e22 Nucleic Acids Research, 2008, Vol. 36, No. 4 Protein phosphorylation is the most common form of post-translational modiﬁcation of proteins; it plays vital roles in the regulation of a variety of important cellular processes including metabolism, signal transduction pathways, transcription, translation, cell growth and cell diﬀerentiation. Only about 2% of the human proteome encode protein kinases, but more than 30% of the proteome are aﬀected by kinase-mediated phosphoryla- tion (8). Nearly half of human kinases have been linked to cancers and other diseases (9). Protein kinases are often classiﬁed into two broad categories based on the amino acid residues on which phosphorylation take place: S/T kinases and Y kinases. The most common S/T kinases are found in the four families: CDK, CK2, PKA and PKC. Kinases in these four families are responsible for about half of the known S/T kinase reactions taking place in eukaryotic organisms. Because experimental identiﬁcation of phosphorylation sites is labor-intensive and costly, accurate computational methods for predicting phosphorylation sites are very important. A large number of such computation programs have been developed. Some of these programs [including NetPhos (10), DISPHOS (11) and Berry et al.’s predicting programs (12)] are generic or non-speciﬁc predictors; i.e. they predict whether a candidate site is a phosphorylation site or not, but do not make predictions about which kinases are involved. Other programs [e.g. KinasePhos (13), Scansite (14), PHOSITE (15), PredPhospho (16), NetPhosK (17), PREDIKIN (18), GPS (19) and PPSP(20)] are kinase-speciﬁc prediction programs; they make predictions about whether a candidate site is a phosphorylation site of a certain kinase, or of a certain family or group of kinases. In terms of data features used, a vast majority of these prediction programs (including NetPhos, NetPhosK, KinasePhos, Scansite, PHOSITE, PredPhospho, GPS and PPSP) extract and use sequence features from the candidate phosphorylation sites [peptides typically of length between 7 and 50 surrounding the phosphoryla- table residues (S/T/Y)], while PREDIKIN takes advant- age of structural data from which interactions between residues in the substrate protein and corresponding residues in the kinase are derived, and DISPHOS utilizes features related to predicted disordered protein regions and predicted secondary structures. In terms of underlying classiﬁcation methods, some programs (e.g. Scansite) use scores calculated from position-speciﬁc score matrices (PSSM) as basis of making predictions, others apply clustering or segmentation methods (e.g. GPS and PHOSITE), or train hidden Markov models (HMM) (KinasePhos), logistic regression models (DISPHOS), artiﬁcial neural networks (ANN) (NetPhos, NetPhosK), support vector machines (SVM) (PredPhospho) or Bayesian-based models (PPSP) for making predictions. In this study, we choose several of these prediction tools as element predictors, and explore meta-predicting strate- gies for the phosphorylation site predicting problem for the four major families of S/T kinases: CDK, CK2, PKA and PKC. After compiling unbiased phosphorylation site datasets for these kinase families, we use these datasets to assess the predicting performance of these element PAGE 2 OF 11 predictors. Subsequently, we look at several strategies to develop meta-predictors for these kinase families. We show that a generalized weighted voting strategy with parameters determined by restricted grid search produced meta-predictors with predicting performances surpassing those of all element predictors for all four kinase families. A web server implementing these meta- predictors has been established and is accessible at the URL http://MetaPred.umn.edu/MetaPredPS/. MATERIALS AND METHODS Compilation of MetaPS06 datasets We compiled four unbiased datasets, each corresponding to one of the four major S/T kinase families: CDK, CK2, PKA and PKC, for the meta-prediction of the phosphor- ylation sites for these kinase families. These datasets are termed the MetaPS06 datasets. The compilation of these datasets consisted of four steps: (i) making an ‘include-all’ compilation of S/T phosphorylation sites; (ii) removing data used in the development of element predictors from the compilation; (iii) assigning class labels to the phosphorylation sites in the compilation and (iv) making four balanced datasets for the four kinase families (Figure 1). Step 1. Making ‘include-all’ compilation of S/T phosphor- ylation sites. The ‘include-all’ compilation of S/T phos- phorylation sites was assembled from three diﬀerent data sources: (i) Phospho.ELM (21), Version 5.0 (released in May 2006) (ii) PhosphoSite (22), obtained in July 2006 and (iii) Swiss-Prot Release 51.1 (release date: 14 November 2006). The data from Phospho.ELM and PhosphoSite include both non-speciﬁc phosphorylation sites (for which no information is provided about the kinases mediating the phosphorylation processes) and kinase-speciﬁc phosphorylation sites (for which the names of the kinases or of the kinase groups/families are provided). Only kinase-speciﬁc phosphorylation sites were included in the compilation. The S/T phosphoryla- tion site data from Swiss-Prot were extracted by searching in the ‘FT’/‘MOD_RES’ ﬁeld of the annotation using keywords ‘phosphoserine’ and ‘phosphothreonine’. Non- speciﬁc phosphorylation sites were eliminated, as well as sites whose annotations contain the following words: ‘by similarity’, ‘potential’, ‘probably’ or ‘partial’. In summary, 2195, 3159 and 1145 S/T phosphorylation sites were retrieved from these three data sources, respectively. These data were merged, resulting in 5626 non-redundant S/T phosphorylation sites. The term ‘phosphorylation site’ used here refers to phosphorylation by a single kinase. A ‘physical’ S/T site may be phos- phorylated by several kinases. If so, it is considered as several phosphorylation sites, one for each kinase. Step 2. Removing data used in the development of element predictors. To make unbiased datasets, all data used in the development of any element predictors must be excluded (7). In the original report of each element prediction program, the data used in the development