Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition

Krishna Kumar Kandaswamy, Ganesan Pugalenthi, Steffen Möller, Enno Hartmann, Kai Uwe Kalies, P. N. Suganthan, Thomas Martinetz

Research output: Contribution to journalArticlepeer-review

89 Scopus citations


Apoptosis is an essential process for controlling tissue homeostasis by regulating a physiological balance between cell proliferation and cell death. The subcellular locations of proteins performing the cell death are determined by mostly independent cellular mechanisms. The regular bioinformatics tools to predict the subcellular locations of such apoptotic proteins do often fail. This work proposes a model for the sorting of proteins that are involved in apoptosis, allowing us to both the prediction of their subcellular locations as well as the molecular properties that contributed to it. We report a novel hybrid Genetic Algorithm (GA)/Support Vector Machine (SVM) approach to predict apoptotic protein sequences using 119 sequence derived properties like frequency of amino acid groups, secondary structure, and physico-chemical properties. GA is used for selecting a near-optimal subset of informative features that is most relevant for the classification. Jackknife cross-validation is applied to test the predictive capability of the proposed method on 317 apoptosis proteins. Our method achieved 85.80% accuracy using all 119 features and 89.91% accuracy for 25 features selected by GA. Our models were examined by a test dataset of 98 apoptosis proteins and obtained an overall accuracy of 90.34%. The results show that the proposed approach is promising; it is able to select small subsets of features and still improves the classification accuracy. Our model can contribute to the understanding of programmed cell death and drug discovery. The software and dataset are available at demos/apoptosis/GASVM

Original languageEnglish (US)
Pages (from-to)1473-1479
Number of pages7
JournalProtein and Peptide Letters
Issue number12
StatePublished - Dec 2010
Externally publishedYes


  • Apoptosis protein
  • Genetic algorithm
  • Jackknife test
  • Subcellular localization
  • Support vector machine.

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry


Dive into the research topics of 'Prediction of apoptosis protein locations with genetic algorithms and support vector machines through a new mode of pseudo amino acid composition'. Together they form a unique fingerprint.

Cite this