Analysis of peptides and proteins using sub-2 μm fully porous and sub 3-μm shell particles

The objective of this study was to evaluate the potential of sub-2 μm totally porous particles and sub-3 μm shell particles for peptide and protein analysis. Specific analytical strategies must be developed for these biomolecules as their importance in the pharmaceutical industry increases and as their structural complexity involves some issues when classical LC conditions are employed. Attention was paid on comparing these different columns in various LC conditions (different temperatures, gradient times, and mobile phase flow rates). The comparison of the different supports was assessed considering columns characteristics (quality of packing, silanol activity, pore size, totally porous or shell particles). In this article, peptides were first analyzed with both column technologies. Similar results to those achieved with low molecular weight compounds were obtained (peak capacity >100 for tgrad around 3 min and columns dimensions of 2.1 mm id × 50 mm), but specific conditions were required (elevated temperature and the use of a volatile ion-pairing reagent, namely TFA). For peptide analysis following tryptic digestion, the goal was to improve peak capacity and resolution because of the large number of generated peptides. For this purpose, longer columns packed with porous sub-2 μm or shell sub-3 μm particles (i.e., 150 mm) and gradient times (i.e., up to 30 min) were tested. On the other hand, proteins in their intact forms have higher molecular weights (MW > 5000 Da) and a tertiary structure, thus requiring different conditions in terms of stationary phase hydrophobicity (C4 vs. C18) and pore size (300 vs. 120 Å). In addition, there were issues with adsorption onto the LC system and/or the column itself. This study showed that proteins with MWs lower than 40,000 Da required chromatographic conditions close to those employed for peptide analysis. For larger proteins, a C4 300 Å stationary phase gave the best results, confirming theoretical predictions.