Abstract
BackgroundPhosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.ResultsWe characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information.ConclusionWhile local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at .
Highlights
Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention
We pursued two major themes: the analysis phosphorylation in a kinase family specific fashion, and to investigate whether phosphorylation sites are characterized by specific threedimensional (3D) structural motifs or epitopes constituted by amino acid residues that are not necessarily close in sequence, thereby providing additional information that can help in predicting phosphorylation sites for proteins with known structure or with available structural models
While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites
Summary
Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. The recognition of phosphorylation sites by specific kinases and the subsequent phosphorylation generally leads to an alteration of the structure, function, or protein binding properties of the target protein, which has evolved as a mechanism to respond to environmental changes via phosphorylation-triggered complex signaling networks and cascades and is playing a crucial role in the regulation of enzymes or transporters in metabolic processes [1,2,3,4]. Given the high number of candidate phosphorylation sites, efforts to experimentally identify and verify them all remain challenging. These difficulties motivated the development of computational methods to predict potential phosphorylation sites in silico. Recognizing that the information content increases significantly when the respective kinase families associated with their targets are considered separately, approaches to predict phosphorylation sites in a kinase-family specific manner based on family-specific local sequence motifs have been presented [5,6,7,8]
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