FluShuffle and FluResort: new algorithms to identify reassorted strains of the influenza virus by mass spectrometry

Background: Influenza is one of the oldest and deadliest infectious diseases known to man. Reassorted strains of the virus pose the greatest risk to both human and animal health and have been associated with all pandemics of the past century, with the possible exception of the 1918 pandemic, resulting in tens of millions of deaths. We have developed and tested new computer algorithms, FluShuffle and FluResort, which enable reassorted viruses to be identified by the most rapid and direct means possible. These algorithms enable reassorted influenza, and other, viruses to be rapidly identified to allow prevention strategies and treatments to be more efficiently implemented.Results: The FluShuffle and FluResort algorithms were tested with both experimental and simulated mass spectra of whole virus digests. FluShuffle considers different combinations of viral protein identities that match the mass spectral data using a Gibbs sampling algorithm employing a mixed protein Markov chain Monte Carlo (MCMC) method. FluResort utilizes those identities to calculate the weighted distance of each across two or more different phylogenetic trees constructed through viral protein sequence alignments. Each weighted mean distance value is normalized by conversion to a Z-score to establish a reassorted strain.Conclusions: The new FluShuffle and FluResort algorithms can correctly identify the origins of influenza viral proteins and the number of reassortment events required to produce the strains from the high resolution mass spectral data of whole virus proteolytic digestions. This has been demonstrated in the case of constructed vaccine strains as well as common human seasonal strains of the virus. The algorithms significantly improve the capability of the proteotyping approach to identify reassorted viruses that pose the greatest pandemic risk. © 2012 Lun et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

ETISEQ – an algorithm for automated elution time ion sequencing of concurrently fragmented peptides for mass spectrometry-based proteomics

<p>Abstract</p> <p>Background</p> <p>Concurrent peptide fragmentation (i.e. shotgun CID, parallel CID or MS^E) has emerged as an alternative to data-dependent acquisition in generating peptide fragmentation data in LC-MS/MS proteomics experiments. Concurrent peptide fragmentation data acquisition has been shown to be advantageous over data-dependent acquisition by providing greater detection dynamic range and providing more accurate quantitative information. Nevertheless, concurrent peptide fragmentation data acquisition remains to be widely adopted due to the lack of published algorithms designed specifically to process or interpret such data acquired on any mass spectrometer.</p> <p>Results</p> <p>An algorithm called Elution Time Ion Sequencing (ETISEQ), has been developed to enable automated conversion of concurrent peptide fragmentation data acquisition data to LC-MS/MS data. ETISEQ generates MS/MS-like spectra based on the correlation of precursor and product ion elution profiles. The performance of ETISEQ is demonstrated using concurrent peptide fragmentation data from tryptic digests of standard proteins and whole influenza virus. It is shown that the number of unique peptides identified from the digests is broadly comparable between ETISEQ processed concurrent peptide fragmentation data and the data-dependent acquired LC-MS/MS data.</p> <p>Conclusion</p> <p>The ETISEQ algorithm has been designed for easy integration with existing MS/MS analysis platforms. It is anticipated that it will popularize concurrent peptide fragmentation data acquisition in proteomics laboratories.</p

Gas phase chemistry of organic anions involving isomerisation / by Kevin M. Downard.

Bibliography: leaves 185-199.xvii, 520 leaves : ill. ; 30 cm.Thesis (Ph.D.)--University of Adelaide, Dept. of Organic Chemistry, 199

Performance of the computer algorithm COMPLX for the detection of protein complexes in the mass spectra of simulated biological mixtures

The performance of the algorithm COMPLX for detecting protein-ligand or other macromolecular complexes has been tested for highly complex data sets. These data contain m/z values for ions of proteins of the SWISS-PROT database within simulated biological mixtures where each component shares a similar molecular weight and/or isoelectric point (pi). As many as 1600 ion signals were entered to challenge the algorithm to identify ion signals associated with a single protein complex that has been ionised and detected within a mass spectrometer. Despite the complexity of such data sets, the algorithm is shown to be able to identify the presence of individual bimolecular complexes. The output data can be re-evaluated by the user as necessary in light of any additional information that is known concerning the nature of predicted associations, as well as the quality of the data-set in terms of errors in m/z values as a direct consequence of the mass calibration or resolution achieved. The data presented illustrates that the best results are obtained when output results are ranked according to the largest continuous series of ion pairs detected for a protein or macromolecule and its complex for which the ligand mass is assigned the lowest mass error. Copyright © 2005 John Wiley & Sons, Ltd.Link_to_subscribed_fulltex

COMPLX: A computer algorithm for the detection of protein-ligand and other macromolecular complexes in mass spectra

A new algorithm has been designed and tested to identify protein, or any other macromolecular, complexes that have been widely reported in mass spectral data. The program takes advantage of the appearance of multiply charged ions that are common to both electrospray ionization and, to a lesser extent, matrix-assisted laser desorption/ionization (MALDI) mass spectra. The algorithm, known as COMPLX for the COMposition of Protein-Ligand compleXes, is capable of identifying complexes for any protein or macromolecule with a binding partner of molecular mass up to 100 000 Da. It does so by identifying ion pairs present in a mass spectrum that, when they share a common charge, have an m/z value difference that is an integer fraction of a ligand or binding partner molecular mass. Several additional criteria must be met in order for the result to be ranked in the output file including that all m/z values for ions of the protein or complex have progressively lower values as their assigned charge increases, the difference between the m/z values for adjacent charge states (z, z + 1) decrease as the assigned charge state increases, and the ratio of any two m/z values assigned to a protein or complex is equal to the inverse ratio of their charge. The entries that satisfy these criteria are then ranked according to the appearance of ions in the mass spectrum associated with the binding partner, the length of a continuous series of charges across any set of ions for a protein and complex and the lowest error recorded for the molecular mass of the ligand or binding partner. A diverse range of hypothetical and experimental mass spectral data were used to implement and test the program, including those recorded for antibody-peptide, protein-peptide and protein-heme complexes. Spectra of increasing complexity, in terms of the number of ions input, were also successfully analysed in which the number of input m/z values far exceeds the few associated with a macromolecular complex. Thus the program will be of value in a future goal of proteomics, where mass spectrometry already plays a central role, for the direct analysis of protein and other associations within biological extracts. Copyright © 2003 John Wiley & Sons, Ltd.Link_to_subscribed_fulltex