# Genome-wide structure-based analysis of protein-protein interactions and networks

> **NIH NIH R35** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $405,000

## Abstract

Project Summary
 Our lab works in the general area of computational structural biology although it also includes an
experimental component. We carry out theoretical and computational research and develop software tools, with
our efforts being guided by a variety of applications of biomedical importance. In the past we have elucidated
the structural and energetic origins of protein-protein and protein-nucleic acid interactions, developed methods
for protein structure prediction, and detected novel structural and functional relationships between proteins based
on their geometric similarity. We currently focus on two distinct areas: the exploitation of structural information
to predict protein function on a genome-wide scale and the molecular basis of cell-cell recognition. The former
topic is the subject of the current proposal which focuses on the prediction of protein-protein interactions (PPIs)
and protein interaction networks. Our overarching goal is to provide a structure-informed perspective in multiple
areas of systems biology, thus filling a major gap in this rapidly growing area of biomedical research.
 Our research plans are derived from our development of the PrePPI algorithm and corresponding
database of human PPIs. PrePPI provides proteome-wide structure-based predictions of PPIs, and discovers
relationships not obtainable from other methods. The P-HIPSTer algorithm, which is derived from PrePPI, offers
analogous information for virus-human PPIs for 1000 human-infecting viruses. The reliability of both resources
has been validated experimentally, and both have revealed novel biological insights. PrePPI, in common with
other PPI databases, is cell-context independent and, for example, does not distinguish among tissue and tumor
types. To address this challenge, we developed the OncoSig algorithm that uses machine learning methods to
combine PrePPI with regulatory interactions from patient genomic data. The generation of tumor-specific lists of
PPIs, called SigSets, can then be mapped onto a context-dependent PPI network, or SigMap. We have also
developed novel methodologies that link protein structure space with chemical compound space.
 The current proposal builds on these accomplishments with new methodological developments and new
applications to network biology. We plan to integrate PrePPI with PPI information derived from genetic
interactions derived from the correlation of gene profiles across many conditions (e.g. tumor types, cell lines or
drug treatments). This will provide an unprecedented structure- and context-dependent view of protein interaction
networks. Other plans include the extension of PrePPI to non-human genomes and the extension of P-HIPSTer
to bacterial pathogens. Our overall vision includes the development of an integrated set of software tools and
databases that will advance cutting edge biomedical applications. These tools will range in scope from protein-
protein interaction networks, structure-derived protein...

## Key facts

- **NIH application ID:** 10320837
- **Project number:** 5R35GM139585-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** BARRY H HONIG
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $405,000
- **Award type:** 5
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10320837

## Citation

> US National Institutes of Health, RePORTER application 10320837, Genome-wide structure-based analysis of protein-protein interactions and networks (5R35GM139585-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10320837. Licensed CC0.

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