Effective Amino Acid Sequencing of Intact Filgrastim by Multimodal Mass Spectrometry with Topdownr.

Therapeutic proteins, known as biologicals, are an important and growing class of drugs for treatment of a series of human ailments. Amino acid sequence variants of therapeutic proteins can affect their safety and efficacy. Top-down mass spectrometry is well suited for the sequence analysis of intact therapeutic proteins. Fine-tuning of tandem mass spectrometry (MS/MS) fragmentation conditions is essential for maximizing the amino acid sequence coverage but is often time-consuming. We used topdownr, an automated and integrated multimodal approach to systematically assess high mass accuracy MS/MS fragmentation parameters to characterize filgrastim, a 19 kDa recombinant human granulocyte colony-stimulating factor used in treating neutropenia. A total of 276 different MS/MS conditions were systematically tested, including the following parameters: protein charge state, HCD and CID collision energy, ETD reaction time, ETD supplemental activation, and UVPD activation time. Stringent and accurate evaluation and annotation of the MS/MS data was achieved by requiring a fragment ion mass error of 5 ppm, considering reproducible N- and C-terminal fragment ions only, and excluding internal fragment ion assignments. We report the first EThcD and UVPD MS/MS analysis of intact filgrastim, and these two techniques combined resulted in 98% amino acid sequence coverage. By combining all tested fragmentation modes, we obtained near-complete amino acid sequence coverage (99.4%) of intact filgrastim.

FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts.

The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.