Validation and Community Sharing of Ocean Spectral Libraries Generated by Machine Learning for Data Independent Acquisition Ocean Metaproteomic Analyses

Area of Science:

• Marine biology
• Proteomics
• Bioinformatics

Background:

• Ocean metaproteomics reveals marine microbial community structure and function.
• Marine samples present challenges due to high biological diversity and large dynamic range of peptides.
• Data independent acquisition (DIA) is a mass spectrometry technique with potential for complex sample analysis.

Purpose of the Study:

• To evaluate the capabilities of DIA for ocean metaproteomic samples.
• To compare DIA performance against data dependent acquisition (DDA) methods.
• To assess the impact of spectral library size and composition on peptide identification.

Main Methods:

• Characterization of DIA mode for ocean metaproteomics.
• Construction of spectral libraries using machine learning (ML) to filter false positives.
• Comparison of DIA with 1D and 2D DDA, with and without gas phase fractionation.
• Evaluation of spectral libraries of varying sizes and origins (e.g., Ocean Protein Portal, metagenomic ORFs).

Main Results:

• DIA outperformed DDA methods in ocean metaproteomic analysis.
• Larger spectral libraries consistently yielded better results, irrespective of sample geographic origin.
• The Ocean Protein Portal (OPP) spectral library demonstrated superior performance compared to smaller, campaign-specific libraries.
• Extremely large libraries, such as those from all metagenomic ORFs, proved unworkable due to identification challenges.

Conclusions:

• DIA is a viable and effective technique for ocean metaproteomics.
• The size and content of spectral libraries are critical for successful peptide identification.
• Previously generated spectral libraries, like those in the OPP, can serve as valuable community resources, reducing redundant sequencing efforts.