Transport protein classification through structured prediction and multiple lernel learning

Membrane transport systems comprise roughly 10% of all proteins in a cell and play a critical role in many biological processes. Improving and expanding their classification is an important goal that can affect studies involving comparative and functional genomics, probing molecular mechanisms of diseases and metabolic processes, and searching new therapeutic targets and pharmacologically relevant transport proteins. In this context, a relevant classification problem is represented by the characterization of transport proteins according to the TC (Transporter Classification) data base (TCDB). In this work we introduce an integrative machine learning based approach that tries to consider all the above issues. To this end we propose a novel structured-output method able to explicitly consider the hierarchical relationships between TCDB categories. The proposed classifier exploits state-of-the-art Multiple Kernel Learning (MKL) strategies to integrate a very large set of features extracted from up-to-date databases and it is conceived to be applied virtually to any organism for the TCDB-wide and proteome-wide prediction of the categories of transporters.