The utility of mass spectrometry-based proteomic data for validation of novel alternative splice forms reconstructed from RNA-Seq data: a preliminary assessment

Abstract Background Most mass spectrometry (MS) based proteomic studies depend on searching acquired tandem mass (MS/MS) spectra against databases of known protein sequences. In these experiments, however, a large number of high quality spectra remain unassigned. These spectra may correspond to novel peptides not present in the database, especially those corresponding to novel alternative splice (AS) forms. Recently, fast and comprehensive profiling of mammalian genomes using deep sequencing (i.e. RNA-Seq) has become possible. MS-based proteomics can potentially be used as an aid for protein-level validation of novel AS events observed in RNA-Seq data. Results In this work, we have used publicly available mouse tissue proteomic and RNA-Seq datasets and have examined the feasibility of using MS data for the identification of novel AS forms by searching MS/MS spectra against translated mRNA sequences derived from RNA-Seq data. A significant correlation between the likelihood of identifying a peptide from MS/MS data and the number of reads in RNA-Seq data for the same gene was observed. Based on in silico experiments, it was also observed that only a fraction of novel AS forms identified from RNA-Seq had the corresponding junction peptide compatible with MS/MS sequencing. The number of novel peptides that were actually identified from MS/MS spectra was substantially lower than the number expected based on in silico analysis. Conclusions The ability to confirm novel AS forms from MS/MS data in the dataset analyzed was found to be quite limited. This can be explained in part by low abundance of many novel transcripts, with the abundance of their corresponding protein products falling below the limit of detection by MS.http://deepblue.lib.umich.edu/bitstream/2027.42/112811/1/12859_2010_Article_4302.pd

A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet

Abstract PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the "what and how" of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides. We also demonstrate how to evaluate the quality of model fit and select an appropriate model from several available alternatives. We illustrate the use of PeptideProphet in association with the Trans-Proteomic Pipeline, a free suite of software used for protein identification.http://deepblue.lib.umich.edu/bitstream/2027.42/112836/1/12859_2012_Article_5421.pd

Computational analysis of unassigned high-quality MS/MS spectra in proteomic data sets

In a typical shotgun proteomics experiment, a significant number of high-quality MS/MS spectra remain “unassigned.” The main focus of this work is to improve our understanding of various sources of unassigned high-quality spectra. To achieve this, we designed an iterative computational approach for more efficient interrogation of MS/MS data. The method involves multiple stages of database searching with different search parameters, spectral library searching, blind searching for modified peptides, and genomic database searching. The method is applied to a large publicly available shotgun proteomic data set.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77526/1/2712_ftp.pd

Examination of the relationship between essential genes in PPI network and hub proteins in reverse nearest neighbor topology

Abstract Background In many protein-protein interaction (PPI) networks, densely connected hub proteins are more likely to be essential proteins. This is referred to as the "centrality-lethality rule", which indicates that the topological placement of a protein in PPI network is connected with its biological essentiality. Though such connections are observed in many PPI networks, the underlying topological properties for these connections are not yet clearly understood. Some suggested putative connections are the involvement of essential proteins in the maintenance of overall network connections, or that they play a role in essential protein clusters. In this work, we have attempted to examine the placement of essential proteins and the network topology from a different perspective by determining the correlation of protein essentiality and reverse nearest neighbor topology (RNN). Results The RNN topology is a weighted directed graph derived from PPI network, and it is a natural representation of the topological dependences between proteins within the PPI network. Similar to the original PPI network, we have observed that essential proteins tend to be hub proteins in RNN topology. Additionally, essential genes are enriched in clusters containing many hub proteins in RNN topology (RNN protein clusters). Based on these two properties of essential genes in RNN topology, we have proposed a new measure; the RNN cluster centrality. Results from a variety of PPI networks demonstrate that RNN cluster centrality outperforms other centrality measures with regard to the proportion of selected proteins that are essential proteins. We also investigated the biological importance of RNN clusters. Conclusions This study reveals that RNN cluster centrality provides the best correlation of protein essentiality and placement of proteins in PPI network. Additionally, merged RNN clusters were found to be topologically important in that essential proteins are significantly enriched in RNN clusters, and biologically important because they play an important role in many Gene Ontology (GO) processes.http://deepblue.lib.umich.edu/bitstream/2027.42/78257/1/1471-2105-11-505.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/2/1471-2105-11-505-S1.DOChttp://deepblue.lib.umich.edu/bitstream/2027.42/78257/3/1471-2105-11-505.pdfPeer Reviewe

Analysis of protein complexes through model‐based biclustering of label‐free quantitative AP‐MS data

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102630/1/msb201041.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102630/2/msb201041-sup-0001.pd

Untargeted, spectral libraryâ free analysis of dataâ independent acquisition proteomics data generated using Orbitrap mass spectrometers

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/1/pmic12370_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/2/pmic12370.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134139/3/pmic12370-sup-0001-SupplementaryInfo.pd

The functional interactome landscape of the human histone deacetylase family

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102187/1/msb201326-sup-0001.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102187/2/msb201326.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102187/3/msb201326.reviewer_comments.pd

Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5

Affinity purification coupled to mass spectrometry (AP-MS) represents a powerful and proven approach for the analysis of protein–protein interactions. However, the detection of true interactions for proteins that are commonly considered background contaminants is currently a limitation of AP-MS. Here using spectral counts and the new statistical tool, Significance Analysis of INTeractome (SAINT), true interaction between the serine/threonine protein phosphatase 5 (PP5) and a chaperonin, heat shock protein 90 (Hsp90), is discerned. Furthermore, we report and validate a new interaction between PP5 and an Hsp90 adaptor protein, stress-induced phosphoprotein 1 (STIP1; HOP). Mutation of PP5, replacing key basic amino acids (K97A and R101A) in the tetratricopeptide repeat (TPR) region known to be necessary for the interactions with Hsp90, abolished both the known interaction of PP5 with cell division cycle 37 homolog and the novel interaction of PP5 with stress-induced phosphoprotein 1. Taken together, the results presented demonstrate the usefulness of label-free quantitative proteomics and statistical tools to discriminate between noise and true interactions, even for proteins normally considered as background contaminants.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83479/1/1508_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/83479/2/pmic_201000770_sm_SupplInfo.pd

Comprehensive analysis of proteins of pH fractionated samples using monolithic LC/MS/MS, intact MW measurement and MALDI-QIT-TOF MS

A comprehensive platform that integrates information from the protein and peptide levels by combining various MS techniques has been employed for the analysis of proteins in fully malignant human breast cancer cells. The cell lysates were subjected to chromatofocusing fractionation, followed by tryptic digestion of pH fractions for on-line monolithic RP-HPLC interfaced with linear ion trap MS analysis for rapid protein identification. This unique approach of direct analysis of pH fractions resulted in the identification of large numbers of proteins from several selected pH fractions, in which approximately 1.5 µg of each of the pH fraction digests was consumed for an analysis time of ca 50 min. In order to combine valuable information retained at the protein level with the protein identifications obtained from the peptide level information, the same pH fraction was analyzed using nonporous (NPS)-RP-HPLC/ESI-TOF MS to obtain intact protein MW measurements. In order to further validate the protein identification procedures from the fraction digest analysis, NPS-RP-HPLC separation was performed for off-line protein collection to closely examine each protein using MALDI-TOF MS and MALDI-quadrupole ion trap (QIT)-TOF MS, and excellent agreement of protein identifications was consistently observed. It was also observed that the comparison to intact MW and other MS information was particularly useful for analyzing proteins whose identifications were suggested by one sequenced peptide from fraction digest analysis. Copyright © 2007 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55964/1/1163_ftp.pd