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Pretrainable geometric graph neural network for antibody affinity maturation.
Cai, Huiyu; Zhang, Zuobai; Wang, Mingkai; Zhong, Bozitao; Li, Quanxiao; Zhong, Yuxuan; Wu, Yanling; Ying, Tianlei; Tang, Jian.
Afiliación
  • Cai H; BioGeometry, Beijing, China.
  • Zhang Z; Mila-Québec AI Institute, Montréal, QC, Canada.
  • Wang M; Department of Computer Science and Operations Research, Université de Montréal, Montréal, QC, Canada.
  • Zhong B; Mila-Québec AI Institute, Montréal, QC, Canada.
  • Li Q; Department of Computer Science and Operations Research, Université de Montréal, Montréal, QC, Canada.
  • Zhong Y; Shanghai Engineering Research Center for Synthetic Immunology, Fudan University, Shanghai, China.
  • Wu Y; MOE/NHC/CAMS Key Laboratory of Medical Molecular Virology, Shanghai Frontiers Science Center of Pathogenic Microorganisms and Infection, Shanghai Institute of Infectious Disease and Biosecurity, School of Basic Medical Sciences, Fudan University, Shanghai, China.
  • Ying T; Mila-Québec AI Institute, Montréal, QC, Canada.
  • Tang J; Department of Computer Science and Operations Research, Université de Montréal, Montréal, QC, Canada.
Nat Commun ; 15(1): 7785, 2024 Sep 06.
Article en En | MEDLINE | ID: mdl-39242604
ABSTRACT
Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Afinidad de Anticuerpos Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Afinidad de Anticuerpos Límite: Humans Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido