title: A generative model for protein contact networks.
abstract: In this paper, we present a generative model for protein contact networks (PCNs). The soundness of the proposed model is investigated by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian. To complement the analysis, we also study the classical topological descriptors, such as statistics of the shortest paths and the important feature of modularity. Our experiments show that the proposed model results in a considerable improvement with respect to two suitably chosen generative mechanisms, mimicking with better approximation real PCNs in terms of diffusion properties elaborated from the normalized Laplacian spectra. However, as well as the other network models, it does not reproduce with sufficient accuracy the shortest paths structure. To compensate this drawback, we designed a second step involving a targeted edge reconfiguration process. The ensemble of reconfigured networks denotes further improvements that are statistically significant. As an important byproduct of our study, we demonstrate that modularity, a well-known property of proteins, does not entirely explain the actual network architecture characterizing PCNs. In fact, we conclude that modularity, intended as a quantification of an underlying community structure, should be considered as an emergent property of the structural organization of proteins. Interestingly, such a property is suitably optimized in PCNs together with the feature of path efficiency.
authors.affiliation: a Department of Computer Science , Ryerson University , 350 Victoria Street, Toronto , ON , M5B 2K3 Canada .
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Livi
authors.affiliation: b Department of Information Engineering, Electronics, and Telecommunications , SAPIENZA University of Rome , Via Eudossiana 18, 00184 Rome , Italy .
authors.forename: Enrico
authors.initials: E
authors.lastname: Maiorino
authors.affiliation: c Department of Environment and Health , Istituto Superiore di Sanità , Viale Regina Elena 299, 00161 Rome , Italy .
authors.forename: Alessandro
authors.initials: A
authors.lastname: Giuliani
authors.affiliation: b Department of Information Engineering, Electronics, and Telecommunications , SAPIENZA University of Rome , Via Eudossiana 18, 00184 Rome , Italy .
authors.forename: Antonello
authors.initials: A
authors.lastname: Rizzi
authors.affiliation: a Department of Computer Science , Ryerson University , 350 Victoria Street, Toronto , ON , M5B 2K3 Canada .
authors.forename: Alireza
authors.initials: A
authors.lastname: Sadeghian
mesh_terms: D000465:Algorithms; D002352:Carrier Proteins / Q000737:chemistry* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
chemical_list: D002352:Carrier Proteins; D011506:Proteins
keywords: generative model; graph Laplacian; mesoscopic analysis; protein contact network