# Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in infectious disease

> **NIH NIH DP2** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $477,000

## Abstract

Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in
 infectious disease
CHALLENGE: Over the past decade, technologies for deep profiling of the human immune system, both in
the context of natural and vaccine-mediated immunity, have become readily available. These approaches
have generated a wide range of molecular profiles across infectious disease contexts. However, existing
studies primarily focus on individual `omic datasets, and do not take into account the underlying molecular
networks. Thus, the primary emphasis has been on uncovering predictive biomarkers, but these biomarkers
may often be correlative surrogates and have little or no connection with the underlying molecular phenotypes
driving disease pathophysiology.
GOAL I propose to develop and use a novel framework to integrate genomic data with three-dimensional (3D)
structurally-resolved protein networks to uncover immuno-modulatory molecular phenotypes in infectious
disease. While protein networks are typically viewed as two-dimensional, with proteins as nodes and
interactions between them as edges, this simplifying representation fails to take into account the 3D structures
of the proteins themselves, and the corresponding interaction interfaces. My past work has demonstrated the
critical importance of taking into account corresponding structural information in the integration of Mendelian
mutations with protein networks, to elucidate molecular phenotypes underlying the corresponding genetic
disorders, with high sensitivity and specificity. Here, I propose to develop a novel framework that integrates
structural genomic data with host-pathogen protein interactome networks to generate 3D host-pathogen
interactomes. These 3D interactome networks are then integrated with host (human) genetic data to uncover
immuno-modulatory molecular phenotypes in HIV and influenza.
INNOVATION AND IMPACT: The proposed work integrates both two orthogonal facets of my expertise in
network systems biology and machine learning, and pushes the envelope on multiple key frontiers. First, it
provides a novel framework for the integration of host genetic data with host-pathogen protein networks.
Second, a key novelty is the incorporation of structural information corresponding to host-pathogen protein
interaction interfaces to refine the traditional principle of “guilt-by-association”, and hone in on specific
molecular phenotypes that modulate infectious disease risk. The identified molecular phenotypes will generate
key mechanistic hypotheses regarding corresponding disease pathophysiology, and help design
interventional strategies. Finally, while the focus here is to use this approach in HIV and influenza, the
framework itself is generalizable and can be used across infectious disease contexts.

## Key facts

- **NIH application ID:** 10458682
- **Project number:** 5DP2AI164325-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Jishnu Das
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $477,000
- **Award type:** 5
- **Project period:** 2021-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10458682

## Citation

> US National Institutes of Health, RePORTER application 10458682, Using three-dimensional protein networks to uncover immuno-modulatory molecular phenotypes in infectious disease (5DP2AI164325-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10458682. Licensed CC0.

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