A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets.

ABSTRACT: Despite major successes with inhibitory receptor blockade in cancer, the identification of novel inhibitory receptors as putative drug targets is needed due to lack of durable responses, therapy resistance, and side effects. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies estimated that our genome contains over 1600 ITIM-bearing transmembrane proteins. However, testing and development of these candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. Therefore, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify putative inhibitory receptors. Using transcriptomi

SUBMITTER: Singh A 

PROVIDER: S-EPMC11460946 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature