A novel set of methods has been developed to characterize polytopic membrane proteins at the topological, organellar and functional level, in order to reduce the existing functional gap in the membrane proteome. Firstly, a novel clustering tool was implemented, named PROCLASS, to facilitate the manual curation of large sets of proteins, in readiness for feature extraction. TMLOOP and TMLOOP writer were implemented to refine current topological models by predicting membrane dipping loops. TMLOOP applies weighted predictive rules in a collective motif method, to overcome the inherent limitations of single motif methods. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity and 1,392 topological models described in the Swiss-Prot database were refined. The subcellular location (TMLOCATE) and molecular function (TMFUN) prediction methods rely on the TMDEPTH feature extraction method along data mining techniques. TMDEPTH uses refined topological models and amino acid sequences to calculate pairs of residues located at a similar depth in the membrane. Evaluation of TMLOCATE showed a normalized accuracy of 75% in discriminating between proteins belonging to the main organelles. At a sequence similarity threshold of 40%, TMFLTN predicted main functional classes with a sensitivity of 64.1-71.4%) and 70% of the olfactory GPCRs were correctly predicted. At a sequence similarity threshold of 90%, main functional classes were predicted with a sensitivity of 75.6-92.8%) and class A GPCRs were sub-classified with a sensitivity of 84.5%>-92.9%. These results reflect a direct association between the spatial arrangement of residues in the transmembrane regions and the capacity for polytopic membrane proteins to carry out their functions. The developed methods have for the first time categorically shown that the transmembrane regions hold essential information associated with a wide range of functional properties such as filtering and gating processes, subcellular location and molecular function
