Literature DB >> 24878498

PepExplorer: a similarity-driven tool for analyzing de novo sequencing results.

Felipe V Leprevost1, Richard H Valente2, Diogo B Lima1, Jonas Perales2, Rafael Melani3, John R Yates4, Valmir C Barbosa5, Magno Junqueira3, Paulo C Carvalho1.   

Abstract

Peptide spectrum matching is the current gold standard for protein identification via mass-spectrometry-based proteomics. Peptide spectrum matching compares experimental mass spectra against theoretical spectra generated from a protein sequence database to perform identification, but protein sequences not present in a database cannot be identified unless their sequences are in part conserved. The alternative approach, de novo sequencing, can make it possible to infer a peptide sequence directly from a mass spectrum, but interpreting long lists of peptide sequences resulting from large-scale experiments is not trivial. With this as motivation, PepExplorer was developed to use rigorous pattern recognition to assemble a list of homologue proteins using de novo sequencing data coupled to sequence alignment to allow biological interpretation of the data. PepExplorer can read the output of various widely adopted de novo sequencing tools and converge to a list of proteins with a global false-discovery rate. To this end, it employs a radial basis function neural network that considers precursor charge states, de novo sequencing scores, peptide lengths, and alignment scores to select similar protein candidates, from a target-decoy database, usually obtained from phylogenetically related species. Alignments are performed using a modified Smith-Waterman algorithm tailored for the task at hand. We verified the effectiveness of our approach using a reference set of identifications generated by ProLuCID when searching for Pyrococcus furiosus mass spectra on the corresponding NCBI RefSeq database. We then modified the sequence database by swapping amino acids until ProLuCID was no longer capable of identifying any proteins. By searching the mass spectra using PepExplorer on the modified database, we were able to recover most of the identifications at a 1% false-discovery rate. Finally, we employed PepExplorer to disclose a comprehensive proteomic assessment of the Bothrops jararaca plasma, a known biological source of natural inhibitors of snake toxins. PepExplorer is integrated into the PatternLab for Proteomics environment, which makes available various tools for downstream data analysis, including resources for quantitative and differential proteomics.
© 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

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Year:  2014        PMID: 24878498      PMCID: PMC4159663          DOI: 10.1074/mcp.M113.037002

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  48 in total

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2.  PepNovo: de novo peptide sequencing via probabilistic network modeling.

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Authors:  Jay W Fox; Solange M T Serrano
Journal:  Curr Pharm Des       Date:  2007       Impact factor: 3.116

4.  Spectral networks: a new approach to de novo discovery of protein sequences and posttranslational modifications.

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Journal:  Biotechniques       Date:  2007-06       Impact factor: 1.993

Review 5.  Tools and challenges for diversity-driven proteomics in Brazil.

Authors:  Magno Junqueira; Paulo Costa Carvalho
Journal:  Proteomics       Date:  2012-07-30       Impact factor: 3.984

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Authors:  M Mann; M Wilm
Journal:  Anal Chem       Date:  1994-12-15       Impact factor: 6.986

9.  An improved algorithm for matching biological sequences.

Authors:  O Gotoh
Journal:  J Mol Biol       Date:  1982-12-15       Impact factor: 5.469

10.  A new blood coagulation inhibitor from the snake Bothrops jararaca plasma: isolation and characterization.

Authors:  Anita M Tanaka-Azevedo; Aparecida S Tanaka; Ida S Sano-Martins
Journal:  Biochem Biophys Res Commun       Date:  2003-09-05       Impact factor: 3.575

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  9 in total

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Journal:  Anal Chem       Date:  2019-04-04       Impact factor: 6.986

2.  Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0.

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Journal:  Sci Data       Date:  2017-07-11       Impact factor: 6.444

6.  Proteomic Deep Mining the Venom of the Red-Headed Krait, Bungarus flaviceps.

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7.  Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications.

Authors:  B Blank-Landeshammer; I Teichert; R Märker; M Nowrousian; U Kück; A Sickmann
Journal:  mBio       Date:  2019-10-15       Impact factor: 7.867

8.  Novel Catalytically-Inactive PII Metalloproteinases from a Viperid Snake Venom with Substitutions in the Canonical Zinc-Binding Motif.

Authors:  Erika Camacho; Libia Sanz; Teresa Escalante; Alicia Pérez; Fabián Villalta; Bruno Lomonte; Ana Gisele C Neves-Ferreira; Andrés Feoli; Juan J Calvete; José María Gutiérrez; Alexandra Rucavado
Journal:  Toxins (Basel)       Date:  2016-10-12       Impact factor: 4.546

9.  ProAlanase is an Effective Alternative to Trypsin for Proteomics Applications and Disulfide Bond Mapping.

Authors:  Diana Samodova; Christopher M Hosfield; Christian N Cramer; Maria V Giuli; Enrico Cappellini; Giulia Franciosa; Michael M Rosenblatt; Christian D Kelstrup; Jesper V Olsen
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  9 in total

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