Automatic Classification of Protein Sequences into Structure/Function Groups via Parallel Cascade Identification: A Feasibility Study

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• 作者：Michael J. Korenberg ; Robert David ; Ian W. Hunter and Jerry E. Solomon
• 关键词：Protein sequence classification ; Nonlinear system identification ; Binary sequences ; SARAH codes
• 刊名：Annals of Biomedical Engineering
• 出版年：2000
• 出版时间：July 2000
• 年：2000
• 卷：28
• 期：7
• 页码：803-811
• 全文大小：118 KB
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Biomedicine
  Biomedicine
  Biomedical Engineering
  Biophysics and Biomedical Physics
  Mechanics
  Biochemistry
  
• 出版者：Springer Netherlands
• ISSN：1573-9686

文摘

A recent paper introduced the approach of using nonlinear system identification as a means for automatically classifying protein sequences into their structure/function families. The particular technique utilized, known as parallel cascade identification (PCI), could train classifiers on a very limited set of exemplars from the protein families to be distinguished and still achieve impressively good two-way classifications. For the nonlinear system classifiers to have numerical inputs, each amino acid in the protein was mapped into a corresponding hydrophobicity value, and the resulting hydrophobicity profile was used in place of the primary amino acid sequence. While the ensuing classification accuracy was gratifying, the use of (Rose scale) hydrophobicity values had some disadvantages. These included representing multiple amino acids by the same value, weighting some amino acids more heavily than others, and covering a narrow numerical range, resulting in a poor input for system identification. This paper introduces binary and multilevel sequence codes to represent amino acids, for use in protein classification. The new binary and multilevel sequences, which are still able to encode information such as hydrophobicity, polarity, and charge, avoid the above disadvantages and increase classification accuracy. Indeed, over a much larger test set than in the original study, parallel cascade models using numerical profiles constructed with the new codes achieved slightly higher two-way classification rates than did hidden Markov models (HMMs) using the primary amino acid sequences, and combining PCI and HMM approaches increased accuracy. © 2000 Biomedical Engineering Society.PAC00: 8714Ee, 8715Cc, 3620Fz, 8715Aa