# High-throughput thermodynamic and kinetic measurements for variant effects prediction in a major protein superfamily

> **NIH NIH F31** · STANFORD UNIVERSITY · 2023 · $47,694

## Abstract

PROJECT SUMMARY
Many disease-associated variants in coding regions of the genome affect translated protein and enzyme
products by perturbing their folded conformation or their function, such as interactions with substrates or
macromolecular partners. However, we lack a unified predictive framework to predict functional effects of coding
variants, limiting how genomic data can be used in precision medicine. Machine learning models trained on large
sequence databases have claimed to predict deleterious effects from coding variants in several model proteins,
but to date their practical usage has been limited because of two major challenges. The first is the lack of
descriptive, “ground truth” biophysical datasets relating sequence variation to native protein properties, due to
the low throughput of traditional biochemical and biophysical experiments. The second is that there is not a well-
established method for integrating these data in state-of-the-art predictive models. To address these critical
limitations, I propose to apply cutting-edge microfluidic techniques to generate large quantitative biophysical
datasets connecting sequence variation to function in human acylphosphatase (ACYP), a model protein of the
alpha/beta fold family (found in ~10% of human proteins), and leverage these data to enhance predictive models.
This microfluidic platform (HT-MEK) contains an array of chambers that allow for parallel expression and
purification of >1,700 proteins, and provides measurements of in vitro kinetic and thermodynamic constants for
each. In Aim 1, I will engineer a series of ACYP functional assays using HT-MEK and derivative microfluidic
technologies, first testing in vitro expression, on-chip stability, and catalytic turnover of a small library of ACYP
variants and finally comparing to traditional biochemical measurements. In Aim 2, I will rapidly generate scanning
mutagenesis libraries in ACYP and make measurements across hundreds of ACYP variants on HT-MEK. In Aim
3, in collaboration with ML experts, I will use this unprecedented quantitative biochemical dataset to fine-tune a
cutting-edge deep learning to provide the first variant effects predictor enhanced by in vitro data at scale. My
preliminary data has shown that this model can generate de novo ACYP sequences that fold and are catalytically
proficient, suggesting that it will provide a strong foundation for functional prediction. Together, my results will
provide insight into the utility of in vitro, biochemical datasets from human proteins in training better predictors of
disease phenotypes. The training that I will obtain in carrying out these Aims will allow me to (1) develop skills
in research design, analysis, and interpretation of protein biophysics data; (2) learn advanced techniques in
protein biochemistry and statistical sequence analysis; and (3) obtain a competitive post-doctoral fellowship with
the long-term goal of establishing an independently-funded laboratory at a researc...

## Key facts

- **NIH application ID:** 10752370
- **Project number:** 1F31HG013267-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Micah Olivas
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $47,694
- **Award type:** 1
- **Project period:** 2023-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10752370

## Citation

> US National Institutes of Health, RePORTER application 10752370, High-throughput thermodynamic and kinetic measurements for variant effects prediction in a major protein superfamily (1F31HG013267-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10752370. Licensed CC0.

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