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-o

SUBMITTER: Ramana J 

PROVIDER: S-EPMC2813246 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature