Niccolò Cardente

Amylo-Pipe integrates mechanistic and kinetic prediction tools for protein and peptide aggregation, offering a web interface for assessing aggregation behavior and supporting protein engineering.

PEPE efficiently extracts multimodal protein embeddings from language models, enabling scalable and memory-efficient generation of large embedding datasets for downstream biological tasks.

The paper develops MS-BCR-DB, a unified BCR repertoire database for MS, identifying disease-associated features and shared clonotypes across patients.

The paper quantitatively maps antigen specificity in immune repertoire embedding spaces, showing distinct regions for different antigen-specific sequences.