Protein metal binding residue prediction based on neural networks.
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.
Lin
Chin-Teng
CT
Brain Research Centre, University System of Taiwan, Department of Electrical and Control Engineering, National Chiao-Tung University, HsinChu 300, Taiwan, R.O.C. ctlin@mail.nctu.edu.tw
Lin
Ken-Li
KL
Yang
Chih-Hsien
CH
Chung
I-Fang
IF
Huang
Chuen-Der
CD
Yang
Yuh-Shyong
YS
D000595:Amino Acid Sequence; D000818:Animals; D001665:Binding Sites; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D008667:Metalloproteins / Q000737:chemistry*; D008958:Models, Molecular; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011487:Protein Conformation; D012680:Sensitivity and Specificity
D008667:Metalloproteins