干旱区生态环境知识资源中心(Arid): BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

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DOI	10.1080/19420862.2021.2020203
	BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning
	Prihoda, David; Maamary, Jad; Waight, Andrew; Juan, Veronica; Fayadat-Dilman, Laurence; Svozil, Daniel; Bitton, Danny A.
通讯作者	Bitton, DA
来源期刊	MABS
ISSN	1942-0862
EISSN	1942-0870
出版年	2022
卷号	14 期号:1
英文摘要	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.
英文关键词	Antibody humanization humanness human-likeness immunogenicity deimmunization immune repertoires machine learning deep learning
类型	Article
语种	英语
开放获取类型	gold, Green Published, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000752626000001
WOS关键词	VARIABLE DOMAINS ; IMMUNOGLOBULIN ; AFFINITY
WOS类目	Medicine, Research & Experimental
WOS研究方向	Research & Experimental Medicine
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376176
推荐引用方式 GB/T 7714	Prihoda, David,Maamary, Jad,Waight, Andrew,et al. BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning[J],2022,14(1).
APA	Prihoda, David.,Maamary, Jad.,Waight, Andrew.,Juan, Veronica.,Fayadat-Dilman, Laurence.,...&Bitton, Danny A..(2022).BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.MABS,14(1).
MLA	Prihoda, David,et al."BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning".MABS 14.1(2022).