A Machine Learning Approach for Prediction of Lipid-Interacting Residues in Amino Acid Sequences

Lipids perform many vital functions in the cell. Cellular levels of lipids are tightly regulated, and alterations in lipid metabolism can cause various human diseases such as inflammation, cancer and neurological disorders. Here, we present a method that takes an amino acid sequence as the only inpu...

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Bibliographic Details
Main Authors: Irausquin, S.J., Liangjiang Wang
Format: Conference Proceeding
Language:English
Subjects:
Amino acids
Biochemistry
Cancer
Diseases
Humans
lipid-interacting residues
Lipidomics
Machine learning
Proteins
sequence-based prediction
Support vector machine classification
Support vector machines
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Summary:Lipids perform many vital functions in the cell. Cellular levels of lipids are tightly regulated, and alterations in lipid metabolism can cause various human diseases such as inflammation, cancer and neurological disorders. Here, we present a method that takes an amino acid sequence as the only input and predicts lipid-interacting residues using support vector machines (SVMs). Protein sequence datasets with known lipid-interacting residues were chosen from the protein data bank (PDB). SVM classifiers were then constructed using data instances encoded with three sequence features. The results suggest that lipid-interacting residues can be predicted at 52.78% sensitivity and 70.84% specificity. To the best of our knowledge, this is the first study that utilizes a machine learning approach to predict lipid-interacting residues based on amino acid sequence data. Our study provides useful information for understanding protein-lipid interactions, and may lead to advances in drug discovery.
DOI:10.1109/BIBE.2007.4375582