AbLang

AbLang performs antibody sequence completion by using a specialized language model trained on the Observed Antibody Space (OAS) to restore missing residues in B-cell receptor repertoire sequences.

Key Features:

• Specialized training on antibody data: Trained exclusively on the Observed Antibody Space (OAS) database to capture antibody-specific sequence semantics and structural nuances.
• Restoration of missing residues: Restores missing residues in antibody sequences, addressing the issue that over 40% of sequences in OAS lack the first 15 amino acids.
• Performance superiority: Demonstrates superior residue-restoration performance compared to IMGT germlines and the general protein language model ESM-1b.
• Independence from germline knowledge: Operates without prior knowledge of an antibody's germline.
• Efficiency and speed: Processes sequences substantially faster than ESM-1b (reported as seven times faster).

Scientific Applications:

• Immunological research: Facilitates analysis of B-cell receptor repertoires and studies of immune response mechanisms by providing more complete sequence data.
• Antibody engineering: Assists design and optimization of therapeutic antibodies by restoring and completing antibody sequences for downstream engineering workflows.
• Disease biomarker discovery: Improves identification and validation of antibody biomarkers for diagnostic and prognostic applications through enhanced sequence completeness.

Methodology:

AbLang is a specialized antibody language model trained exclusively on the Observed Antibody Space (OAS) and evaluated via comparative benchmarking against IMGT germlines and the ESM-1b protein language model.