Protein metal binding residue prediction based on neural networks

Chin Teng Lin*, Ken Li Lin, Chih Hsien Yang, I. Fang Chung, Chuen Der Huang, Yuh Shyong Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


Over one-third of protein structures contain metal ions, which are the necessary elements in life systems. Traditionally, structural biologists were used to investigate properties of metalloproteins (proteins which bind with metal ions) by physical means and interpreting the function formation and reaction mechanism of enzyme by their structures and observations from experiments in vitro. Most of proteins have primary structures (amino acid sequence information) only; however, the 3-dimension structures are not always available. In this paper, a direct analysis method is proposed to predict the protein metal-binding amino acid residues from its sequence information only by neural networks with sliding window-based feature extraction and biological feature encoding techniques. In four major bulk elements (Calcium, Potassium, Magnesium, and Sodium), the metal-binding residues are identified by the proposed method with higher than 90% sensitivity and very good accuracy under 5-fold cross validation. With such promising results, it can be extended and used as a, powerful methodology for metal-binding characterization from rapidly increasing protein sequences in the future.

Original languageEnglish
Pages (from-to)71-84
Number of pages14
JournalInternational journal of neural systems
Issue number1-2
StatePublished - 1 Feb 2005


  • Artificial neural networks (ANNs)
  • Bioinformatics
  • Life elements
  • Metalloprotein


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