Accurate prediction of hot spot residues through physicochemical characteristics of amino acid sequences | Academic Article individual record
abstract

Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In this article, we investigated the problem of identifying hot spots using only physicochemical characteristics extracted from amino acid sequences. We first extracted 132 relatively independent physicochemical features from a set of the 544 properties in AAindex1, an amino acid index database. Each feature was utilized to train a classification model with a novel encoding schema for hot spot prediction by the IBk algorithm, an extension of the K-nearest neighbor algorithm. The combinations of the individual classifiers were explored and the classifiers that appeared frequently in the top performing combinations were selected. The hot spot predictor was built based on an ensemble of these classifiers and to work in a voting manner. Experimental results demonstrated that our method effectively exploited the feature space and allowed flexible weights of features for different queries. On the commonly used hot spot benchmark sets, our method significantly outperformed other machine learning algorithms and state-of-the-art hot spot predictors. The program is available at http://sfb.kaust.edu.sa/pages/software.aspx.

author list (cited authors)
Chen, P., Li, J., Wong, L., Kuwahara, H., Huang, J. Z., & Gao, X.
publication date
2013
publisher
Wiley Publisher
keywords
  • Humans
  • Protein Interaction Maps
  • Protein-protein Interaction
  • Databases, Protein
  • Animals
  • Juvenile Hormones
  • Amino Acids
  • Artificial Intelligence
  • Feature Selection
  • Algorithms
  • Amino Acid Sequence
  • Drosophila Proteins
  • Classification
  • Receptors, Erythropoietin
  • Proteins
  • Hot Spot Residue
  • Drosophila
  • Models, Molecular
  • Physicochemical Characteristic
altmetric score

1.25

citation count

31

PubMed Central ID
23504705
identifier
128596SE
Digital Object Identifier (DOI)
start page
1351
end page
1362
volume
81
issue
8