Prediction of lipid-interacting amino acid residues from sequence features.
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.
Wang
Liangjiang
L
Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina 29634, USA. liangjw@clemson.edu
Irausquin
Stephanie J
SJ
Yang
Jack Y
JY
D000595:Amino Acid Sequence; D000596:Amino Acids / Q000737:chemistry; D001185:Artificial Intelligence; D001665:Binding Sites; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D055808:Drug Discovery; D050356:Lipid Metabolism; D008055:Lipids / Q000737:chemistry; D008565:Membrane Proteins / Q000737:chemistry* / Q000378:metabolism*; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D012372:ROC Curve
D000596:Amino Acids; D008055:Lipids; D008565:Membrane Proteins