arXiv:2512.08613v1 Announce Type: new
Abstract: Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data augmentation technique is used on the CB513 dataset to expand the training samples. The transformer shows strong ability to generalize across variable-length sequences while effectively capturing both local and long-range residue interactions.
Mucin-type O-glycans regulate proteoglycan stability and chondrocyte maturation
O-glycosylation is a ubiquitous post-translational modification essential for protein stability, cell signaling, and tissue organization, yet how distinct O-glycan subclasses coordinate tissue development remains unclear.



