Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of p<b><i>I</i></b> predictability of peptides

Cargile, Benjamin; Talley, Dana; Stephenson, James L.

doi:10.1002/elps.200305722

Cited by 140 publications

(187 citation statements)

References 28 publications

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“…The only way to do so is by using other parameters than database scores only. Peptide retention time during chromatographic separation is one such a parameter, the isoelectric point of peptides, introduced recently by others, 19,22,31 is another. In this study, we have investigated whether the overall false positive rate in a large-scale experiment can be decreased by peptide IEF followed by elimination of outliers deviating from the expected pI.…”

Section: Discussionmentioning

confidence: 99%

In-Gel Isoelectric Focusing of Peptides as a Tool for Improved Protein Identification

et al. 2006

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In the analysis of proteins in complex samples, pre-fractionation is imperative to obtain the necessary depth in the number of reliable protein identifications by mass spectrometry. Here we explore isoelectric focusing of peptides (peptide IEF) as an effective fractionation step that at the same time provides the added possibility to eliminate spurious peptide identifications by filtering for pI. Peptide IEF in IPG strips is fast and sharply confines peptides to their pI. We have evaluated systematically the contribution of pI filtering and accurate mass measurements on the total number of protein identifications in a complex protein mixture (Drosophila nuclear extract). At the same time, by varying Mascot identification cutoff scores, we have monitored the false positive rate among these identifications by searching reverse protein databases. From mass spectrometric analyses at low mass accuracy using an LTQ ion trap, false positive rates can be minimized by filtering of peptides not focusing at their expected pI. Analyses using an LTQ-FT mass spectrometer delivers low false positive rates by itself due to the high mass accuracy. In a direct comparison of peptide IEF with SDS-PAGE as a pre-fractionation step, IEF delivered 25% and 43% more proteins when identified using FT-MS and LTQ-MS, respectively. Cumulatively, 2190 non redundant proteins were identified in the Drosophila nuclear extract at a false positive rate of 0.5%. Of these, 1751 proteins (80%) were identified after peptide IEF and FT-MS alone. Overall, we show that peptide IEF allows to increase the confidence level of protein identifications, and is more sensitive than SDS-PAGE.

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Section: Discussionmentioning

confidence: 99%

In-Gel Isoelectric Focusing of Peptides as a Tool for Improved Protein Identification

et al. 2006

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“…Electrofocusing of peptides was performed with IPGphor (Amersham Biosciences, GE Healthcare, Piscataway, NJ) until 20 kVh. After focusing, the strip was cut into 12 fractions, peptides were extracted as described previously (8) and desalted again to remove urea.…”

Section: Methodsmentioning

confidence: 99%

Identification of miR-193b Targets in Breast Cancer Cells and Systems Biological Analysis of Their Functional Impact

Leivonen

Rokka

Östling

et al. 2011

Molecular & Cellular Proteomics

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Identification of protein targets for microRNAs (miRNAs)is a significant challenge due to the complexity of miRNAmediated regulation. We have previously demonstrated that miR-193b targets estrogen receptor-␣ (ER␣) and inhibits estrogen-induced growth of breast cancer cells.Here, we applied a high-throughput strategy using quantitative iTRAQ (isobaric tag for relative and absolute quantitation) reagents to identify other target proteins regulated by miR-193b in breast cancer cells.iTRAQ analysis of pre-miR-193b transfected MCF-7 cells resulted in identification of 743 unique proteins, of which 39 were down-regulated and 44 up-regulated as compared with negative control transfected cells. Computationally predicted targets of miR-193b were highly enriched (sevenfold) among the proteins whose level of expression decreased after miR-193b transfection. Only a minority of these (13%) showed similar effect at the mRNA level illustrating the importance of post-transcriptional regulation. The most significantly repressed proteins were selected for validation experiments. These data confirmed 14 -3-3 (YWHAZ), serine hydroxyl transferase (SHMT2), and aldo-keto reductase family 1, member C2 (AKR1C2) as direct, previously uncharacterized, targets of miR-193b. Functional RNAi assays demonstrated that specific combinations of knockdowns of these target genes by siRNAs inhibited growth of MCF-7 cells, mimicking the effects of the miR-193b overexpression. Interestingly, the data imply that besides targeting ER␣, the miR-193b effects include suppression of the local production of estrogens and other steroid hormones mediated by the AKR1C2 gene, thus provoking two separate molecular mechanisms inhibiting steroid-dependent growth of breast cancer cells.In conclusion, we present here a proteomic screen to identify targets of miR-193b, and a systems biological approach to mimic its effects at the level of cellular phe- MicroRNAs (miRNAs)1 regulate gene expression post-transcriptionally by binding primarily to the 3Јuntranslated region (3ЈUTR) of their target mRNAs, resulting in mRNA destabilization or translational repression (1, 2). Genes encoding 1048 human miRNAs have so far been identified (miRBase v.16.0) (3), and miRNAs are predicted to regulate the expression of up to 60% of all human protein-encoding genes (4). A large number of bioinformatic methods have been developed for miRNA target prediction (1), but these are still highly unspecific and inaccurate because miRNAs typically contain an imperfect match to their target sequences. In addition, a single miRNA can target hundreds of proteins and a single protein can be influenced by multiple miRNAs (1, 5, 6). Hence, comprehensive understanding of the phenotypic effects of miRNAs at the level of the entire cell is currently difficult. mRNA profiling by microarrays has been widely used for miRNA target identification, but microarrays only detect the effects of miRNAs at the transcriptional level, and will miss targets repressed solely at the translational level. However, compare...

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“…In various applications we have shown that when peptide retention time prediction was combined with peptide/protein identification programs such as SEQUEST, the number of false positive identifications could be decreased and/or the number of confident peptide identifications from LC-MS/MS experiments 51-53 increased. Le Bihan et al 54 [57][58][59] demonstrated confident peptide identifications could be further enhanced by the application of information from isoelectric focusing fractionation as a first dimension in shotgun proteomics. The good correlations observed between predicted and experimental peptide pI values allowed pI information to be used as an additional filtering step to increase the confidence of peptide/protein identifications.…”

Section: Introductionmentioning

confidence: 99%

Improved Peptide Elution Time Prediction for Reversed-Phase Liquid Chromatography-MS by Incorporating Peptide Sequence Information

et al. 2006

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We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using ~345,000 non-redundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of ~1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis et al., Anal. Chem. 2003, 75, 1039-1048 and other previously reported models.

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Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides

Cited by 140 publications

References 28 publications

In-Gel Isoelectric Focusing of Peptides as a Tool for Improved Protein Identification

In-Gel Isoelectric Focusing of Peptides as a Tool for Improved Protein Identification

Identification of miR-193b Targets in Breast Cancer Cells and Systems Biological Analysis of Their Functional Impact

Improved Peptide Elution Time Prediction for Reversed-Phase Liquid Chromatography-MS by Incorporating Peptide Sequence Information

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