# Integration of biophysics and deep learning to understand species-specificity of fertilization and the rapid evolution of protein disorder

> **NIH NIH R35** · OHIO STATE UNIVERSITY · 2024 · $375,781

## Abstract

Project Summary
 Proteins often fold into three-dimensional shapes and operate as cellular machines with well-defined
reaction mechanics – hence the common biochemical expression “structure is function.” However, this rule
only applies to the slowest evolving portions of the human genome. Proteins that evolve more rapidly are
typically less ordered, both enabling their accelerated evolution and expanding the biochemical landscape on
which natural selection may act. Currently there exist few tools and conceptual frameworks to understand the
sequence-function relationship of quickly evolving dynamical proteins. Across the tree of life, reproductive
proteins evolve at extraordinary rates – typically faster than immune genes – and the Wilburn lab studies the
biophysics and molecular evolution of species-specific fertilization in animals. The continuous coevolution of
interacting sperm and egg proteins has selected for biochemical properties such as intrinsic disorder, weak
binding affinities, etc. that complicate their study. High-field NMR spectroscopy is unique among structural
methods in its ability to study such heterogenous protein systems, and I have pioneered NMR studies of
fertilization proteins in a classic model of fertilization research (marine abalone). Over the next 5 years, we will
interrogate the sequence-to-function relationships of gamete recognition proteins important for species-specific
fertilization by pairing high throughput mutagenesis screens with targeted biophysical analyses to better
understand the complex interplay of molecular dynamics with protein evolvability and interaction kinetics. NMR
methods will be expanded to the study of mammalian fertilization proteins. To facilitate our own work and
empower other researchers, deep learning-based analytical tools in evolutionary genomics, mass spectrometry
proteomics, and NMR dynamics will be developed. The proposed research will provide an evolutionary
framework to better understand the breadth of protein biochemistry encoded by the human genome, and
includes diverse training opportunities for undergraduate, graduate students, and postdocs

## Key facts

- **NIH application ID:** 10916420
- **Project number:** 5R35GM150583-02
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Damien Beau Wilburn
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $375,781
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10916420

## Citation

> US National Institutes of Health, RePORTER application 10916420, Integration of biophysics and deep learning to understand species-specificity of fertilization and the rapid evolution of protein disorder (5R35GM150583-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10916420. Licensed CC0.

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