Lipocalins constitute a ubiquitous family of small, robust and versatile extracellular proteins. The functional repertoire of lipocalins encompassess modulation of immune response, regulation of cellular homeostasis, clearance of endogenous and exogenous compounds, roles in pheromone transport, olfaction, invertebrate coloration and prostaglandin synthesis and are associated with environmental stresses in plants.We have collected the currently known lipocalin sequences in the database LipocalinDB which houses more than 400 sequences from 200 organisms.
More bewildering than the functional diversity of the lipocalins is the fact that despite being members of the same family, these are extremely disparate at the sequence level. Infact, the sequence identity may fall below 10 or 20% for lipocalins executing different physiological functions. The only filial tie that unites the family members is the three-dimensional structure. This comprises a binding cavity with high structural plasticity, composed of four structurally hypervariable loops mounted on a stable β-barrel scaffold. This confers upon lipocalins the ability to bind such a vast variety of ligands. Indeed, lipocalins are nature's magnum opus. However, this represents as much a challenge as a wonder. Thanks to the low sequence identity, the identification of members is a formidable task and crystallisation and structure solution remain the only certain ways of doing so.
In our efforts to solve this challenge, we have developed a new algorithm, LipocalinPred. This is an SVM-based prediction method to identify novel lipocalins using various protein features. The features used in this work include amino acid composition (AAC), Dipeptide Composition(DPC), PSSM profile and Secondary structure Composition (SSC). Consistent with our expectations, we obtained highly accurate predictions with SSC and SSC hybrid models.

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