# Machine learning-based development of serologic test for acute Lyme disease diagnosis

> **NIH NIH R43** · BIOMORPH TECHNOLOGIES LLC · 2021 · $300,000

## Abstract

Project Summary/Abstract
Lyme Disease is a tickborne illness with markedly increasing prevalence in the United States and an urgent
need for improved diagnostics in its early stages, when treatment is most efficient. While classic clinical
presentation of the early illness is the presence of erythema migrans (EM), or “bullseye rash”, surrounding the
tick bite site, 20-30% of patients do not present with EM. Further complicating diagnosis is a proportion of
patients who present with EM, but are seronegative on the current standard two-tiered test algorithm (STTTA).
The proposed research addresses the need for improved serological tests to diagnose early Lyme Disease in
these patients, while the disease is the most responsive to treatment. A proof-of-concept antigen panel
capable of distinguishing STTTA-positive acute Lyme samples from endemic controls was identified using a
novel antigen discovery approach. This approach relies on representing an entire binding space of a donor’s
circulating antibody repertoire using machine learning models based on the antibody binding profile to a
diverse, random library of 126,050 peptides with an average length of 9 amino acids, which is a sparse
representation of all possible amino acid combinations. Resulting models are then used to identify pathogen
epitopes with high predictive power that are combined into a panel with diagnostic efficacy. Here, the unmet
need of diagnosing early Lyme disease in STTTA-seronegative patients is addressed by the addition of
antigens predicted as specific to this patient population. Diagnostic efficacy of the supplemented proof-of-
concept antigen panel, that was identified in a previous proof-of-principle study, will be tested using an
expanded cohort of STTTA seronegative donors and endemic controls. Specificity of the panel for Lyme
disease will be confirmed using a panel of look-a-like illnesses including autoimmune diseases and tickborne
diseases. This work is expected to yield data demonstrating the feasibility of a novel immunoassay for the
diagnosis of early stage Lyme Disease patients currently missed by present tests. Additionally, it will serve as a
demonstration of the antigen discovery approach as a means to identify diagnostic antigens for difficult
pathogens.

## Key facts

- **NIH application ID:** 10259497
- **Project number:** 1R43AI162473-01
- **Recipient organization:** BIOMORPH TECHNOLOGIES LLC
- **Principal Investigator:** Laimonas Kelbauskas
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $300,000
- **Award type:** 1
- **Project period:** 2021-06-04 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10259497

## Citation

> US National Institutes of Health, RePORTER application 10259497, Machine learning-based development of serologic test for acute Lyme disease diagnosis (1R43AI162473-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10259497. Licensed CC0.

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