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Amino Acids

, Volume 35, Issue 2, pp 321–327 | Cite as

Using pseudo amino acid composition to predict protein subcellular location: approached with amino acid composition distribution

  • J.-Y. Shi
  • S.-W. Zhang
  • Q. Pan
  • G.-P. Zhou
Article

Summary.

In the Post Genome Age, there is an urgent need to develop the reliable and effective computational methods to predict the subcellular localization for the explosion of newly found proteins. Here, a novel method of pseudo amino acid (PseAA) composition, the so-called “amino acid composition distribution” (AACD), is introduced. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented by a feature vector. Finally, the feature vectors of all sequences thus obtained are further input into the multi-class support vector machines to predict the subcellular localization. The results show that AACD is quite effective in representing protein sequences for the purpose of predicting protein subcellular localization.

Keywords: Protein subcellular localization – Amino acid composition distribution – Pseudo amino acid composition – Support vector machines 

Abbreviations:

AAC

amino acid composition

AACD

amino acid composition distribution

5CV

5-fold cross validation

DAG

directed acyclic graph

DPC

dipeptide composition

KNN

k-nearest neighbor

OVO

one-versus-one

OVR

one-versus-rest

PPC

polypeptide composition

PseAA

pseudo amino acid composition

RBF

radial basis function

SVM

support vector machines

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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • J.-Y. Shi
    • 1
  • S.-W. Zhang
    • 2
  • Q. Pan
    • 2
  • G.-P. Zhou
    • 3
  1. 1.School of Computer Science and EngneeringNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Department of Biological Chemistry and Molecular PharmacologyHarvard Medical SchoolBostonUSA

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