# Cross-path reactive chromatography/mass spectrometry as a versatile platform for characterization of primary and higher order structure of complex heterogeneous proteins > **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $310,086 ## Abstract PROJECT SUMMARY High-throughput characterization of increasingly complex and heterogeneous protein structures (including both primary and higher order structures) is now required in a variety of fields ranging from personalized medicine (biomarkers) to industrial-scale production of recombinant proteins (for both product quality control and feedback adaptive process control). However, extensive structural characterization usually involves several multi-step processes that are both time- and labor-consuming, and frequently cannot be implemented in a high-throughput format. Additional complication arises from the presence of multiple protein sub-populations in the analytical/clinical/production sample, which may exhibit altered functional or biophysical properties despite having very similar structural characteristics (e.g, small soluble aggregates, aberrant glycoforms, disulfide- scrambled species, etc.). The proposed research aims at developing a robust and versatile analytical technology using the novel cross-path reactive chromatography (XP-RC) platform with on-line detection by electrospray ionization mass spectrometry (ESI MS) augmented by protein ion manipulation in the gas phase (including both conventional top-down MS/MS and the limited charge reduction technique developed in our laboratory). XP-RC allows protein chemical modifications (such as disulfide reduction, covalent labeling, H/D exchange, etc.) to be combined in-line with the separation step and enables real-time MS measurements that are not adversely affected by components incompatible with the ESI process. This is achieved by utilizing the unique elution characteristics (retention) of proteins and small-molecule reagents in non-denaturing chromatographic media (size exclusion or ion exchange); during their retention the proteins can be exposed to various reagents to induce the desired modification(s) in a highly controlled fashion. Our preliminary data provide strong evidence that multiple reactions can be carried out inside a single column in a sequential manner by exposing the protein to multiple reagent plugs as it moves through the column prior to MS detection/characterization of the modified protein. The initial efforts will be focused on implementing differential in-line reduction of disulfide bonds followed by free thiol capping with isotopically labeled reagents for high-throughput disulfide mapping and glycoform profiling (Aim 1). These efforts will be then extended to enable selective reduction of inter-chain disulfides while preserving the non-covalent interactions and internal disulfides in complex protein systems to enable identification of binding partners within such systems; MS/MS detection will allow binding interfaces within such selectively preserved complexes to be localized (Aim 2). An alternative approach will utilize in-line chemical labeling as a means of localizing the binding interfaces. Lastly, the XP-RC/MS platform will be used to implement dilution-f... ## Key facts - **NIH application ID:** 9878890 - **Project number:** 5R01GM132673-02 - **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST - **Principal Investigator:** IGOR A KALTASHOV - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $310,086 - **Award type:** 5 - **Project period:** 2019-03-01 → 2023-02-28 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9878890 ## Citation > US National Institutes of Health, RePORTER application 9878890, Cross-path reactive chromatography/mass spectrometry as a versatile platform for characterization of primary and higher order structure of complex heterogeneous proteins (5R01GM132673-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9878890. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*