Toward a MALDI in-source decay (ISD) method for top-down analysis of protein footprinting.
Ruidong JiangDon L RempelMichael L GrossPublished in: European journal of mass spectrometry (Chichester, England) (2023)
Irreversible protein footprinting is a mass spectrometry-based approach in which solvent-accessible sites of a protein are modified to assess high-order protein structure. Structural insights can be gained by determining the position and extents of modification. The usual approach to obtain the "footprint" is to analyze the protein through bottom-up LC-MS/MS. In this approach, the proteins are digested to yield a mixture of peptides that are then separated by LC before locating the modification sites by MS/MS. This process consumes substantial amounts of time and is difficult to accelerate for applications that require quick and high-throughput analysis. Here, we describe employing matrix-assisted laser desorption/ionization (MALDI) in-source decay (ISD) to analyze a footprinted small test protein (ubiquitin) via a top-down approach. Matrix-assisted laser desorption/ionization is easily adapted for high-throughput analysis, and top-down strategies can avoid lengthy proteolysis and LC separation. We optimized the method with model peptides and then demonstrated its feasibility on ubiquitin submitted to two types of footprinting. We found that MALDI ISD can produce a comprehensive set of fragment ions for small proteins, affording footprinting information in a fast manner and giving results that agree with the established methods, and serve as a rough measure of protein solvent accessibility. To assist in the implementation of the MALDI approach, we developed a method of processing top-down ISD data.
Keyphrases
- mass spectrometry
- high throughput
- amino acid
- protein protein
- liquid chromatography
- ms ms
- binding protein
- small molecule
- high resolution
- primary care
- high performance liquid chromatography
- single cell
- liquid chromatography tandem mass spectrometry
- electronic health record
- risk assessment
- ionic liquid
- simultaneous determination
- health information
- deep learning
- high resolution mass spectrometry