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• W2895839735 abstract "Antibodies are proteins of the adaptive immune system; they can be designed to bind almost any molecule, and are increasingly being used as biotherapeutics. Experimental antibody design is an expensive and time-consuming process, and computational antibody design methods can now be used to help develop new therapeutics and diagnostics. Within the design pipeline, accurate antibody structure modeling is essential, as it provides the basis for antibody-antigen docking, binding affinity prediction, and estimating thermal stability. Ideally, models should be rapidly generated, allowing the exploration of the breadth of antibody space. This allows methods to replicate the natural processes of antibody diversification (e.g., V(D)J recombination and somatic hypermutation), and cope with large volumes of data that are typical of next-generation sequencing datasets. Here we describe ABodyBuilder and PEARS, algorithms that build and mutate antibody model structures. These methods take ~30 s to generate a model antibody structure." @default.
• W2895839735 created "2018-10-26" @default.
• W2895839735 creator A5003624530 @default.
• W2895839735 creator A5054519833 @default.
• W2895839735 date "2018-09-27" @default.
• W2895839735 modified "2023-10-01" @default.
• W2895839735 title "High-Throughput Antibody Structure Modeling and Design Using ABodyBuilder" @default.
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• W2895839735 doi "https://doi.org/10.1007/978-1-4939-8736-8_21" @default.
• W2895839735 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30298409" @default.
• W2895839735 hasPublicationYear "2018" @default.
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