A whole genome shotgun sequence of T. gondii has been generated by The Institute for Genomic Research (TIGR; now the J. Craig Venter Institute) at 126 coverage (available at http:// www.toxodb.org), and additional genome sequence is available from the Sanger Centre (http://www.sanger.ac.uk/Projects/ Protozoa). Initially, three gene prediction algorithms, TigrScan [6], TwinScan [7], and GlimmerHMM [6], were employed by TIGR and ToxoDB.org to identify genes in the ME49 strain of T. gondii. TigrScan and GlimmerHMM are Generalized Hidden Markov Model, ab initio gene prediction methods. The two models differ in their underlying statistical methods; TigrScan employs weight matrices and Markov chains, while GlimmerHMM incorporates additional splice site models. TwinScan combines comparative genomics and the probability model approach by integrating local genomic alignments from related species and ab initio methods. TwinScan was run using Eimeria tenella as the informant sequence. A fourth dataset of ME49-strain gene predictions was developed by ToxoDB and is currently available from version release 4.3 [8]. The Release4 predictions were produced using GLEAN [9], an algorithm that creates consensus gene predictions by integrating available experimental data such as expressed sequence tags (ESTs) and proteomics data. The computational gene prediction methods produce protein sequence sets that represent the theoretical proteome of Toxoplasma gondii; however, the datasets of predicted protein sequences are quite different and are likely to introduce substantial inaccuracy [10– 12]. Without experimental verifications it is not possible to assess whether any of the datasets offer a comprehensive view of the T. gondii proteome or which datasets, if any, are more accurate than the others. Large scale proteomics approaches have been used to analyze genomes of various organisms such as S. cerevisiae [13], M. mobile [14], C. parvum [15], T. gondii [16] and S. luteogriseus [17]. Targeted studies of T. gondii rhoptry [18], secretory [19], and micronemal [20] proteins highlight the value of applying proteomics to explore important subproteomes. Further, proteomics can be used to elucidate the role of post-translational modifications, such as N- glycosylation, in the function of important proteins [21]. We hypothesize that a systems-level analysis of the T. gondii proteome, using an approach that integrates proteomics and bioinformatics, will identify novel proteins that represent unique chemotherapeutic targets or have important biological functions during the obligate intracellular development of the parasite. We assembled a database comprising all computationally and experimentally derived sequences in an effort to capture the complete hypothetical proteome of T. gondii. Functional annota- tion of the proteome, using motif prediction methods, helped to gain insight into the biological relevance of the predicted proteins. Comparative genomics identified those proteins that were unique to T. gondii. Finally, MS and EST data were mapped to the experimentally derived and computationally generated proteins to experimentally validate predicted sequences and to assess the accuracy of various gene prediction methods. Results High throughput proteomics analysis of T. gondii We explored the T. gondii proteome by performing tandem mass spectrometry (MS/MS) experiments on T. gondii plasma mem- brane, cytoskeletal and cytosolic protein preparations. A total of 252 MS/MS experiments provided 7,270 (203,990) unique (and redundant) proteolytic peptides with an average false discovery rate of approximately 2% based on peptide detection in a ‘‘decoy’’ database (Table 1). The decoy database was generated by Mascot Validation of Gene Predictions (www.matrixscience.com) in the following way: during the search, every time a protein sequence from the target database is tested, a random sequence of the same length is automatically generated and tested. The average amino acid composition of the random sequences is the same as the average composition of the target database. Peptides were searched against human and mycoplasma protein databases to remove potential contaminating peptides. The MS/MS peptides were searched against the hypothetical T. gondii proteome, comprised of all computationally predicted and experimental sequences, to identify experimentally supported sequences. Expressed sequence tag data was used to provide further experimental support for some proteins in the T. gondii proteome. All MS data and analysis is publicly accessible in full depth at the Web site of the Albert Einstein Biodefense Proteomics Research Center (http://www.fiserlab.org/biodefense and http:// www.fiserlab.org/epicdb ) and summaries have been deposited to the NIH maintained Resource Center for Biodefense Proteomics Research, website http://www.proteomicsresource.org/ and to ToxoDB (http://www.toxodb.org). Predicted and experimentally known proteins of T. gondii The hypothetical proteome, comprised of 30,197 amino acid sequences, combines predicted proteins from the TigrScan (8,336 sequences), TwinScan (7,588), Glimmer (4,954), and Release4 (7,793) datasets, and the available T. gondii sequences from the NCBI non-redundant protein database (1,526) (NR). The majority of the sequences in the combined dataset (94%) are less than 2,000 residues long though some sequences are as long as 14,514 residues. The averages, ranges, and distributions of the sequence lengths already suggest substantial differences among the protein datasets (Table 2, Fig. 1). Glimmer predicted sequences (1,077 residues average length) are, on average, much longer than the TigrScan, TwinScan, Release4, or NR sequences (681, 614, 719, and 510 residues average length, respectively). Furthermore, the maximum sequence length for TigrScan (9,696 residues) is substantially shorter than the maximum lengths of the TwinScan, Glimmer, Release4, or NR sequences (13,936, 14,514, 11,862, and 12,269 residues, respectively). The NR sequences have the shortest average length (510 residues) and the distribution shows the highest frequency of sequences of approximately 200– 300 residues. The median lengths for each distribution, compared among each dataset, mimic the characteristics of the differences in average lengths. The Glimmer sequences had the highest median length (695), followed by Release4 (472), TigrScan (457), TwinScan (395), and the NR sequences (341). The shorter average and median lengths of the NR sequences is not necessarily an inherent characteristic of T. gondii proteins but could be an artifact because the researched proteins are likely not a Table 1. Number, average length, and average MASCOT score for all peptides identified by MASCOT when the MS results were searched against the amino acid sequences in the combined dataset. Number of MS/MS experiments Number of identified MS/MS peptides redundant peptides unique peptides Average peptide length (residues) Average MASCOT ion score doi:10.1371/journal.pone.0003899.t001 252 203,990 7,270 15.3 63.14 PLoS ONE | www.plosone.org 2 December 2008 | Volume 3 | Issue 12 | e3899