# Information Processing by Post-translational Modification

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2020 · $366,235

## Abstract

Summary/Abstract
Most proteins in an organism are reversibly modified by covalent attachment of chemical groups to
specific amino-acid residues. Such “post-translational modification” (PTM) allows protein function to
be modulated on a physiological time-scale. PTMs are involved in regulating most cellular processes
and frequently disordered in major diseases. It is not uncommon for a protein to have many types of
modification, for these to occur on multiple sites across the protein and for modifications on different
sites to interact combinatorially to influence protein function. This can create an explosion of
combinatorial modification states. The tumor suppressor p53, on which this proposal will focus, has
more than 100 sites of modification, creating the potential for more than 1030 combinatorial
modification states on this single protein. Very few of these states will be present at any time but that
leaves open the question of which states are present and how they vary under different cellular
conditions. Our previous work has laid a foundation for addressing these questions. We have
introduced the quantitative language of “modforms” and “modform distributions”, which provide a
rigorous biophysical basis for describing PTM states in vivo. We have created mathematical methods
for analyzing the mass-spectrometry (MS) approaches which can measure such distributions and
have determined their fundamental limits. We have shown the necessity of combining “bottom-up” and
“middle-down” MS, in which proteins are first digested in peptides, with “top-down” MS on whole
proteins. These different types of MS data constrain a protein's modform distribution within a high-
dimensional “modform region”. We have developed publicly-accessible software for estimating such
regions. We have discovered that p53 integrates its pulsatile expression dynamics and its PTM state
to act as a central cellular hub and orchestrate multiple downstream pathways in response to multiple
upstream conditions. Here, we build on these findings with a tested multi-disciplinary group of
collaborators, whose expertise spans mathematics, computation, cell biology and mass spectrometry.
Our goal is to develop MS methods to estimate modform regions of typical cellular proteins and to use
those methods to unravel how p53 combines dynamics and modforms to process information. By
focusing on such a challenging exemplar, we expect to learn a great deal about p53 itself while
developing concepts and methods that can be widely applied to other cellular proteins in which PTMs
play a central role.

## Key facts

- **NIH application ID:** 10013233
- **Project number:** 5R01GM105375-06
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** JEREMY GUNAWARDENA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $366,235
- **Award type:** 5
- **Project period:** 2014-12-15 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10013233

## Citation

> US National Institutes of Health, RePORTER application 10013233, Information Processing by Post-translational Modification (5R01GM105375-06). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10013233. Licensed CC0.

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