# Manipulating and predicting the unfolded ensembles of disordered proteins

> **NIH NIH R01** · UNIVERSITY OF CHICAGO · 2020 · $349,459

## Abstract

PROJECT SUMMARY
The physical properties of intrinsically disordered proteins (IDPs) affect their positive (functional) and negative
(disease-causing) roles in cell function. Yet despite intense effort, we still lack a predictive understanding of the
physical properties that govern whether a given polypeptide chain sequence will adopt an expanded or
collapsed conformational ensemble under physiological conditions – and by extension, how collapse affects
protein function. The Sosnick and Clark labs have formed a collaboration to develop precisely this
understanding. This project consists of experimental studies of IDPs tightly integrated with new data analysis
procedures and computational modeling tools. This project builds on our recent findings with “PNt”, a 334
residue IDP that under physiological conditions adopts an expanded ensemble of conformations well
approximated by a self-avoiding random walk, despite having an amino acid content that, according to the
current paradigm, should lead to a collapsed, self-associated state. We hypothesize that current models fail to
predict the behavior of PNt due to current knowledge gaps regarding which sequence patterns, beyond global
sequence composition, lead to collapse. We propose that deviations from an expanded state, e.g., adopting a
collapsed globule, are due to specific sequence patterns including local stretches of hydrophobic residues. We
will test this hypothesis by reordering (“shuffling”) the amino acid sequence of PNt to induce collapse, and
likewise shuffle the amino acid sequence of maltose binding protein (MBP) to promote expansion of its
denatured state, which we recently demonstrated is highly collapsed. We will use a battery of biophysical
methods (SAXS, hydrogen-deuterium exchange, NMR) to measure the extent of local versus global collapse,
assessing the sensitivity of conformational ensembles to subtle changes in sequence order, and the
relationship between collapse and hydrogen bonding. We will determine which hydrophobicity scales yield the
best predictions of experimental results for polypeptide chain collapse, and use the effects of shuffling to
parameterize simulations. Finally, we will test the impact of altering IDP collapse on protein function in vivo,
specifically the efficient secretion of autotransporter virulence proteins to the surface of Gram-negative
bacteria. Our overall goal is to accurately predict the conformational ensemble for a user-inputted amino acid
sequence and solvent condition.

## Key facts

- **NIH application ID:** 9985151
- **Project number:** 5R01GM130122-03
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** Patricia Louise Clark
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $349,459
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985151

## Citation

> US National Institutes of Health, RePORTER application 9985151, Manipulating and predicting the unfolded ensembles of disordered proteins (5R01GM130122-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9985151. Licensed CC0.

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