Plant proteogenomics: from protein extraction to improved gene predictions

Area of Science:

• Proteogenomics
• Genomic Annotation
• Mass Spectrometry

Background:

• Traditional genome annotation heavily relies on ab initio prediction, leading to inaccuracies due to lack of experimental protein-level evidence.
• Mass spectrometry (MS)-based proteomics offers a solution by providing experimental validation for genomic sequences.

Purpose of the Study:

• To present a robust tool, the UCSD proteogenomic pipeline, for validating mapped peptides and improving genome annotation accuracy.
• To provide reliable methods for plant protein extraction and digestion suitable for proteogenomic analysis.

Main Methods:

• Mapping peptide-spectrum matches (PSMs) to the genome using the Inspect MS/MS database search tool.
• Assigning statistical significance using a target-decoy search approach to estimate false discovery rates (FDRs).
• Calculating spectral probabilities for precise false-positive rates (FPRs) and p-values for PSM rescoring.

Main Results:

• The UCSD proteogenomic pipeline effectively maps PSMs to the genome and assigns statistical significance with estimated FDRs.
• The pipeline offers an enhanced method for calculating FPRs and p-values, improving PSM reliability.
• Optimized methods for plant protein extraction and trypsin digestion yield high-quality samples for proteogenomic analysis.

Conclusions:

• The presented proteogenomic pipeline significantly improves the accuracy of genome annotation by incorporating experimental MS data.
• The developed methods address challenges in plant proteogenomics, enabling more reliable analysis of plant genomes.
• This work provides a valuable resource for researchers aiming to validate and refine genomic annotations.