Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications.

ABSTRACT: Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes

SUBMITTER: Chomicz D 

PROVIDER: S-EPMC11008471 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature