2015/01/04 by Alessia Annibale, Annibale, Alessia, Anthony C. C. Coolen +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #82Bxx (Secondary) #92Bxx (Primary) #Bioinformatics and Genomic Networks #Computational Drug Discovery Methods #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Protein Structure and Dynamics #cond-mat.dis-nn #msc:82Bxx #msc:92Bxx
paper · pdf · doi:10.48550/arxiv.1501.00662
38 pages, 10 figures
arxiv created 2015/01/04 · openalex publication_date 2015/01/04 · arxiv updated 2015/01/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Protein interaction networks (PIN) are popular means to visualize the proteome. However, PIN datasets are known to be noisy, incomplete and biased by the experimental protocols used to detect protein interactions. This paper aims at understanding the connection between true protein interactions and the protein interaction datasets that have been obtained using the most popular experimental techniques, i.e. mass spectronomy (MS) and yeast two-hybrid (Y2H). We show that the most natural adjacency matrix of protein interaction networks has a separable form, and this induces precise relations between moments of the degree distribution and the number of short loops. These relations provide powerful tools to test the reliability of datasets and hint at the underlying biological mechanism with which proteins and complexes recruit each other.