Unlock Advanced Antibody Analysis with AbLang2 on Vecura

What is AbLang2?

AbLang2 is an antibody-specific paired protein language model trained on millions of heavy-chain and light-chain variable region sequences. Unlike general protein models that often struggle with germline bias, AbLang2 is specifically optimized to predict non-germline residues, which are crucial for antigen binding. By processing paired heavy and light chains jointly, it effectively captures the inter-chain co-evolutionary signals essential for high-fidelity antibody analysis.

What can users do with AbLang2 on Vecura?

With AbLang2 on Vecura, users can:

• Restore masked sequences: Intelligently fill in missing or mutated CDR positions to aid in sequence design.
• Generate rich embeddings: Create per-residue or fixed-length sequence embeddings for downstream tasks like clustering, classification, or regression.
• Score sequence naturalness: Evaluate antibody sequences using unbiased pseudo-log-likelihoods to rank their likelihood of being naturally occurring.
• Analyze mutational landscapes: Use per-position likelihoods and probability distributions to scan for the effects of specific mutations on sequence stability and function.

What the output means

The output provides a comprehensive suite of numerical and sequence-based data, ranging from restored amino acid sequences to high-dimensional embeddings and statistical scores like pseudo-log-likelihood and confidence metrics.

This output should be used to support scientific decision making. It does not replace experimental validation.

Why this matters

In antibody engineering, distinguishing between germline-derived sequences and essential, non-germline antigen-binding residues is a significant challenge. General models often default to germline residues, leading to suboptimal predictions in critical binding regions. AbLang2 addresses this limitation by focusing on the unique paired nature of antibody variable domains, providing researchers with a more accurate lens through which to view antibody naturalness and design.

By enabling this model within an accessible workflow, researchers can streamline the early stages of antibody development, from variant optimization to the classification of high-quality binding candidates.

• Developed by: Oxford Protein Informatics Group (OPIG)
• Source: Official GitHub Repository
• Reference: AbLang2 preprint (bioRxiv, 2024)