# Protein therapeutics inspired by importins

> **NIH NIH R21** · THOMAS JEFFERSON UNIVERSITY · 2022 · $215,341

## Abstract

Project Summary
Protein aggregation underlies several neurodegenerative diseases and is a pathological hallmark of
Amyotrophic Lateral Sclerosis (ALS). In this devastating disease, cytoplasmic inclusions containing
aggregated, often hyper post-translationally modified proteins are found in degenerating motor neurons and
surrounding oligodendrocytes. There is no specific pharmacological treatment to prevent protein aggregation
or promote disaggregation and clearance of existing aggregates that drive the progression of
neurodegeneration. This exploratory R21 proposal builds upon the recent discovery that nuclear import factors
of the importin β-superfamily can exert disaggregase activity toward other proteins. Combining the power of
protein biochemistry and structural biology with our know-how in nucleocytoplasmic transport, we seek to learn
from importins the biological principles for disaggregase activity and use this knowledge to design more
versatile protein therapeutics. We believe that the high-risk, high-reward R21 funding mechanism will fuel the
creative and diligent pursuit of answers to difficult biological questions, permitting our research program to
achieve significant advancements in developing novel protein therapeutics that reduce pathogenic protein
aggregation.

## Key facts

- **NIH application ID:** 10506677
- **Project number:** 1R21NS128396-01
- **Recipient organization:** THOMAS JEFFERSON UNIVERSITY
- **Principal Investigator:** Gino Cingolani
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $215,341
- **Award type:** 1
- **Project period:** 2022-08-15 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10506677

## Citation

> US National Institutes of Health, RePORTER application 10506677, Protein therapeutics inspired by importins (1R21NS128396-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10506677. Licensed CC0.

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