Computational design of antibodies.

By Sharon Fischman, Yanay Ofran. 2018

64 citations recorded by the source corpus.

Antibody design aims to create new antibodies with biological activity that can be used in therapy and research. Traditional methods for antibody discovery, such as animal immunization and large-scale library screening, generate antibodies that bind to the target of interest, but do not necessarily have the desired functional effect. Computational methods can be utilized as a means to guide the search for biologically relevant antibodies, focusing on specificity and affinity determinants to target a particular region of the antigen. Such an approach would allow for the design of epitope-specific antibodies that will have the desired effect on the function of the targeted protein.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

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In Molecular Biology

Citations in the corpus

Fast and flexible design of novel proteins using graph neural networks (2019, background): “Engineering novel protein sequences has a broad variety of uses, including academic research, industrial process engineering (1), and most notably, protein-based therapeutics, which are now a very important class of drugs (2, 3).”
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design (2021, background): “…structure (Alley et al., 2019; Shin et al., 2021) can lead to sub-optimal performance (Ingraham et al., 2019), while generating from a predeﬁned 3D structure (Ingraham et al., 2019) is not suitable for antibodies since the desired structure is rarely known a priori (Fischman & Ofran, 2018).”