title: LipocalinPred: a SVM-based method for prediction of lipocalins.
abstract: BACKGROUND
  Functional annotation of rapidly amassing nucleotide and protein sequences presents a challenging task for modern bioinformatics. This is particularly true for protein families sharing extremely low sequence identity, as for lipocalins, a family of proteins with varied functions and great diversity at the sequence level, yet conserved structures.
  
  
  RESULTS
  In the present study we propose a SVM based method for identification of lipocalin protein sequences. The SVM models were trained with the input features generated using amino acid, dipeptide and secondary structure compositions as well as PSSM profiles. The model derived using both PSSM and secondary structure emerged as the best model in the study. Apart from achieving a high prediction accuracy (>90% in leave-one-out), lipocalinpred correctly differentiates closely related fatty acid-binding proteins and triabins as non-lipocalins.
  
  
  CONCLUSIONS
  The method offers a promising approach as a lipocalin prediction tool, complementing PROSITE, Pfam and homology modelling methods.
authors.affiliation: Bioinformatics Laboratory, Structural and Computational Biology Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi, India. ramanj@icgeb.res.in
authors.forename: Jayashree
authors.initials: J
authors.lastname: Ramana
authors.forename: Dinesh
authors.initials: D
authors.lastname: Gupta
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D054834:Lipocalins / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software
chemical_list: D054834:Lipocalins; D011506:Proteins