De novo derivation of proteomes from transcriptomes for transcript and protein identification
- Resource Type
- Authors
- Conrad Bessant; Gary L A Barker; Vanessa C. Evans; Kate J. Heesom; Jun Fan; David A. Matthews
- Source
- Nature methods
- Subject
- Proteomics
Proteome
Sequence analysis
CHO Cells
Biology
Arginine
Polymorphism, Single Nucleotide
Biochemistry
Article
Adenoviridae
Transcriptome
03 medical and health sciences
Cricetulus
Sequence Analysis, Protein
Tandem Mass Spectrometry
Cricetinae
Animals
Humans
RNA, Messenger
Databases, Protein
Molecular Biology
030304 developmental biology
Genetics
Carbon Isotopes
0303 health sciences
Nitrogen Isotopes
Lysine
030302 biochemistry & molecular biology
Nuclear Proteins
RNA-Binding Proteins
Chromatography liquid
Cell Biology
Protein identification
Software
Chromatography, Liquid
HeLa Cells
Biotechnology
- Language
- ISSN
- 1548-7105
1548-7091
Identification of proteins by tandem mass spectrometry requires a database of the proteins that could be in the sample. This is available for model species (e.g. humans) but not for non-model species. Ideally, for a non-model species the sequencing of expressed mRNA would generate a protein database for mass spectrometry based identification, allowing detection of genes and proteins using high throughput sequencing and protein identification technologies. Here we use human cells infected with human adenovirus as a complex and dynamic model to demonstrate this approach is robust. Our Proteomics Informed by Transcriptomics technique identifies >99% of over 3700 distinct proteins identified using traditional analysis reliant on comprehensive human and adenovirus protein lists. This facilitates high throughput acquisition of direct evidence for transcripts and proteins in non-model species. Critically, we show this approach can also be used to highlight genes and proteins undergoing dynamic changes in post transcriptional protein stability.