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.

Public Health Relevance

Most proteins in an organism are chemically modified on a reversible basis and disruptions to such ?post-translational modification? (PTM) play an important role in diseases like cancer and Alzheimer's. In this multi-disciplinary proposal, we build upon previous experimental, mathematical and computational advances to analyze the tumor suppressor and ?guardian of the genome? p53, which is modified at over 100 sites. We expect to learn a great deal about how p53 processes information as a central cellular hub, while developing concepts and methods which can be widely applied to other proteins in which PTM plays a key role.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Brazhnik, Paul
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Harvard Medical School
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Compton, Philip D; Kelleher, Neil L; Gunawardena, Jeremy (2018) Estimating the Distribution of Protein Post-Translational Modification States by Mass Spectrometry. J Proteome Res 17:2727-2734
Malleshaiah, Mohan; Padi, Megha; Rué, Pau et al. (2016) Nac1 Coordinates a Sub-network of Pluripotency Factors to Regulate Embryonic Stem Cell Differentiation. Cell Rep 14:1181-1194