Title: Prediction of lipid-interacting amino acid residues from sequence features

Authors: Liangjiang Wang, Stephanie J. Irausquin, Jack Y. Yang

Addresses: Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA. ' Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA. ' Department of Radiation Oncology, Massachusetts General Hospital Cancer Center and Harvard Medical School, Harvard University, Boston, Massachusetts 02114, USA

Abstract: Proteins and lipids are integral components of cell membranes, and play important roles in cell signalling. Alterations of normal protein-lipid recognition may cause various diseases. However, molecular mechanisms underlying protein-lipid recognition are still poorly understood. In this study, we have developed a support vector machine based approach for predicting lipid-interacting residues from amino acid sequence features. To the best of our knowledge, this is the first study that applies machine learning to sequence-based prediction of lipid-interacting residues in proteins. Our study provides useful information for understanding protein lipid interactions, and may lead to advances in drug discovery.

Keywords: lipid interacting residues; biochemical features; sequence-based prediction; support vector machines; SVMs; machine learning; amino acid residues; cell signalling; amino acid sequence features; proteins; protein-lipid interactions; drug discovery; lipids.

DOI: 10.1504/IJCBDD.2008.018707

International Journal of Computational Biology and Drug Design, 2008 Vol.1 No.1, pp.14 - 25

Published online: 14 Jun 2008 *

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