A comparative computational analysis of protein sequences and literature mining classify ‘orphan’ neurotransmitter transporters

Various sources of protein data, such as knowledgebases and scientific literature, are currently available, as are numerous tools for their analysis. The matter becomes one of choosing the tools that are most appropriate for the specific task and for the specific proteins. A combination of standard and alternative tools may lead to biologically significant results.Here, a computational classification of proteins is made using standard multiple sequence alignment in combination with an alternative method for analysis of hydropathy distribution in proteins. Both of these methods are applied to the Na+/Cl−-dependent neurotransmitter symporters (NSSs), resulting in two alternative classifications. The classifications are validated and interpreted biologically by literature and knowledgebase annotation mining, producing a consensus classification. The classification leads to the identification and functional characterization of three families of largely structurally and functionally uncharacterized orphan NSSs. The literature and knowledgebase annotations are mined to functionally characterize the NSSs in these families. The presented work also demonstrates that, in specific cases, the analysis of the hydropathy distribution in proteins is capable of revealing functional properties of proteins.