This paper deals with the relations among structural, topological, and chemical properties of
the E. coli proteome from the vantage point of the solubility/aggregation propensity of proteins. Each
E. coli protein is initially represented according to its known folded 3D shape. This step involves
representing the available E. coli proteins in terms of graphs. We first analyze those graphs by
considering pure topological characterizations, i.e., by analyzing the mass fractal dimension and the distribution
underlying both shortest paths and vertex degrees. Results confirm the general architectural principles of proteins.
Successively, we focus on the statistical properties of a representation of such graphs in terms of vectors composed of
several numerical features, which we extracted from their structural representation. We found that protein size is the main
discriminator for the solubility, while however there are other factors that help explaining the solubility degree. We finally
analyze such data through a novel one-class classifier, with the aim of discriminating among very and poorly soluble
proteins. Results are encouraging and consolidate the potential of pattern recognition techniques when employed to
describe complex biological systems.
Keywords: Protein analysis, graph representation, descriptors and complexity measures for graphs, one-class classification.
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