# Quantitative high-throughput methods for antibody fragment optimization and discovery

> **NIH NIH R44** · PROTILLION BIOSCIENCES, INC. · 2021 · $855,260

## Abstract

Abstract
Monoclonal antibodies and antibody fragments are an important class of therapeutics comprising a $150B
industry. However, methods for discovering and optimizing antibodies to have desired affinity are generally
laborious laboratory procedures that require months of hands-on research performed by highly skilled
personnel (e.g. phage display, hybridoma, single cell). Additionally, the selection of leads to move forward in
the therapeutic development pipeline often must be made with limited information that does not necessarily
correspond to quantitative binding affinity. To address these challenges, Protillion has commercialized Prot-
MaP, a platform for measuring quantitative protein binding across large libraries of 105 to 109 variants on
automated instrumentation, with a time-to-result of approximately 2 days. We achieve this by generating
immobilized proteins directly on Illumina DNA sequencing flow cells through a process of in-situ transcription
and translation. This platform allows for direct, quantitative measurements of fluorescent antigen binding to
entire protein libraries at unprecedented scale—a scale that is finally a match for the sparseness of protein
function in amino acid mutation space. In our Phase I period, we adapted Prot-MaP to display VHHs
(nanobodies) capable of binding the SARS-CoV-2 spike (S1) receptor binding domain (RBD) protein. Our
multi-step optimization first comprehensively identified “beneficial” mutations, which were then combined into a
second combinatorial library. This strategy identified tens of thousands of protein variants with affinity superior
to wild type, with the best exhibiting the highest reported binding affinity for a VHH to this target, a 100-fold
improvement from the starting point. We also developed a strategy to humanize this nanobody, producing a
near-fully-human sequence that maintained high affinity. In Phase II, we will first improve automation and
commercial scalability of our instrumentation, and develop deep learning models for library design and
selection of therapeutic leads. We will next optimize other SARS-CoV-2 S1 RBD-binding nanobodies, as well
as nanobodies capable of binding PD-L1, a target relevant to cancer immunotherapy. We will develop a
universally applicable pipeline for identifying high-affinity, humanized, clinically-relevant VHH reagents. We will
also extend our display capabilities to larger, scFv domains, and carry out scFv affinity optimization against two
separate target ligands, including SARS-CoV-2 S1 RBD. Finally, we will adapt our methods to display up to 109
distinct protein variants on a NovaSeq sequencing chip, a scale sufficient to identify binders de novo from
naïve humanized VHH libraries. The activities outlined in this proposal will enable display multiple types of
antibody fragments, optimize affinity and humanize their sequences, and clearly define the landscape of
functional protein sequences. The capability of de novo discovery of new binders...

## Key facts

- **NIH application ID:** 10325926
- **Project number:** 2R44GM137655-02
- **Recipient organization:** PROTILLION BIOSCIENCES, INC.
- **Principal Investigator:** Curtis Layton
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $855,260
- **Award type:** 2
- **Project period:** 2020-05-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10325926

## Citation

> US National Institutes of Health, RePORTER application 10325926, Quantitative high-throughput methods for antibody fragment optimization and discovery (2R44GM137655-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10325926. Licensed CC0.

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