# Predicting and analyzing variation in cellular interactomes

> **NIH NIH R01** · PRINCETON UNIVERSITY · 2020 · $312,660

## Abstract

Project Summary
Over the last two decades, significant experimental efforts have determined large sets of “reference”
interactions for humans and other model organisms, along with substantial knowledge about the binding
specificities of proteins, including for a large fraction of human transcription factors (TFs). The resulting data
have proven to be an incredibly useful resource for understanding how cells function; nevertheless, they do not
capture how molecular interactions and networks are different from the reference across individuals. Indeed,
while human genomes in both healthy and disease populations are rapidly being sequenced, the
corresponding individual-specific interaction networks remain largely unexamined; this represents a major gap
in our knowledge, as mutations that alter molecular interactions underlie a wide range of human diseases.
Further, the substantial amount of genetic variation across populations makes it infeasible in the near term to
experimentally determine per-individual interaction networks. Thus our long-term goal is to develop
computational methods to uncover whether and how mutations within coding and non-coding portions of the
genome perturb cellular interactions and networks. Our specific aims are: (1) We will develop computational
structure-based approaches to identify and catalog, at proteome-scale, variations within proteins that are likely
to impact their ability to bind with DNA, RNA, small molecules, peptides or ions, thereby providing a
comprehensive resource for analyzing protein interaction variation. (2) We will develop novel structure-based
and probabilistic methods to predict how DNA-binding specificities are altered when a TF is mutated; since
mutated TFs have been linked to numerous diseases, this will be a great aid in understanding disease
networks and pathology. (3) We will develop new methods to uncover non-coding somatic mutations that alter
human regulatory networks in cancer; this is a critical step towards ultimately uncovering patient-specific
cancer networks. Overall by pursuing these aims—which integrate mutational information with existing
knowledge about reference interactions, interfaces and specificities—we will develop novel computational
methods that will significantly advance our understanding of molecular interactions perturbed in disease and
healthy contexts.

## Key facts

- **NIH application ID:** 9896829
- **Project number:** 5R01GM076275-11
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** MONA SINGH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $312,660
- **Award type:** 5
- **Project period:** 2006-02-18 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9896829

## Citation

> US National Institutes of Health, RePORTER application 9896829, Predicting and analyzing variation in cellular interactomes (5R01GM076275-11). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9896829. Licensed CC0.

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