title: Detection of protein catalytic residues at high precision using local network properties.
abstract: BACKGROUND
  Identifying the active site of an enzyme is a crucial step in functional studies. While protein sequences and structures can be experimentally characterized, determining which residues build up an active site is not a straightforward process. In the present study a new method for the detection of protein active sites is introduced. This method uses local network descriptors derived from protein three-dimensional structures to determine whether a residue is part of an active site. It thus does not involve any sequence alignment or structure similarity to other proteins. A scoring function is elaborated over a set of more than 220 proteins having different structures and functions, in order to detect protein catalytic sites with a high precision, i.e. with a minimal rate of false positives.
  
  
  RESULTS
  The scoring function was based on the counts of first-neighbours on side-chain contacts, third-neighbours and residue type. Precision of the detection using this function was 28.1%, which represents a more than three-fold increase compared to combining closeness centrality with residue surface accessibility, a function which was proposed in recent years. The performance of the scoring function was also analysed into detail over a smaller set of eight proteins. For the detection of 'functional' residues, which were involved either directly in catalytic activity or in the binding of substrates, precision reached a value of 72.7% on this second set. These results suggested that our scoring function was effective at detecting not only catalytic residues, but also any residue that is part of the functional site of a protein.
  
  
  CONCLUSIONS
  As having been validated on the majority of known structural families, this method should prove useful for the detection of active sites in any protein with unknown function, and for direct application to the design of site-directed mutagenesis experiments.
authors.affiliation: Structural Bioinformatics Group, Otto-Warburg Laboratory, Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, D-14195 Berlin, Germany. slama.p@gmail.com
authors.forename: Patrick
authors.initials: P
authors.lastname: Slama
authors.forename: Ioannis
authors.initials: I
authors.lastname: Filippis
authors.forename: Michael
authors.initials: M
authors.lastname: Lappe
mesh_terms: D000465:Algorithms; D000818:Animals; D002384:Catalysis; D020134:Catalytic Domain; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D008968:Molecular Conformation; D011487:Protein Conformation; D017510:Protein Folding; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods; D025801:Ubiquitin / Q000737:chemistry
chemical_list: D025801:Ubiquitin