Automatic 3D protein structure classification without structural alignment

Zeyar Aung, Kian Lee Tan

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

In this paper, we present a new scheme named ProtClass for automatic classification of three-dimensional (3D) protein structures. It is a dedicated and unified multiclass classification scheme. Neither detailed structural alignment nor multiple binary classifications are required in this scheme. We adopt a nearest neighbor-based classification strategy. We use a filter-and-refine scheme. In the first step, we filter out the improbable answers using the precalculated parameters from the training data. In the second, we perform a relatively more detailed nearest neighbor search on the remaining answers. We use very concise and effective encoding schemes of the 3D protein structures in both steps. We compare our proposed method against two other dedicated protein structure classification schemes, namely SGM and CPMine. The experimental results show that ProtClass is slightly better in accuracy than SGM and much faster. In comparison with CPMine, ProtClass is much more accurate, while their running times are about the same. We also compare ProtClass against a structural alignment-based classification scheme named DALI, which is found to be more accurate, but extremely slow. The software is available upon request from the authors.

Original languageBritish English
Pages (from-to)1221-1241
Number of pages21
JournalJournal of Computational Biology
Volume12
Issue number9
DOIs
StatePublished - Nov 2005

Keywords

  • Abstract representation
  • Filter-and-refine
  • Nearest neighbor classification
  • Protein structure

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