Computational Modeling To Adapt Neutralizing Antibody [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 574.5 [Unassigned]
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. National Nuclear Security Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2020
• Mô tả vật lý: Size: 14 p. : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 261780
• Chủ đề: 1. 60 applied life sciences; 2. 97 mathematics and computing

Tóm tắt

Monoclonal antibodies (mAbs) is the leading therapy for viral infections because it provides immediate protection and can be administered at higher levels than in a natural immune response. Finding mAbs that neutralize a broad spectrum of viral targets has proven difficult because many species and strains exist and blanket targeting is a slow and laborious process to experimentally screen 108 variants. A new method is needed to rapidly redesign mAbs for homologous targets. This project speeds up redesign using structure-based computational design to reduce the mAbs search space to a manageable level and screen mutants at a much higher rate than in experiments. Computation will also provide critical knowledge about the fundamental interactions. The project will adapt S230, a human antibody that neutralizes SARS-CoV, to neutralize SARS-COV-2.