Fig. 1: Optimization of ECD, EID, and UVPD parameters in bottom-up experiments. | Nature Methods

Fig. 1: Optimization of ECD, EID, and UVPD parameters in bottom-up experiments.

From: Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage

Fig. 1: Optimization of ECD, EID, and UVPD parameters in bottom-up experiments.The alternative text for this image may have been generated using AI.

a, Experimental workflow. b–e, Number of PSMs and peptides identified in UVPD experiments varying the number of UV laser pulses at 3 mJ pulse−1 (b), UVPD experiments using four laser pulses and varying the pulse energy (c), EID experiments varying the irradiation time at 25 eV of electron energy (d), and ECD experiments varying the irradiation time at ~1 eV of electron energy (e). In UVPD and EID, b, y or a, c, x, z fragments were used for data analysis; c and z ions were used in the analysis of ECD data. Schematic diagram in a created in BioRender; Govender Kirkpatrick, M. https://biorender.com/qqloq0m (2025).

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