State of the interactomes: an evaluation of molecular networks for generating biological insights

• Published in: Molecular systems biology 2025-01, Vol.21 (1), p.1-29
• Main Authors: Wright, Sarah N, Colton, Scott, Schaffer, Leah V, Pillich, Rudolf T, Churas, Christopher, Pratt, Dexter, Ideker, Trey
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Computational Biology - methods; EMBO10; EMBO56; Gene Prioritization; Gene Regulatory Networks; Genomics - methods; Humans; Interaction Prediction; Interactome; Life Sciences; Network; Protein Interaction Mapping; Protein Interaction Maps; Proteomics - methods; Signal Transduction; Systems Biology
• ISSN: 1744-4292; 1744-4292
• DOI: 10.1038/s44320-024-00077-y

Advancements in genomic and proteomic technologies have powered the creation of large gene and protein networks (“interactomes”) for understanding biological systems. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 45 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP, Reactome, and SIGNOR demonstrate stronger performance in interaction prediction. Our study provides a benchmark for interactomes across diverse biological applications and clarifies factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future. Synopsis Systematic evaluation of 45 human interactomes offers insights and tools for network selection across biological applications. Skew toward high expression, protein abundance, and citation count is observed in all but the largest interactomes, and over- and under-enrichment of biological functions is seen across experimental networks. The greatest utility for disease gene prioritization is demonstrated by large comprehensive interactomes, and updated parsimonious composite networks (PCNets) are developed for enhanced performance. Signaling networks and AlphaFold-Multimer can be used for the prediction of interactions involving underrepresented functions, such as those involving transmembrane receptors. Accessible python code is provided for ongoing analysis of interactome resources. Systematic evaluation of 45 human interactomes offers insights and tools for network selection across biological applications.