Optimal Collision Energies and Bioinformatics Tools for Efficient Bottom-up Sequence Validation of Monoclonal Antibodies

Abstract

Rigorous validation of amino acid sequence is fundamental in the characterization of original and biosimilar protein biopharmaceuticals. Widely accepted workflows are based on bottom-up mass spectrometry, and they often require multiple techniques and significant manual work. Here, we demonstrate that optimization of a set of tandem mass spectroscopy (MS/MS) collision energies and automated combination of all available information in the measurements can increase the sequence validated by one technique close to the inherent limits. We created a software (called “Serac”) that consumes results of the Mascot database search engine and identifies the amino acids validated by bottom-up MS/MS experiments using the most rigorous, industrially acceptable definition of sequence coverage (we term this “confirmed sequence coverage”). The software can combine spectra at the level of amino acids or fragment ions to exploit complementarity, provides full transparency to justify validation, and reduces manual effort. With its help, we investigated collision energy dependence of confirmed sequence coverage of individual peptides and full proteins on trypsin-digested monoclonal antibody samples (rituximab and trastuzumab). We found the energy dependence to be modest, but we demonstrated the benefit of using spectra taken at multiple energies. We describe a workflow based on 2–3 LC-MS/MS runs, carefully selected collision energies, and a fragment ion level combination, which yields ∼85% confirmed sequence coverage, 25%–30% above that from a basic proteomics protocol. Further increase can mainly be expected from alternative digestion enzymes or fragmentation techniques, which can be seamlessly integrated to the processing, thereby allowing effortless validation of full sequences.

Collision energies on QTof and Orbitrap instruments: How to make proteomics measurements comparable?

“…The precursor mass tolerance was 7 and 5 ppm, whereas fragment peak tolerance was 0.05 and 0.03 Da for QTof and Orbitrap measurements, respectively 42 . The resulting Mascot output files (.dat) were subject to further analysis by our recently developed program called Serac 10 …”

“…2152 peptides were identified in these runs, although not necessarily every peptide in every run (see Table 1). 8 On the Thermo Orbitrap four-fifths of them were doubly charged, and most of the rest were triply charged, with a few examples for quadruply charged peptides (see Table 1). These species span the full hydrophobicity range typical for tryptic peptides (see retention time data in Table S1).…”

“…Next, the collision energy dependence of Mascot score for tryptic peptides was examined. Analogously to our previous work focusing on the Bruker QTof experiments, 8 we created normalized Mascot score versus collision energy curves for the Orbitrap HCD data. We processed 3130 peptides, approximately two-thirds of which were doubly charged and most of the rest were triply charged (see Table 1).…”

“…The b ions' intensity shows maximum at the lower energy optimum, whereas y ions are most abundant at the higher energy maximum, although the latter are dominant in the whole energy range. These findings led us to develop novel proteomics workflows via energy optimization, resulting in significantly better protein coverage, higher sequence coverage, 8,9 and more efficient sequence validation 10 than typical protocols using generic collision energy setup.…”

“…Recently, we have studied collision energy effects in a large scale proteomics study using a QTof mass spectrometer, which is one of the most frequently used instrument in this field. 8 We have characterized the quality of a CID spectrum by the identification score of the database search using the widely accepted Mascot search engine. For some peptides, it has been found that the Mascot score as a function of the collision energy showed an approximately Gaussian profile, with about 10 eV width at half-maximum ("unimodal behavior").…”