kasearch 0.0.1

Optimized tool for antibody sequence search

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KA-Search: Rapid and exact sequence identity search of known antibodies

In antibody therapeutic drug discovery, finding antibodies with similar sequence, or complementary determining regions (CDR) as an antibody of interest is a convenient method for understanding ones drug. A simple and often used approach for finding similar sequences is using sequence identity, either for the whole sequence or CDRs. However, with the current and ever-growing size of available antibody sequences, searching against all known antibodies is becoming ever the more difficult. The Observed Antibody Space (OAS) database currently contains 1.7 billion antibody sequences. A tool optimized for this specific task is therefore relevant for speeding up this process and making it feasible.

Here, we introduce Known Antibody Search (KA-Search), a tool for rapid search of similar whole chain, CDRs and CDR3 antibodies in the OAS database. We demonstrate how with a prepared target dataset, KA-Search can be used to find the N most similar antibodies from a dataset of 1.7 billion antibodies within 1.5 hour on a _ CPU. Further, for convenience, a user can create their own database to search against, utilizing the same functionality and speed on in-house data.

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Install KA-Search

KA-Search is freely available and can be installed with pip.

    pip install kasearch

or directly from github.

    pip install -U git+

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Searching with KA-Search

A Jupyter notebook showcasing KA-Search can be found here.

First, your sequence(s) needs to be transformed into the correct alignment.

from kasearch import SetQueries

raw_queries = [
    'QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS',
    'EVQLQQSGTVLARPGASVKMSCEASGYTFTNYWMHWVKQRPGQGLEWIGAIYPGNSDTSYIQKFKGKAKLTAVTSTTSVYMELSSLTNEDSAVYYCTLYDGYYVFAYWGQGTLVTVSA',
    'QVQLLESGGGLVQPGGSLRLSCAASGFTFSTAAMSWVRQAPGKGLEWVSGISGSGSSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARELSYLYSGYYFDYWGQGTLVTVSS',
]

QueryDB = SetQueries(raw_queries)
QueryDB.queries[0]

Acheiving an output that looks like this:

Query(aligned_query=array([81, 86, 75,  0, 76, 81, 69, 83, 71, 65,  0, 69, 76, 65, 82, 80, 71,
       65, 83, 86, 75, 76, 83, 67, 75, 65, 83, 71, 89, 84, 70,  0,  0,  0,
        0,  0,  0,  0,  0,  0, 84, 78, 89, 87, 77, 81,  0, 87, 86, 75, 81,
        0, 82,  0, 80,  0, 71,  0, 81,  0,  0, 71,  0, 76, 68,  0, 87, 73,
       71, 65, 73, 89, 80, 71,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
       68, 71, 78, 84, 82, 89,  0,  0, 84,  0,  0, 72,  0,  0, 75, 70,  0,
        0, 75,  0,  0,  0, 71, 75, 65, 84, 76, 84, 65,  0, 68,  0,  0,  0,
       75,  0, 83,  0,  0, 83, 83,  0,  0,  0,  0, 84,  0, 65, 89, 77, 81,
       76, 83, 83, 76, 65, 83,  0, 69, 68, 83, 71, 86, 89, 89, 67, 65, 82,
       71, 69, 71, 78,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,
        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0, 89, 65, 87, 70, 65,
       89, 87, 71,  0, 81, 71, 84, 84, 86, 84, 86, 83, 83], dtype=int8), chain='Heavy')

You then initiate your search and search against the target database of choice.

search_oas = SearchOAS(database_path=DATABASE_PATH, 
                       allowed_chain='Heavy', 
                       n_jobs=2
                      )
                      
search_oas.search(QueryDB.queries[0], keep_best_n=2)
search_oas.current_best_identities

Returning the following best identities

array([[0.79510413, 0.78571429, 0.83333333],
       [0.79161837, 0.78571429, 0.78409091]])

You can then access the indices and use them to extract the meta data of the N best matches.

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Citation

Work in preparation