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Exploration of Artificial Intelligence-Based Spectral Libraries for Data Independent Acquisition in Complex Ocean Metaproteomic Analyses

ABSTRACT: Ocean metaproteomics provides valuable insights into the structure and function of marine microbial communities. Yet, ocean samples are challenging due to their extensive biological diversity that results in a very large number of peptides with a large dynamic range. This study characterized the capabilities of data independent acquisition (DIA) mode for use in ocean metaproteomic samples. Spectral libraries were constructed from discovered peptides and proteins using machine learning algorithms to remove incorporation of false positives in the libraries. When compared with 1-dimensional and 2-dimensional data dependent acquisition analyses (DDA), DIA outperformed DDA both with and without gas phase fractionation. We found that larger discovered protein spectral libraries performed better, regardless of the geographic distance between where samples were collected for library generation and where the test samples were collected. Moreover, the spectral library containing all unique proteins present in the Ocean Protein Portal outperformed smaller libraries generated from individual sampling campaigns. However, a spectral library constructed from all open reading frames in a metagenome was found to be too large to be workable, resulting in low peptide identifications due to challenges maintaining a low false discovery rate with such a large database size. Given sufficient sequencing depth and validation studies, spectral libraries generated from previously discovered proteins can serve as a community resource, saving resequencing efforts. The spectral libraries generated in this study are available at the Ocean Protein Portal for this purpose.

INSTRUMENT(S):

ORGANISM(S): Bacteria Environmental Samples <bacteria

DISEASE(S): Disease Free

SUBMITTER: Matthew McIlvin

LAB HEAD: Mak Saito

PROVIDER: PXD057948 | Pride | 2025-09-29

REPOSITORIES: Pride

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			Action	DRS
	191231_DIA_1000to1100_metzyme_st5_120m.mzML	Mzml
	191231_DIA_1000to1100_metzyme_st5_120m.raw	Raw
	191231_DIA_1000to1100_metzyme_st5_20m.mzML	Mzml
	191231_DIA_1000to1100_metzyme_st5_20m.raw	Raw
	191231_DIA_1000to1100_metzyme_st5_50m.mzML	Mzml

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Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.

Dataset Information

Exploration of Artificial Intelligence-Based Spectral Libraries for Data Independent Acquisition in Complex Ocean Metaproteomic Analyses

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