Human plasma proteome analysis by reversed sequence database search and molecular weight correlation based on a...

Area of Science:

• Proteomics
• Biochemistry
• Mass Spectrometry

Background:

• Shotgun proteomics involves protein fractionation, digestion, and peptide separation via liquid chromatography.
• Mass spectrometry generates numerous tandem mass spectra for protein identification through database searching.
• Current database search scores often lack sufficient confidence for accurate peptide identification.

Purpose of the Study:

• To develop a confident protein identification method for high-throughput analysis of the human proteome.
• To establish a filtering protocol for database searching using a simpler reference proteome.
• To apply this protocol for clustering the human plasma proteome.

Main Methods:

• Utilized Pseudomonas putida KT2440 proteome as a reference due to its simplicity and fewer modifications.
• Applied reversed sequence database searching for filtering the bacterial proteome.
• Correlated peptide data with molecular weight in 1-D gel electrophoresis band positions.
• Developed a characterization protocol to cluster the human plasma proteome into distinct groups.

Main Results:

• Successfully filtered the P. putida KT2440 proteome using the proposed database search strategy.
• Established criteria for clustering the human plasma proteome based on bacterial proteome data analysis.
• Demonstrated a rapid method for generating a higher confidence protein list for the human proteome.

Conclusions:

• The developed protein filtering method enhances confidence in high-throughput proteomic analyses.
• This approach is effective for identifying heavily modified and cleaved proteins in complex proteomes.
• Bacterial proteome data analysis provides a valuable strategy for refining human proteome identification protocols.