title: Prediction of zinc-binding sites in proteins from sequence.
abstract: BACKGROUND
  Motivated by the abundance, importance and unique functionality of zinc, both biologically and physiologically, we have developed an improved method for the prediction of zinc-binding sites in proteins from their amino acid sequences.
  
  
  RESULTS
  By combining support vector machine (SVM) and homology-based predictions, our method predicts zinc-binding Cys, His, Asp and Glu with 75% precision (86% for Cys and His only) at 50% recall according to a 5-fold cross-validation on a non-redundant set of protein chains from the Protein Data Bank (PDB) (2727 chains, 235 of which bind zinc). Consequently, our method predicts zinc-binding Cys and His with 10% higher precision at different recall levels compared to a recently published method when tested on the same dataset.
  
  
  BACKGROUND
  The program is available for download at www.fos.su.se/~nanjiang/zincpred/download/
authors.affiliation: Structural Chemistry, Arrhenius Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden.
authors.forename: Nanjiang
authors.initials: N
authors.lastname: Shu
authors.forename: Tuping
authors.initials: T
authors.lastname: Zhou
authors.forename: Sven
authors.initials: S
authors.lastname: Hovmöller
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D008667:Metalloproteins / Q000737:chemistry*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D015032:Zinc / Q000737:chemistry*
chemical_list: D008667:Metalloproteins; D015032:Zinc