# Targeting hyper-reactive cysteines within PPI interfaces

> **NIH NIH F31** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $40,049

## Abstract

Project Summary
Protein-protein interactions (PPIs) are essential in cellular processes and human diseases. It is estimated that
80% of proteins rely on PPIs to perform their primary functions. Thus, modulating PPIs should be a powerful way
to interfere with pathological pathways and treat human diseases. However, PPIs are challenging targets for
small-molecule drugs due to the lack of druggable pockets to achieve sufficient drug affinity. Recent advances
in covalent inhibition of kinases have shown that targeting hyperreactive cysteines with small electrophilic
molecules can achieve increased potency, prolonged target engagement, and improved selectivity. We
hypothesize that electrophilic molecules forming covalent bonds with hyperreactive cysteines on PPI interfaces
may represent a new avenue for developing PPI-modulating drugs.
 Building on the recent development in Artificial Intelligence (AI) methods for protein structure modeling
and analysis, the ongoing efforts to predict and model 3D structures of human PPIs in my sponsor’s lab, and my
co-sponsor’s expertise in developing covalent inhibitors, this project aims to predict and validate druggable
hyperreactive cysteines located within PPI interfaces.
 Using a Convolutional Neural Network trained on a large dataset of reactive cysteines to integrate the
physiochemical environment around a cysteine in the 3D space, I will develop a predictor for cysteine reactivity.
I will identify reactive cysteines on human PPI interfaces by integrating experimental results and predictions on
PPI structures and cysteine reactivity. Based on my preliminary data, I expect to find thousands of PPI interfaces
with hyperreactive cysteines. Next, I will analyze the protein surface pockets around hyperreactive cysteines on
PPI interfaces using both established tools to evaluate pocket druggability and new AI methods to characterize
the geometric and chemical fingerprints of a pocket. Comparing the fingerprint of a potential drug pocket against
the surface pockets of the entire human proteome will allow me to identify pockets with unique features for
specific drug targeting. Results from these analyses will be incorporated into a comprehensive online database
of reactive cysteines on PPI interfaces and their druggability. Combining the above analyses with other structural
(such as interface size and other components in a complex) and functional considerations, I will choose several
dozen target PPIs to perform virtual screens using multiple established methods to identify covalent drug
candidates. Several promising drug candidates supported by multiple methods will be tested experimentally
through pull-down assays. Experimentally tested candidates will be further studied by affinity chromatography
and mass spectrometry to evaluate the off-target binding partners.
 Overall, this project will provide valuable insights into the identification and targeting of hyperreactive
cysteines within PPI interfaces, offering new...

## Key facts

- **NIH application ID:** 10901603
- **Project number:** 1F31GM154464-01
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Jesse Durham
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $40,049
- **Award type:** 1
- **Project period:** 2024-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901603

## Citation

> US National Institutes of Health, RePORTER application 10901603, Targeting hyper-reactive cysteines within PPI interfaces (1F31GM154464-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10901603. Licensed CC0.

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