{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:24:32Z","timestamp":1767961472585,"version":"3.49.0"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"01n02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2005,2]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0129065705000116","type":"journal-article","created":{"date-parts":[[2005,5,6]],"date-time":"2005-05-06T08:00:10Z","timestamp":1115366410000},"page":"71-84","source":"Crossref","is-referenced-by-count":59,"title":["PROTEIN METAL BINDING RESIDUE PREDICTION BASED ON NEURAL NETWORKS"],"prefix":"10.1142","volume":"15","author":[{"given":"CHIN-TENG","family":"LIN","sequence":"first","affiliation":[{"name":"Brain Research Centre, University System of Taiwan; Department of Electrical and Control Engineering, National Chiao-Tung University, HsinChu, 300, Taiwan, R.O.C."}]},{"given":"KEN-LI","family":"LIN","sequence":"additional","affiliation":[{"name":"Department of Electrical and Control Engineering, National Chiao-Tung University, Computer Center of Chung-Hua University, HsinChu, 300, Taiwan, R.O.C."}]},{"given":"CHIH-HSIEN","family":"YANG","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics, National Yang-Ming University, Taipei, 115, Taiwan, R.O.C."}]},{"given":"I-FANG","family":"CHUNG","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics, National Yang-Ming University, Taipei, 115, Taiwan, R.O.C."}]},{"given":"CHUEN-DER","family":"HUANG","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, HsiuPing Institute of Technology, Dali, Taichung, 412, Taiwan, R.O.C."}]},{"given":"YUH-SHYONG","family":"YANG","sequence":"additional","affiliation":[{"name":"Brain Research Centre, University System of Taiwan; Institute of Bioinformatics, National Chiao-Tung University, HsinChu, 300, Taiwan, R.O.C."}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","unstructured":"M. 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