Accelerate Antibody Discovery: KA-Search is Now Integrated into Vecura

What is KA-Search?

KA-Search (Known Antibody Search) is a specialized computational tool designed for the rapid and exhaustive sequence-identity searching of antibody variable domains. By leveraging pre-aligned databases—most notably the Observed Antibody Space (OAS) containing over 2.4 billion sequences—it bypasses traditional, computationally expensive pairwise alignment methods. Instead, it utilizes a vectorized positional comparison approach based on a fixed 200-position IMGT grid, allowing for lightning-fast performance without the need for GPUs or neural networks.

It helps users identify the most sequence-identical known antibodies for a given query sequence. It is especially useful for assessing the novelty of candidate antibodies, finding structural templates, and characterizing the immunogenicity landscape of therapeutic leads.

What can users do with KA-Search on Vecura?

With KA-Search on Vecura, users can:

• Perform high-speed sequence-identity searches against massive antibody datasets directly through an intuitive interface.
• Filter search results by species, chain type, or specific variable-domain regions such as the full domain, all CDRs, or just the CDR3 loop.
• Retrieve comprehensive metadata for top hits, including V/J-gene germline assignments and OAS provenance.
• Easily integrate identity scores and aligned sequences into downstream antibody engineering and discovery workflows.

What the output means

The output provides a list of top-N antibody hits ranked by their sequence identity to your query, complete with detailed AIRR-formatted metadata and OAS study information. The provided identity score acts as a quantitative measure of similarity within your chosen region of interest (whole domain, CDRs, or CDR3).

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

Why this matters

The ability to quickly and accurately compare new antibody sequences against billions of known entries is a cornerstone of modern antibody discovery. By enabling rapid homology surveys and template identification, researchers can more efficiently narrow down candidates for structure prediction and therapeutic development, significantly accelerating the pipeline from sequence to clinical candidate.

By providing this tool, Vecura removes the technical barriers associated with managing massive datasets and complex alignment pipelines, democratizing access to high-performance computational immunology.

• Developed by: The Oxford Protein Informatics Group (OXPIG)
• Source: Official GitHub Repository
• Reference: Scientific Reports (2023) - DOI: 10.1038/s41598-023-38108-7