RT Journal Article
SR Electronic
T1 BioPhi: A platform for antibody design, humanization and humanness evaluation based on natural antibody repertoires and deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.08.455394
DO 10.1101/2021.08.08.455394
A1 Prihoda, David
A1 Maamary, Jad
A1 Waight, Andrew
A1 Juan, Veronica
A1 Fayadat-Dilman, Laurence
A1 Svozil, Daniel
A1 Bitton, Danny A.
YR 2021
UL http://biorxiv.org/content/early/2021/08/08/2021.08.08.455394.abstract
AB Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains the primary route 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 database 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. Together, BioPhi 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.BioPhi is accessible at https://biophi.dichlab.org and https://github.com/Merck/BioPhi.Competing Interest StatementThe authors have declared no competing interest.