Mutual Interactors as a Principle for Phenotype Discovery in Molecular Interaction Networks

Biological networks are powerful representations for the discovery of molecular phenotypes. Fundamental to network analysis is the principle—rooted in social networks—that nodes that interact in the network tend to have similar properties. While this long-standing principle underlies powerful methods in biology that associate molecules with phenotypes on the basis of network proximity, interacting molecules are not necessarily similar, and molecules with similar properties do not necessarily interact.

Here, we show that molecules are more likely to have similar phenotypes, not if they directly interact in a molecular network, but if they interact with the same molecules. We call this the mutual interactor principle and show that it holds for several kinds of molecular networks, including protein-protein interaction, genetic interaction, and signaling networks.

We develop a machine learning framework for predicting molecular phenotypes on the basis of mutual interactors. Strikingly, the framework can predict drug targets, disease proteins, and protein functions in different species, and it performs better than much more complex algorithms. The framework is robust to incomplete biological data and is capable of generalizing to phenotypes it has not seen during training. Our work represents a network-based predictive platform for phenotypic characterization of biological molecules.

Molecules in and across living cells are constantly interacting, giving rise to complex biological networks. These networks serve as a powerful resource for the study of human disease, molecular function and drug-target interactions. For instance, evidence from multiple sources suggests that causative genes from the same or similar diseases tend to reside in the same neighborhood of protein-protein interaction networks. Similarly, proteins associated with the same molecular functions form highly-connected modules within protein-protein interaction networks.

These observations have motivated the development of bioinformatics methods that use molecular networks to infer associations between proteins and molecular phenotypes, including diseases, molecular functions, and drug targets. Many of these methods assume that molecular networks obey the organizing principle of homophily: the idea that similarity breeds connection. However, while this principle has been well-documented in social networks of many types (e.g. friendship, work, co-membership), it is unclear whether it captures the dynamics of biological networks. If not, existing bioinformatics methods that assume homophily may not realize the full potential of biological networks for scientific discovery.

Motivation for Mutual Interactors

To better understand the place for homophily in bioinformatics, we consider groups of phenotypically similar molecules (e.g. molecules associated with the same disease, involved in the same function, or targeted by the same drug) and study their interactions in large-scale biological networks. We find that most molecules associated with similar phenotypes do not interact directly in molecular networks, a result which puts into question the assumption of homophily, an assumption that is taken for granted by so many bioinformatics methods.

In fact, a different principle better explains how phenotypic similarity relates to network structure in biology. On average, two molecules that interact directly with one another will have less in common than two molecules that share many mutual interactors, just as people in a social network may share mutual friends. We call this the mutual interactor principle and validate it empirically on a diverse set of biological networks (see Figure 1c).

Mutual Interactors Approach

Motivated by our findings, we develop a machine learning framework, Mutual Interactors, that can predict a molecule’s phenotype based on the mutual interactors it shares with other molecules. We demonstrate the power, robustness, and scalability of Mutual Interactors on three key prediction tasks: disease protein prediction, drug target identification, and protein function prediction. With experiments across three different kinds of molecular networks (protein-protein interaction, signaling and genetic interaction) and four species (H. sapiens, S. cerevisiae, A. thaliana, M. musculus), we find that Mutual Interactors substantially outperforms existing methods, with gains in recall up to 61%. Additionally, we show that the weights learned by our method provide insight into the functional properties and druggability of mutual interactors.

Mutual Interactors is an approach based on a different network principle than existing bioinformatics methods. That it can outperform state-of-the-art approaches suggests a need to rethink the fundamental assumptions underlying machine learning methods for network biology

Uncovering Disease-Driving Proteins Using Mutual Interactors

Identifying Drug Targets Using Mutual Interactors

Publication

Mutual Interactors as a Principle for Phenotype Discovery in Molecular Interaction Networks
Sabri Eyuboglu*, Marinka Zitnik*, and Jure Leskovec
Pacific Symposium on Biocomputing, PSB 2023

@inproceedings{eyuboglu2023mutual,
title = {Mutual Interactors as a Principle for Phenotype Discovery in Molecular Interaction Networks},
author = {Eyuboglu, Sabri and Zitnik, Marinka and Leskovec, Jure},
booktitle = {Pacific Symposium on Biocomputing, PSB},
year      = {2023}
}

Code

Python implementation of Mutual Interactors are available in the GitHub repository.

Authors

Latest News

Sep 2022:   New Paper in Nature Chemical Biology

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

Sep 2022:   Self-Supervised Pre-Training at NeurIPS 2022

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

Sep 2022:   Best Paper Honorable Mention Award at IEEE VIS

Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.

Sep 2022:   Multimodal Representation Learning with Graphs

Aug 2022:   On Graph AI for Precision Medicine

The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.

Aug 2022:   Evaluating Explainability for GNNs

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

Jul 2022:   New Frontiers in Graph Learning at NeurIPS

Excited to organize the New Frontiers in Graph Learning workshop at NeurIPS.

Jul 2022:   AI4Science at NeurIPS

We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.

Jul 2022:   Graph AI for Precision Medicine at ISMB

Jul 2022:   Welcoming Fellows and Summer Students

Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.

Jun 2022:   Broadly Generalizable Pre-Training Approach

Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

Jun 2022:   Welcoming New Postdocs

Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.

May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

May 2022:   Building KGs to Support Precision Medicine

Apr 2022:   Webster on the Cover of Cell Systems

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Apr 2022:   NASA Space Biology

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

Mar 2022:   AI4Science at ICML 2022

We are excited to be selected to organize the AI4Science meeting at ICML 2022. Stay tuned for details. http://www.ai4science.net/icml22

Mar 2022:   Graph Algorithms in Biomedicine at PSB 2023

Excited to be organizing a session on Graph Algorithms at PSB 2023. Stay tuned for details.

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics