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Proteomic Profiling and Protein Identification by MALDI-TOF Mass Spectrometry in Unsequenced Parasitic Nematodes

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      Abstract

      Lack of genomic sequence data and the relatively high cost of tandem mass spectrometry have hampered proteomic investigations into helminths, such as resolving the mechanism underpinning globally reported anthelmintic resistance. Whilst detailed mechanisms of resistance remain unknown for the majority of drug-parasite interactions, gene mutations and changes in gene and protein expression are proposed key aspects of resistance. Comparative proteomic analysis of drug-resistant and -susceptible nematodes may reveal protein profiles reflecting drug-related phenotypes. Using the gastro-intestinal nematode, Haemonchus contortus as case study, we report the application of freely available expressed sequence tag (EST) datasets to support proteomic studies in unsequenced nematodes. EST datasets were translated to theoretical protein sequences to generate a searchable database. In conjunction with matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF-MS), Peptide Mass Fingerprint (PMF) searching of databases enabled a cost-effective protein identification strategy. The effectiveness of this approach was verified in comparison with MS/MS de novo sequencing with searching of the same EST protein database and subsequent searches of the NCBInr protein database using the Basic Local Alignment Search Tool (BLAST) to provide protein annotation. Of 100 proteins from 2-DE gel spots, 62 were identified by MALDI-TOF-MS and PMF searching of the EST database. Twenty randomly selected spots were analysed by electrospray MS/MS and MASCOT Ion Searches of the same database. The resulting sequences were subjected to BLAST searches of the NCBI protein database to provide annotation of the proteins and confirm concordance in protein identity from both approaches. Further confirmation of protein identifications from the MS/MS data were obtained by de novo sequencing of peptides, followed by FASTS algorithm searches of the EST putative protein database. This study demonstrates the cost-effective use of available EST databases and inexpensive, accessible MALDI-TOF MS in conjunction with PMF for reliable protein identification in unsequenced organisms.

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      Most cited references 46

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      A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding

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        Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

        The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
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          Probability-based protein identification by searching sequence databases using mass spectrometry data.

          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.
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            Author and article information

            Affiliations
            [1 ]Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom
            [2 ]Liverpool School of Tropical Medicine, Liverpool, United Kingdom
            [3 ]Institute of Infection and Global Health, University of Liverpool, Liverpool, United Kingdom
            [4 ]Institute of Biological, Environmental and Rural Sciences (IBERS), Aberystwyth University, Aberystwyth, United Kingdom
            Stanford University, United States of America
            Author notes

            Conceived and designed the experiments: PM EJL DAW JEH PMB HHR. Performed the experiments: PM SP DAW MCP. Analyzed the data: PM EJL DAW MCP JEH HHR. Contributed reagents/materials/analysis tools: PM EJL DAW MCP. Wrote the paper: PM EJL JEH PMB HHR.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2012
            29 March 2012
            : 7
            : 3
            3315570
            22479418
            PONE-D-11-23950
            10.1371/journal.pone.0033590
            (Editor)
            Millares et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
            Counts
            Pages: 11
            Categories
            Research Article
            Agriculture
            Agrochemicals
            Pest Control
            Biology
            Biochemistry
            Proteomics
            Toxicology
            Veterinary Science
            Animal Management
            Veterinary Diseases
            Veterinary Parasitology
            Veterinary Toxicology

            Uncategorized

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