# Towards a comprehensive multiscale 3D human interactome network

> **NIH NIH R01** · CORNELL UNIVERSITY · 2020 · $384,165

## Abstract

PROJECT SUMMARY/ABSTRACT
Almost all proteins function through interacting with other proteins. On average, a protein interacts with ~5
other protein partners in the current human interactome. Therefore, it is of great importance to accurately
determine the interface of each interaction, in order to understand how each protein works with different
partners to carry out different functions. In our previous Nature Biotechnology study, we implemented a
proteome-scale homology modeling approach to generate the first 3D human structural interactome: the
interface for each interaction in this network was determined at atomic resolution through co-crystal structures
and homology models. Using our 3D interactome, we found that, among >1,800 known disease genes
associated with two or more clinically distinctly disorders, pairs of mutations on the same gene but in different
interfaces with different partners are significantly more likely to cause distinct diseases.
However, only 4,150 human protein interactions have co-crystal structures and 2,921 have high-quality
homology models. ~50,000 interactions (87% of the current human interactome) are not amenable to current
structural modeling methods. Here, we propose to develop a big-data-driven machine-learning approach
integrating biophysiochemical, evolutionary, structural, and population genetic features to identify interaction-
specific interfaces for the whole human interactome. Because several key features are unavailable for many
proteins and interactions, we propose an innovative approach to use an ensemble of random forest classifiers,
named Ensemble Protein Interface Classifier (EPIC), to address this large-scale non-random missing data
problem (Aim 1). The high throughput of our massively parallel Clone-seq and INtegrated PrOtein INteractome
perTurbation screening (InPOINT) pipeline! uniquely enables us to perform real-time experimental parameter
optimization (in Years 2-4 we will clone ~1,500 mutations and examine their impact on ~2,500 interactions
every year to iteratively evaluate and refine EPIC; Aim 2). Finally, we will construct a comprehensive
multiscale 3D interactome for all known human protein-protein interactions: we will collect/generate atomic-
resolution structural models for interactions whenever possible (co-crystal structures and homology models);
we will accurately determine interaction-specific interface residues and domains for the whole human
interactome. We will deploy an interactive web portal to disseminate our results and allow functional genomic
inference in the context of our structural interactome (Aim 3).
Our comprehensive multiscale 3D human interactome and the accompanying web portal will greatly reduce the
barrier-to-entry for performing systematic structural analysis on a large number of proteins and their
interactions, and open the flood gates for such analyses in genomic studies.

## Key facts

- **NIH application ID:** 9963288
- **Project number:** 5R01GM124559-04
- **Recipient organization:** CORNELL UNIVERSITY
- **Principal Investigator:** Haiyuan Yu
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $384,165
- **Award type:** 5
- **Project period:** 2017-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9963288

## Citation

> US National Institutes of Health, RePORTER application 9963288, Towards a comprehensive multiscale 3D human interactome network (5R01GM124559-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9963288. Licensed CC0.

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