Ashley L. McCormack

Bio: Ashley L. McCormack is an academic researcher from University of Washington. The author has contributed to research in topics: Mass spectrometry & Protein mass spectrometry. The author has an hindex of 16, co-authored 18 publications receiving 9173 citations.

Method to Correlate Tandem Mass Spectra of Modified Peptides to Amino Acid Sequences in the Protein Database

Abstract: A method to correlate uninterpreted tandem mass spectra of modified peptides, produced under low-energy (10-50 eV) collision conditions, with amino acid sequences in a protein database has been developed. The fragmentation patterns observed in the tandem mass spectra of peptides containing covalent modifications is used to directly search and fit linear amino acid sequences in the database. Specific information relevant to sites of modification is not contained in the character-based sequence information of the databases. The search method considers each putative modification site as both modified and unmodified in one pass through the database and simultaneously considers up to three different sites of modification. The search method will identify the correct sequence if the tandem mass spectrum did not represent a modified peptide. This approach is demonstrated with peptides containing modifications such as S-carboxymethylated cysteine, oxidized methionine, phosphoserine, phosphothreonine, or phosphotyrosine. In addition, a scanning approach is used in which neutral loss scans are used to initiate the acquisition of product ion MS/MS spectra of doubly charged phosphorylated peptides during a single chromatographic run for data analysis with the database-searching algorithm. The approach described in this paper provides a convenient method to match the nascent tandem mass spectra of modified peptides to sequences in a protein database and thereby identify previously unknown sites of modification.

Direct analysis and identification of proteins in mixtures by LC/MS/MS and database searching at the low-femtomole level.

Abstract: A method to directly identify proteins contained in mixtures by microcolumn reversed-phase liquid chromatography electrospray ionization tandem mass spectrometry (LC/MS/MS) is studied. In this method, the mixture of proteins is digested with a proteolytic enzyme to produce a large collection of peptides. The complex peptide mixture is then separated on-line with a tandem mass spectrometer, acquiring large numbers of tandem mass spectra. The tandem mass spectra are then used to search a protein database to identify the proteins present. Results from standard protein mixtures show that proteins present in simple mixtures can be readily identified with a 30-fold difference in molar quantity, that the identifications are reproducible, and that proteins within the mixture can be identified at low femtomole levels. Based on these studies, methodology has been developed for direct LC/MS/MS analysis of proteins enriched by immunoaffinity precipitation, specific interaction with a protein−protein fusion product, a...

Mining genomes: correlating tandem mass spectra of modified and unmodified peptides to sequences in nucleotide databases.

Abstract: The correlation of uninterpreted tandem mass spectra of modified and unmodified peptides, produced under low-energy (10-50 eV) collision conditions, with nucleotide sequences is demonstrated. In this method nucleotide databases are translated in six reading frames, and the resulting amino acid sequences are searched "on the fly" to identify and fit linear sequences to the fragmentation patterns observed in the tandem mass spectra of peptides. A cross-correlation function is then used to provide a measurement of similarity between the mass-to-charge ratios for the fragment ions predicted by amino acid sequences translated from the nucleotide database and the fragment ions observed in the tandem mass spectrum. In general, a difference greater than 0.1 between the normalized cross-correlation functions for the first- and second-ranked search results indicates a successful match between sequence and spectrum. Measurements of the deviation from maximum similarity employing the spectral reconstruction method are made. The search method employing nucleotide databases is also demonstrated on the spectra of phosphorylated peptides. Specific sites of modification are identified even though no specific information relevant to sites of modification is contained in the character-based sequence information of nucleotide databases.

Coronin Promotes the Rapid Assembly and Cross-linking of Actin Filaments and May Link the Actin and Microtubule Cytoskeletons in Yeast

Abstract: Coronin is a highly conserved actin-associated protein that until now has had unknown biochemical activities. Using microtubule affinity chromatography, we coisolated actin and a homologue of coronin, Crn1p, from Saccharomyces cerevisiae cell extracts. Crn1p is an abundant component of the cortical actin cytoskeleton and binds to F-actin with high affinity ( K d 6 × 10−9 M). Crn1p promotes the rapid barbed-end assembly of actin filaments and cross-links filaments into bundles and more complex networks, but does not stabilize them. Genetic analyses with a crn1Δ deletion mutation also are consistent with Crn1p regulating filament assembly rather than stability. Filament cross-linking depends on the coiled coil domain of Crn1p, suggesting a requirement for Crn1p dimerization. Assembly-promoting activity is independent of cross-linking and could be due to nucleation and/or accelerated polymerization. Crn1p also binds to microtubules in vitro, and microtubule binding is enhanced by the presence of actin filaments. Microtubule binding is mediated by a region of Crn1p that contains sequences (not found in other coronins) homologous to the microtubule binding region of MAP1B. These activities, considered with microtubule defects observed in crn1Δ cells and in cells overexpressing Crn1p, suggest that Crn1p may provide a functional link between the actin and microtubule cytoskeletons in yeast.

Probability-based protein identification by searching sequence databases using mass spectrometry data.

Abstract: Several algorithms have been described in the literature for protein identification by searching a sequence database using mass spectrometry data. In some approaches, the experimental data are peptide molecular weights from the digestion of a protein by an enzyme. Other approaches use tandem mass spectrometry (MS/MS) data from one or more peptides. Still others combine mass data with amino acid sequence data. We present results from a new computer program, Mascot, which integrates all three types of search. The scoring algorithm is probability based, which has a number of advantages: (i) A simple rule can be used to judge whether a result is significant or not. This is particularly useful in guarding against false positives. (ii) Scores can be compared with those from other types of search, such as sequence homology. (iii) Search parameters can be readily optimised by iteration. The strengths and limitations of probability-based scoring are discussed, particularly in the context of high throughput, fully automated protein identification.

Mass spectrometry-based proteomics

Abstract: Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

The Perseus computational platform for comprehensive analysis of (prote)omics data.

Abstract: A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

Quantitative analysis of complex protein mixtures using isotope-coded affinity tags

Abstract: We describe an approach for the accurate quantification and concurrent sequence identification of the individual proteins within complex mixtures. The method is based on a class of new chemical reagents termed isotope-coded affinity tags (ICATs) and tandem mass spectrometry. Using this strategy, we com- pared protein expression in the yeast Saccharomyces cerevisiae, using either ethanol or galactose as a carbon source. The measured differences in protein expression correlated with known yeast metabolic function under glucose-repressed conditions. The method is redundant if multiple cysteinyl residues are present, and the relative quantification is highly accurate because it is based on stable isotope dilution techniques. The ICAT approach should provide a widely applicable means to compare quantitatively glob- al protein expression in cells and tissues.

Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.

Abstract: We present a statistical model to estimate the accuracy of peptide assignments to tandem mass (MS/MS) spectra made by database search applications such as SEQUEST. Employing the expectation maximization algorithm, the analysis learns to distinguish correct from incorrect database search results, computing probabilities that peptide assignments to spectra are correct based upon database search scores and the number of tryptic termini of peptides. Using SEQUEST search results for spectra generated from a sample of known protein components, we demonstrate that the computed probabilities are accurate and have high power to discriminate between correctly and incorrectly assigned peptides. This analysis makes it possible to filter large volumes of MS/MS database search results with predictable false identification error rates and can serve as a common standard by which the results of different research groups are compared.