Open AccessPredicting small ligand binding sites in proteins using backbone structure
Author(s) -
Andrew J. Bordner
Publication year - 2008
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btn543
Subject(s) - binding site , protein data bank (rcsb pdb) , homology modeling , ligand (biochemistry) , computational biology , random forest , chemistry , homology (biology) , biological system , computer science , amino acid , biology , machine learning , stereochemistry , biochemistry , enzyme , receptor
Specific non-covalent binding of metal ions and ligands, such as nucleotides and cofactors, is essential for the function of many proteins. Computational methods are useful for predicting the location of such binding sites when experimental information is lacking. Methods that use structural information, when available, are particularly promising since they can potentially identify non-contiguous binding motifs that cannot be found using only the amino acid sequence. Furthermore, a prediction method that can utilize low-resolution models is advantageous because high-resolution structures are available for only a relatively small fraction of proteins.