Intelligent in silico antibody library design and optimization for therapeutic use

NIH RePORTER · NIH · R43 · $219,459 · view on reporter.nih.gov ↗

Abstract

Antibody discovery technologies have delivered life saving therapies over the past three decades, however, many challenging targets remain undrugged due to limitations of these technologies. Phage display technology presents a solution to discovering antibodies to targets that do not elicit a strong immune response in animals, are toxic, or are difficult to produce in large quantities in the correct conformation. Synthetic libraries, in which antibodies are designed in silico, are a promising approach that does not rely on animal immunizations or donors. However, most synthetic libraries in use today were constructed by fixing certain regions of the antibody while generating random sequences from position-specific amino acid frequencies for antigen-binding regions. This departure from the space of natural antibodies results in antibodies with poor biophysical characteristics such as low melting temperature, aggregation propensity, or general ‘stickiness’. Digital Proteomics has developed a computational workflow for natural antibody repertoire analysis, harnessing the throughput of next-generation sequencing of immune cells, that will be used to design a better synthetic library for therapeutic antibody discovery. Rules of natural antibody development will be incorporated into the design of a synthetic antibody library, thereby retaining the biophysical property profile of natural antibodies. Antibody genes develop through a sequential process involving site-specific genome recombination and somatic hypermutation, which will be modeled in silico. While in conventional antibodies, this process occurs at two independent loci (a heavy and light chain that must interact), the proof-of-concept will be applied to the single chain antibodies produced by camelids. Using an improved synthetic library framework, the computational design of an antibody library optimized for therapeutic discovery will contain antibodies that display superior biophysical properties and reduced sequence liabilities. The library will be an invaluable tool for rapid discovery of therapeutic antibodies to a wide range of diseases.

Key facts

NIH application ID
10477656
Project number
6R43GM140499-02
Recipient
ABTERRA BIOSCIENCES, INC.
Principal Investigator
Natalie Castellana
Activity code
R43
Funding institute
NIH
Fiscal year
2021
Award amount
$219,459
Award type
6
Project period
2021-02-01 → 2023-07-31