English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate in silico modeling of the antigen processing pathway is crucial toenable personalized epitope vaccine design for cancer. An important step ofsuch pathway is the degradation of the vaccine into smaller peptides by theproteasome, some of which are going to be presented to T cells by the MHCcomplex. While predicting MHC-peptide presentation has received a lot ofattention recently, proteasomal cleavage prediction remains a relativelyunexplored area in light of recent advancesin high-throughput massspectrometry-based MHC ligandomics. Moreover, as such experimental techniquesdo not allow to identify regions that cannot be cleaved, the latest predictorsgenerate decoy negative samples and treat them as true negatives when training,even though some of them could actually be positives. In this work, we thuspresent a new predictor trained with an expanded dataset and the solidtheoretical underpinning of positive-unlabeled learning, achieving a newstate-of-the-art in proteasomal cleavage prediction. The improved predictivecapabilities will in turn enable more precise vaccine development improving theefficacy of epitope-based vaccines. Pretrained models are available on GitHub

Improved proteasomal cleavage prediction with positive-unlabeled learning