# Rapid structure-based software to enhance antibody affinity and developability for high-throughput screening

> **NIH NIH R44** · DNASTAR, INC. · 2020 · $999,980

## Abstract

Therapeutic monoclonal antibodies bind to specific regions of proteins called epitopes, which elicit cellular
responses that treat or cure disease. Discovering therapeutic antibodies traditionally requires laborious and
expensive screening experiments, so computational approaches that select which antibodies bind an epitope
best and have the most desirable pharmaceutical properties are in high demand. Structure-based antibody
design is also important to the modern drug discovery and development process. This approach requires a high-
resolution quaternary (3D) protein complex structure, whose experimental determination is often a slow process
that is not always successful. Protein structure and binding interface prediction algorithms are poised to impact
human health by accelerating the construction of high-confidence structural models of drug targets and
biopharmaceuticals, which will help identify new therapeutic strategies. However, the current algorithms are
limited in their ability to distinguish stronger-binding antibodies from weaker ones, which is preventing the
discovery of broad classes of therapeutics. In addition, technologies are needed to predict if a candidate antibody
will fail as early as possible in the development process. With improvements in simulating removal of molecular
liabilities without damaging function, computer-aided antibody design can be used to lower drug development
costs and focus experiments on the most promising drug candidates.
 Here we propose to advance antibody discovery by developing highly accurate software tools built on the
success of DNASTAR’s NovaFold Antibody program for antibody structure prediction, NovaDock for flexible
protein-protein docking, and Lasergene Protein Design for protein engineering. The aims of the project focus 1)
on developing more accurate and effective immune complex (an interacting antibody and antigen) structure
predictions through better modeling of the challenging complementarity-determining regions (CDR), which play
a critical role in antibody affinity and selectivity; and 2) on predicting antibody sequences that reduce chemical
and energetic liabilities that prove detrimental to an antibody’s manufacturing process or therapeutic effect in a
patient. In particular, overall predictive capability will be improved by incorporating computational acceleration
techniques to support the virtual screening of tens of thousands of antibody sequences. Finally, and for the first
time, this project will develop a “virtual immune system” to approach human antibody discovery, where antibodies
will be modeled from germline sequences and selected for best recognizing an antigen of interest. The overall
project goal is to deliver an advanced antibody screening pipeline that is powerful, accurate, and produces fast
results, which will accelerate antibody discovery by enabling detailed and accurate immune complex structure
predictions and structure-based liability detection at a high-throughp...

## Key facts

- **NIH application ID:** 10080587
- **Project number:** 1R44AI155254-01
- **Recipient organization:** DNASTAR, INC.
- **Principal Investigator:** Steven Joseph Darnell
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $999,980
- **Award type:** 1
- **Project period:** 2020-05-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10080587

## Citation

> US National Institutes of Health, RePORTER application 10080587, Rapid structure-based software to enhance antibody affinity and developability for high-throughput screening (1R44AI155254-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10080587. Licensed CC0.

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