期刊
MABS
卷 14, 期 1, 页码 -出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/19420862.2021.2020203
关键词
Antibody humanization; humanness; human-likeness; immunogenicity; deimmunization; immune repertoires; machine learning; deep learning
资金
- Merck Sharp Dohme Corp.
- University of Chemistry and Technology via the Ministry of Education, Youth and Sports of the Czech Republic [LM2018130]
- [RVO 68378050-KAV-NPUI]
Despite manual and laborious traditional humanization methods, automated methods are advancing. The BioPhi platform offers novel approaches for humanization and evaluation, providing more efficient design and discovery of therapeutic antibodies.
Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert knowledge. Although automation efforts are advancing, existing methods are either demonstrated on a small scale or are entirely proprietary. To predict the immunogenicity risk, the human-likeness of sequences can be evaluated using existing humanness scores, but these lack diversity, granularity or interpretability. Meanwhile, immune repertoire sequencing has generated rich antibody libraries such as the Observed Antibody Space (OAS) that offer augmented diversity not yet exploited for antibody engineering. Here we present BioPhi, an open-source platform featuring novel methods for humanization (Sapiens) and humanness evaluation (OASis). Sapiens is a deep learning humanization method trained on the OAS using language modeling. Based on an in silico humanization benchmark of 177 antibodies, Sapiens produced sequences at scale while achieving results comparable to that of human experts. OASis is a granular, interpretable and diverse humanness score based on 9-mer peptide search in the OAS. OASis separated human and non-human sequences with high accuracy, and correlated with clinical immunogenicity. BioPhi thus offers an antibody design interface with automated methods that capture the richness of natural antibody repertoires to produce therapeutics with desired properties and accelerate antibody discovery campaigns. The BioPhi platform is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.
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