FLASHTagger: an open-source web application for ion type- and precursor mass-free protein identification in top-down mass spectrometry

The growing capacity to detect proteins and protein complexes in MS pose computational challenges in identifying them through Top-DownMS (TDMS). While alternative fragmentation methods such as ECD and UVPD open up multiple fragmentation pathways increasing sequence coverage, they also complicate the interpretation of fragment spectra. Recently developed protocols like complex-down MS often isolate protein complexes at once yielding multiplexed fragment spectra. Together with complex signal structure of TDMS spectra and frequent errors in deconvolution, they present challenges in correct precursor ion interpretation.

Addressing these issues, we present FLASHTagger, a high-sensitivity protein identification tool for TDMS platforms. Unlike most conventional database searches that work with a predefined set of fragment ion types and assume precursor ions of monomeric proteoform of a single kind, FLASHTagger employs a de novo sequence tag-based protein filtration algorithm that does not need ion type and precursor mass specifications. FLASHTagger takes deconvolved TD-MS/MS scans (currently deconvolved by FLASHDeconv) and a protein sequence database as inputs. On the deconvolved spectrum, high-scoring sequence tags are efficiently generated using multi-dimensional dynamic programming. The tags are used for fast searches to find the candidate proteins. For each matched protein, protein-level false discovery rates (FDRs) are calculated with a decoy database.

FLASHTagger can be configured for both targeted and discovery studies by specifying the database file input. For both modes, however, deconvolution and tag generation are done blindly (e.g., without sequence information). We first validated the targeted mode using monoclonal antibody standards, where FLASHTagger found tags corresponding to both the heavy and light chain CDR3 regions, demonstrating it can robustly identify heterodimers. To benchmark discovery mode, we analyzed three nTD (native TD) datasets which were generated by isolating and fragmenting (EChcD) native oligomeric states of VAMP2 (monomer, rat), semiSWEET (dimer, yeast), and AqpZ (tetramer, E. coli), respectively, directly from intact liposomes. The data was searched against a proteome database containing 1,775 E. coli membrane proteins plus the target proteins. For all cases, target proteins were identified as the top hits. The matched tags revealed various ion types including terminal b/y and c/z ion types and even internal ions. For AqpZ, we could identify the unmodified, as well as dimethylated proteoforms.

Current implementation of FLASHTagger focuses on the low complexity datasets, but analysis of complex datasets will be made available in near future as a part of our new proteoform search engine. The precursor independent feature of FLASHTagger would open up the gate toward data independent acquisition in TDP. FLASHTagger is deployed at https://abi-services.cs.uni-tuebingen.de/flashviewer/ (Figure 1).