Characterization of Graphs for Protein Structure Modeling and Recognition of Solubility
AbstractThis paper deals with the relations among structural, topological, and chemical properties of theE.Coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each E.Coliprotein is initially represented according to its known folded 3D shape. This step consists in representingthe available E.Coli proteins in terms of graphs. We rst analyze those graphs by considering puretopological characterizations, i.e., by analyzing the mass fractal dimension and the distribution underlyingboth shortest paths and vertex degrees. Results con rm the general architectural principles of proteins.Successively, we focus on the statistical properties of a representation of such graphs in terms of vectorscomposed of several numerical features, which we extracted from their structural representation. Wefound that protein size is the main discriminator for the solubility, while however there are other factorsthat help explaining the solubility degree. We nally analyze such data through a novel one-class classi er,with the aim of discriminating among very and poorly soluble proteins. Results are encouraging andconsolidate the potential of pattern recognition techniques when employed to describe complex biologicalsystems.Index terms| Protein analysis; Graph representation; Descriptors and complexity measures for graphs;One-class classi cation.