Prediction of zinc-binding sites in proteins from sequence.
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/
Shu
Nanjiang
N
Structural Chemistry, Arrhenius Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden.
Zhou
Tuping
T
Hovmöller
Sven
S
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*
D008667:Metalloproteins; D015032:Zinc