Published online 30 January 2008 Nucleic Acids Research, 2008, Vol. 36, No. 4 e22 doi:10.1093/nar/gkm848 Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection Ji Wan 1 , Shuli Kang 1 , Chuanning Tang 1 , Jianhua Yan 1 , Yongliang Ren 1 , Jie Liu1, Xiaolian Gao 2 , Arindam Banerjee 3 , Lynda B. M. Ellis4 and Tongbin Li1,* 1Department of Neuroscience, 3Department of Computer Science and Engineering and 4Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA and 2Department of Biology and Biochemistry, University of Houston, Houston, TX 77204, USA Received May 15, 2007; Revised August 28, 2007; Accepted September 26, 2007 ABSTRACT Meta-predictors make predictions by organizing and processing the predictions produced by several other predictors in a defined problem domain. A proficient meta-predictor not only offers better predicting performance than the individual predic- tors from which it is constructed, but it also relieves experimentally researchers from making difficult judgments when faced with conflicting results made by multiple prediction programs. As increas- ing numbers of predicting programs are being developed in a large number of fields of life sciences, there is an urgent need for effective meta-prediction strategies to be investigated. We compiled four unbiased phosphorylation site datasets, each for one of the four major serine/ threonine (S/T) protein kinase families—CDK, CK2, PKA and PKC. Using these datasets, we examined several meta-predicting strategies with 15 phos- phorylation site predictors from six predicting programs: GPS, KinasePhos, NetPhosK, PPSP, PredPhospho and Scansite. Meta-predictors con- structed with a generalized weighted voting meta- predicting strategy with parameters determined by restricted grid search possess the best perfor- mance, exceeding that of all individual predictors in predicting phosphorylation sites of all four kinase families. Our results demonstrate a useful decision-making tool for analysing the predictions of the various S/T phosphorylation site predictors. An implementation of these meta-predictors is available on the web at: http://MetaPred.umn.edu/ MetaPredPS/. INTRODUCTION The past few years have seen a boom in the development of prediction programs in a wide range of life science areas. The 2006 Web Server issue of Nucleic Acids Research (1) alone introduced nearly 150 web servers, a considerable proportion of which are prediction servers developed over the past 2 years. In many key problem domains, multiple prediction programs coexist. While each prediction program has its unique virtues and strengths, often times none of them is perfect—every program makes false predictions under certain circum- stances. When these programs make conﬂicting predic- tions, it is diﬃcult for bench researchers—the users of the programs—to arrive at sensible decisions, thus the original intention of the programs is defeated. In several important problem domains, eﬀorts have been made to assess and compare prediction programs independently (2–6). These eﬀorts assist users to determine which programs to ‘trust more’ based on the types of problems they have. In this series of studies, we take one step further on this issue: in addition to making an independent assessment of the predicting performance of several prediction programs in the deﬁned problem domains, we investigate strategies of combining the strengths of these predictors, called element predictors, to construct ‘meta-predictors’ whose perfor- mance may exceed that of any element predictor. In the previous study (7), we investigated the meta-prediction in the domain of protein subcellular localization problem. In the study described herein, we look at the meta- prediction in another important problem domain: the prediction of phosphorylation sites of four major families of serine/threonine (or S/T) protein kinases. Protein phosphorylation is the transfer of a phosphate group from a high-energy phosphate donor such as ATP, to an amino acid such as serine (S), threonine (T) or tyrosine (Y) of a protein, mediated by a protein kinase. *To whom correspondence should be addressed. Tel: +1 612 626 3481; Fax: +1 612 626 5009; Email: toli@biocompute.umn.edu The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. 2008 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.