# Intelligent in silico antibody library design and optimization for therapeutic use

> **NIH NIH R43** · DIGITAL PROTEOMICS LLC · 2021 · $24,590

## 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:** 10139510
- **Project number:** 1R43GM140499-01
- **Recipient organization:** DIGITAL PROTEOMICS LLC
- **Principal Investigator:** Natalie Castellana
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $24,590
- **Award type:** 1
- **Project period:** 2021-02-01 → 2021-05-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10139510

## Citation

> US National Institutes of Health, RePORTER application 10139510, Intelligent in silico antibody library design and optimization for therapeutic use (1R43GM140499-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10139510. Licensed CC0.

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