Abstract
BackgroundIdentifying 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.ResultsThe 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.ConclusionAs 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.
Highlights
Identifying the active site of an enzyme is a crucial step in functional studies
A scoring function based on residue local network descriptors, which did not involve any sequence alignment of the proteins under study or any attribution of function to proteins, was calculated for each residue of a
Residues were labelled as catalytic when their resulting score was superior to a given threshold value, and the threshold was fitted in order to obtain a minimal false detection rate, or maximal precision
Summary
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. The distributions of different structural properties only [5] or in combination with physico-chemical properties of residues [6] have been studied These properties were e.g. integrated into a neural network algorithm, in order to predict active site residues over various proteins with known structures [7]. A similar approach was used by Petrova, so as to predict active sites using Support Vector Machine on different structural and conservation properties of protein residues [8] Another method, the 'Evolutionary Trace', relies on the hypothesis that important residues show slower mutation rates than non-functional residues in proteins and that, in three-dimensional structures, such residues are more likely to be clustered with each others than to be isolated in space [9,10,11]. Representation of protein structures as networks of interacting residues enabled efficient detection of protein functional sites from three-dimensional structures [14,15,16]
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