Bioinformatics Workflow for Gonococcal Proteomics

Methods Mol Biol. 2019:1997:185-205. doi: 10.1007/978-1-4939-9496-0_12.

Abstract

High-throughput quantitative proteomics unravels secrets of Neisseria gonorrhoeae biology by profiling proteome responses to environmental and endogenous cues and opens translational research paths through identification of vaccine candidates, drug targets/virulence factors, and biomarkers. Bioinformatics tools and databases are indispensable for downstream analysis of proteomic datasets to generate biologically meaningful outcomes. In this chapter, we present a workflow for proteomic data analysis with emphasis on publicly available resources, software systems, and tools that predict protein subcellular localization (CELLO, PSORTb v3.0, SOSUI-GramN, SignalP 4.1, LipoP 1.0, TMHMM 2.0) and functional annotation (EggNOG-mapper 4.5.1., DAVID v6.8, and KEGG) of N. gonorrhoeae proteins. This computational step-by-step procedure may help to foster new hypotheses and to provide insights into the structure-function relationship of proteins.

Keywords: Bioinformatics; CELLO; DAVID, KEGG; Data mining; EggNOG-mapper; Functional enrichment; LipoP; Neisseria gonorrhoeae; PSORTb; Pathway mapping; Quantitative proteomics; SOSUI-GramN; SignalP; Subcellular localization; TMHMM.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Bacterial Proteins / genetics*
  • Databases, Protein
  • Datasets as Topic
  • Gene Ontology
  • Molecular Sequence Annotation
  • Neisseria gonorrhoeae / genetics*
  • Proteome / genetics*
  • Proteomics / methods*
  • Software

Substances

  • Bacterial Proteins
  • Proteome