Large-scale protein identifications from highly complex protein mixtures have recently been achieved using multidimensional liquid chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) and subsequent database searching with algorithms such as SEQUEST. Here, we describe a probability-based evaluation of false positive rates associated with peptide identifications from three different human proteome samples. Peptides from human plasma, human mammary epithelial cell (HMEC) lysate, and human hepatocyte (Huh)-7.5 cell lysate were separated by strong cation exchange (SCX) chromatography coupled offline with reversed-phase capillary LC-MS/MS analyses. The MS/MS spectra were first analyzed by SEQUEST, searching independently against both normal and sequence-reversed human protein databases, and the false positive rates of peptide identifications for the three proteome samples were then analyzed and compared. The observed false positive rates of peptide identifications for human plasma were significantly higher than those for the human cell lines when identical filtering criteria were used, suggesting that the false positive rates are significantly dependent on sample characteristics, particularly the number of proteins found within the detectable dynamic range. Two new sets of filtering criteria are proposed for human plasma and human cell lines, respectively, to provide an overall confidence of >95% for peptide identifications. The new criteria were compared, using a normalized elution time (NET) criterion (Petritis et al. Anal. Chem. 2003, 75, 1039-1048), with previously published criteria (Washburn et al. Nat. Biotechnol. 2001, 19, 242-247). The results demonstrate that the present criteria provide significantly higher levels of confidence for peptide identifications from mammalian proteomes without greatly decreasing the number of identifications.

The use of artificial neural networks (ANNs) is described for predicting the reversed-phase liquid chromatography retention times of peptides enzymatically digested from proteome-wide proteins. To enable the accurate comparison of the numerous LC/MS data sets, a genetic algorithm was developed to normalize the peptide retention data into a range (from 0 to 1), improving the peptide elution time reproducibility to approximately 1%. The network developed in this study was based on amino acid residue composition and consists of 20 input nodes, 2 hidden nodes, and 1 output node. A data set of approximately 7000 confidently identified peptides from the microorganism Deinococcus radiodurans was used for the training of the ANN. The ANN was then used to predict the elution times for another set of 5200 peptides tentatively identified by MS/MS from a different microorganism (Shewanella oneidensis). The model was found to predict the elution times of peptides with up to 54 amino acid residues (the longest peptide identified after tryptic digestion of S. oneidensis) with an average accuracy of approximately 3%. This predictive capability was then used to distinguish with high confidence isobar peptides otherwise indistinguishable by accurate mass measurements as well as to uncover peptide misidentifications. Thus, integration of ANN peptide elution time prediction in the proteomic research will increase both the number of protein identifications and their confidence.

The diagnosis of inherited disorders of amino acids (AA) metabolism is usually performed on automated analysers by ion-exchange chromatography and quantification after ninhydrin derivatisation of about 50 different AA. A single run liquid chromatography/tandem mass spectrometry (LC/MS/MS) method for these molecules can be an alternative to this time-consuming technique. The first step of this development is the infusion study of the fragmentation of 79 molecules of biological interest in electrospray ionisation tandem mass spectrometry (ESI-MS/MS), in positive and in negative ionisation mode. Among them, three molecules can be detected only in negative ionisation mode, 38 only in positive mode and 38 in the two modes. All the most abundant fragmentations are presented, with optimisation of the MS/MS parameters. The positive ionisation mode was retained for the simultaneous analysis of 76 molecules. One sensitive and/or specific transition is proposed for the monitoring of each molecule. Improvement in sensitivity of detection was obtained with the use of an acidic mobile phase. Flow injection analysis studies led us to highlight a number of interferences-due to isobaric molecules, to in-source collision-induced dissociation, or to natural isotopic distribution of the elements-which are listed. For a reliable quantification method, these molecules have to be separated by LC before analysis in the tandem mass spectrometer. Ion-pairing reversed-phase liquid chromatography (RPLC) using perfluorinated carboxylic acids as ion-pairing agents has already been found suitable for analysis of AA in MS/MS positive ionisation mode and is under development.