Software Lock Mass by Two-Dimensional Minimization of Peptide Mass Errors

Mass accuracy is a key parameter in proteomic experiments, improving specificity, and success rates of peptide identification. Advances in instrumentation now make it possible to routinely obtain high resolution data in proteomic experiments. To compensate for drifts in instrument calibration, a compound of known mass is often employed. This ‘lock mass’ provides an internal mass standard in every spectrum. Here we take advantage of the complexity of typical peptide mixtures in proteomics to eliminate the requirement for a physical lock mass. We find that mass scale drift is primarily a function of the m/z and the elution time dimensions. Using a subset of high confidence peptide identifications from a first pass database search, which effectively substitute for the lock mass, we set up a global mathematical minimization problem. We perform a simultaneous fit in two dimensions using a function whose parameterization is automatically adjusted to the complexity of the analyzed peptide mixture. Mass deviation of the high confidence peptides from their calculated values is then minimized globally as a function of both m/z value and elution time. The resulting recalibration function performs equal or better than adding a lock mass from laboratory air to LTQ-Orbitrap spectra. This ‘software lock mass’ drastically improves mass accuracy compared with mass measurement without lock mass (up to 10-fold), with none of the experimental cost of a physical lock mass, and it integrated into the freely available MaxQuant analysis pipeline (www.maxquant.org)

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages

Genomic Signature-Based Identification of Influenza A Viruses Using RT-PCR/Electro-Spray Ionization Mass Spectrometry (ESI-MS) Technology

BACKGROUND: The emergence and rapid spread of the 2009 H1N1 pandemic influenza A virus (H1N1pdm) in humans highlights the importance of enhancing the capability of existing influenza surveillance systems with tools for rapid identification of emerging and re-emerging viruses. One of the new approaches is the RT-PCR electrospray ionization mass spectrometry (RT-PCR/ESI-MS) technology, which is based on analysis of base composition (BC) of RT-PCR amplicons from influenza "core" genes. Combination of the BC signatures represents a "genomic print" of an influenza A virus. METHODOLOGY/PRINCIPAL FINDINGS: Here, 757 samples collected between 2006 and 2009 were tested, including 302 seasonal H1N1, 171 H3N2, 7 swine triple reassortants, and 277 H1N1pdm viruses. Of the 277 H1N1pdm samples, 209 were clinical specimens (throat, nasal and nasopharyngeal swabs, nasal washes, blood and sputum). BC signatures for the clinical specimen from one of the first cases of the 2009 pandemic, A/California/04/2009, confirmed it as an unusual, previously unrecognized influenza A virus, with "core" genes related to viruses of avian, human and swine origins. Subsequent analysis of additional 276 H1N1pdm samples revealed that they shared the genomic print of A/California/04/2009, which differed from those of North American swine triple reassortant viruses, seasonal H1N1 and H3N2 and other viruses tested. Moreover, this assay allowed distinction between "core" genes of co-circulating groups of seasonal H1N1, such as clades 2B, 2C, and their reassortants with dual antiviral resistance to adamantanes and oseltamivir. CONCLUSIONS/SIGNIFICANCE: The RT-PCR/ESI-MS assay is a broad range influenza identification tool that can be used directly on clinical specimens for rapid and accurate detection of influenza virus genes. The assay differentiates the H1N1pdm from seasonal and other nonhuman hosts viruses. Although not a diagnostic tool, this assay demonstrates its usefulness and robustness in influenza virus surveillance and detection of novel and unusual viruses with previously unseen genomic prints

Identification of subunit-subunit interactions in bacteriophage P22 procapsids by chemical cross-linking and mass spectrometry

Viral capsids are dynamic structures which self-assemble and undergo a series of structural transformations to form infectious viruses. The dsDNA bacteriophage P22 is used as a model system to study the assembly and maturation of icosahedral dsDNA viruses. The P22 procapsid, which is the viral capsid precursor, is assembled from coat protein with the aid of scaffolding protein. Upon DNA packaging, the capsid lattice expands and becomes a stable virion. Chemical cross-linking analyzed by mass spectrometry was used to identify residue specific inter- and intra-subunit interactions in the P22 procapsids. All the intersubunit cross-links occurred between residues clustered in a loop region (residues 157-207) which was previously identified by mass spectrometry based on hydrogen/deuterium exchange and biochemical experiments. DSP and BS3 which have similar distance constraints (12 A and 11.4 A, respectively) cross-linked the same residues between two subunits in the procapsids (K183-K183), whereas DST, a shorter cross-linker, cross-linked lysine 175 in one subunit to lysine 183 in another subunit. The replacement of threonine with a cysteine at residue 182 immediately adjacent to the K183 cross-linking site resulted in slow spontaneous disulfide bond formation in the procapsids without perturbing capsid integrity, thus suggesting flexibility within the loop region and close proximity between neighboring loop regions. To build a detailed structure model, we have predicted the secondary structure elements of the P22 coat protein, and attempted to thread the prediction onto identified helical elements of cryoEM 3D reconstruction. In this model, the loop regions where chemical cross-linkings occurred correspond to the extra density (ED) regions which protrude upward from the outside of the capsids and face one another around the symmetry axes.close323