Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition

BMC Bioinformatics. 2007 Nov 30;8:466. doi: 10.1186/1471-2105-8-466.


Background: Subcellular location prediction of proteins is an important and well-studied problem in bioinformatics. This is a problem of predicting which part in a cell a given protein is transported to, where an amino acid sequence of the protein is given as an input. This problem is becoming more important since information on subcellular location is helpful for annotation of proteins and genes and the number of complete genomes is rapidly increasing. Since existing predictors are based on various heuristics, it is important to develop a simple method with high prediction accuracies.

Results: In this paper, we propose a novel and general predicting method by combining techniques for sequence alignment and feature vectors based on amino acid composition. We implemented this method with support vector machines on plant data sets extracted from the TargetP database. Through fivefold cross validation tests, the obtained overall accuracies and average MCC were 0.9096 and 0.8655 respectively. We also applied our method to other datasets including that of WoLF PSORT.

Conclusion: Although there is a predictor which uses the information of gene ontology and yields higher accuracy than ours, our accuracies are higher than existing predictors which use only sequence information. Since such information as gene ontology can be obtained only for known proteins, our predictor is considered to be useful for subcellular location prediction of newly-discovered proteins. Furthermore, the idea of combination of alignment and amino acid frequency is novel and general so that it may be applied to other problems in bioinformatics. Our method for plant is also implemented as a web-system and available on

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amino Acid Sequence / physiology*
  • Artificial Intelligence*
  • Cluster Analysis
  • Computational Biology / methods
  • Databases, Protein
  • Internet
  • Intracellular Space / metabolism*
  • Intracellular Space / ultrastructure
  • Models, Biological
  • Pattern Recognition, Automated / methods
  • Plant Proteins / metabolism*
  • Plant Proteins / ultrastructure
  • Predictive Value of Tests
  • Protein Transport
  • Reproducibility of Results
  • Sequence Alignment / statistics & numerical data
  • Structure-Activity Relationship


  • Plant Proteins