of biological machines and life processes.
