Leverage Antibody-Specific Insights with AbLang on Vecura
This update enables antibody researchers and protein engineers to streamline sequence analysis and design through a guided, no-code workflow on Vecura, eliminating the need to manage complex local model infrastructure.
What is AbLang?
AbLang is an antibody-specific protein language model trained on over a billion sequences from the Observed Antibody Space (OAS) database. Unlike general protein models, it is specifically fine-tuned on the variable regions of antibodies to accurately model their unique sequence patterns. It serves as a powerful tool for antibody engineering, helping researchers fill in missing data, understand residue naturalness, and generate numerical representations for downstream analysis.
What can users do with AbLang on Vecura?
With AbLang on Vecura, users can:
- Restore Missing Residues: Automatically fill in gaps (
*) in antibody sequencing data to create complete sequences ready for further modeling. - Generate Sequence Embeddings: Transform antibody variable-region sequences into fixed-size 768-dimensional vectors for tasks like clustering, similarity searches, or as inputs for machine learning models.
- Score Residue Likelihoods: Obtain probability scores for every amino acid at every position, enabling the identification of stable, natural mutations.
- Support Antibody Design: Streamline the analysis of antibody variable regions by leveraging a model that understands the specific constraints of heavy and light chain structures.
What the output means
The outputs consist of restored sequence data, per-residue and per-sequence numerical embeddings, and position-specific amino acid likelihoods. These results provide predictive insights into the structural and functional properties of antibodies.
This output should be used to support scientific decision making. It does not replace experimental validation.
Why this matters
The design of therapeutic antibodies relies on understanding the "naturalness" and structural viability of amino acid sequences within the variable regions. By focusing exclusively on antibody-specific data, AbLang provides a higher level of accuracy than general-purpose protein models, allowing for more reliable sequence curation and better-informed experimental design.
- Developed by: The Oxford Protein Informatics Group (OXHPI)
- Source: AbLang GitHub Repository
- Reference: AbLang paper (Bioinformatics Advances, 2022)
Try AbLang on Vecura.
Open the model workspace and start evaluating it with your own inputs.