Rational design of humanized antibody inhibitors for cathepsin S.

Cathepsin S (CTSS) is involved in pathogenesis of many human diseases. Inhibitors blocking its protease activity hold therapeutic potential. In comparison to small-molecule inhibitors, monoclonal antibodies capable of inhibiting CTSS enzymatic activity may possess advantageous pharmacological properties. Here we designed and produced inhibitory antibodies targeting human CTSS by genetically fusing the propeptide of procathepsin S (proCTSS) with antibodies in clinic. The resulting antibody fusions in full-length or fragment antigen-binding format could be stably expressed and potently inhibit CTSS proteolytic activity in high specificity. These fusion antibodies not only demonstrate a new approach for facile synthesis of antibody inhibitors against CTSS, but also represent novel anti-CTSS therapeutic candidates.

pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research.

T cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical researchers with limited computational backgrounds face numerous obstacles to properly and efficiently utilizing bioinformatics tools for analyzing TCR-Seq data. Here we report pyTCR, a computational notebook-based solution for comprehensive and scalable TCR-Seq data analysis. Computational notebooks, which combine code, calculations, and visualization, are able to provide users with a high level of flexibility and transparency for the analysis. Additionally, computational notebooks are demonstrated to be user-friendly and suitable for researchers with limited computational skills. Our tool has a rich set of functionalities including various TCR metrics, statistical analysis, and customizable visualizations. The application of pyTCR on large and diverse TCR-Seq datasets will enable the effective analysis of large-scale TCR-Seq data with flexibility, and eventually facilitate new discoveries.