Heather Desaire

Heather Desaire
  • Dean's Professor
  • Keith D. Wilner Chair in Chemistry

Contact Info

220D MRB
1450 Jayhawk Blvd
Lawrence, KS 66045


B.A., Grinnell College, 1997
Ph.D., University of California-Berkeley, 2001
Associate Scientist, Quintiles Inc., 2002, Kansas City, MO


Mass Spectrometry: Bioanalytical and Physical Organic Applications


Desaire Research Group Poster

Analytical Chemistry. Tandem mass spectrometry, HPLC-MS/MS, and CE-MS/MS. Organic reaction mechanisms and structural determination of glycoproteins.

Research in my group focuses on using mass spectrometry to study a variety of molecules, from large glycoproteins to simple bi-functional organic molecules. Structural information about the molecules is obtained using tandem mass spectrometry. In a tandem mass spectrometry experiment, the compound of interest is isolated inside the mass spectrometer; then, in a second step, it is fragmented. When the fragments are detected, a significant amount of structural information about the original compound is obtained. This technique can be used to characterize glycosylated proteins (see project 1) and small organic molecules, including pharmaceuticals (see project 2).

Project 1. Structural analysis of glycoproteins:

Glycoproteins are an important class of biological compounds. For example, gonadotropins, small glycoprotein hormones, regulate the activity of the pituitary. While these molecules are important biologically, they are very difficult to study, in part because of structural ambiguity of the carbohydrate on the protein. We take a novel approach to characterizing these compounds. First, the glycoprotein is subjected to enzymatic digestion, and glycopeptides are released. The resulting glycopeptides may be separated and are used in a variety of mass spectrometry studies. Different tandem mass spectrometric methods are explored to find an approach that provides the most structural information possible about the glycopeptides. By performing tandem mass spectrometry on the glycopeptides, we may be able to learn information about the glycoprotein structure that is not accessible using traditional approaches. Tandem mass spectrometry will be coupled with either HPLC (high-pressure liquid chromatography) or CE (capillary electrophoresis), so separation, detection, and analysis of the glycopeptides will be achieved in one step.

Project 2. Organic reaction mechanisms:

Another research focus in my group involves developing a "rule book" for the dissociation reactions observed in tandem mass spectrometry. Currently, there are no rules to explain how dissociations occur during tandem mass spectrometry (even though rules are available for higher-energy processes of electron impact or chemical ionization mass spectrometry.) We are developing a set of rules to explain when, where, and why dissociations occur, for various types of molecules. Using the fundamental principles of physical organic chemistry, hand-selected model compounds are used to study reaction mechanisms that occur during tandem mass spectrometry. Once rules that govern dissociation reactions are developed, tandem mass spectrometry will be more useful for researchers performing structural studies on small, organic molecules. One application of this work includes using our "rule book" to develop new approaches to studying pharmaceuticals. As a long-term goal, we will demonstrate that tandem mass spectra can be as effective as NMR (and much more efficient) to characterize certain types of metabolic byproducts of drugs.

Web-based tools developed and administered by the Desaire Group:

GlycoPep DB: (Used to assign glycan composition to MS data of glycopeptides)

GlycoPep ID: (Used to assign peptide composition to MS data of glycopeptides)

Selected Publications

Chua, A.E., Pfeifer, L.D., Sekera, E.R., Hummon, A.B., and Desaire, H. Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning. J. Am. Soc. Mass Spectrom. 2023, 34, 2775-2784. https://doi.org/10.1021/jasms.3c00295

Desaire, H., Chua, A.E., Isom, M., Jarosova, R., and Hua, D, Distinguishing academic science writing from humans or ChatGPT with over 99% accuracy using off-the-shelf machine learning tools. Cell Rep. Phys. Sci.,4 (2023), Article 101426. https://doi.org/10.1016/j.xcrp.2023.101426

Pfeifer, L.D.; Patabandige, M.W.; Desaire, H. Leveraging R (LevR) for fast processing of mass spectrometry data and machine learning: Applications analyzing fingerprints and glycopeptides. Frontiers in Analytical Science, 2022, 2. https://doi.org/10.3389/frans.2022.961592

Desaire, H., Go, E.P., Hua, D., Advances, obstacles, and opportunities for machine learning in proteomics, Cell Reports Physical Science,Volume 3, Issue 10,2022, 101069, ISSN 2666-3864, https://doi.org/10.1016/j.xcrp.2022.101069.

Desaire, H., How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning. Journal of Proteome Research 2022 21 (9), 2071-2074 DOI: 10.1021/acs.jproteome.2c00117

Desaire, H., et al., Exposing the Brain Proteomic Signatures of Alzheimer’s Disease in Diverse Racial Groups: Leveraging Multiple Data Sets and Machine LearningJ. Proteome Res. 2022, 21, 1095−1104

Zou, Z.C., et al., High level stable expression of recombinant HIV gp120 in glutamine synthetase gene deficient HEK293T cells. Protein Expression and Purification, 2021. 181: p. 9.

Weber, J.J., et al., Structural insight into the novel iron-coordination and domain interactions of transferrin-1 from a model insect, Manduca sexta. Protein Science, 2021. 30(2): p. 408-422.

Patabandige, M.W., E.P. Go, and H. Desaire, Clinically Viable Assay for Monitoring Uromodulin Glycosylation. Journal of the American Society for Mass Spectrometry, 2021. 32(2): p. 436-443.

Khan, M.J., et al., Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma. Journal of Alzheimers Disease, 2021. 79(3): p. 1327-1344.

Khan, M.J., et al., Dataset of why inclusion matters for Alzheimer's disease biomarker discovery in plasma. Data in Brief, 2021. 35: p. 8.

Hua, D. and H. Desaire, Improved Discrimination of Disease States Using Proteomics Data with the Updated Aristotle Classifier. Journal of Proteome Research, 2021. 20(5): p. 2823-2829.

Go, E.P., et al., The opportunity cost of automated glycopeptide analysis: case study profiling the SARS-CoV-2 S glycoprotein. Analytical and Bioanalytical Chemistry, 2021. 413(29): p. 7215-7227.

Desaire, H., M.W. Patabandige, and D. Hua, The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data. Analytical and Bioanalytical Chemistry, 2021. 413(6): p. 1583-1593.

Alam, S.M., et al., Antigenicity and Immunogenicity of HIV-1 Envelope Trimers Complexed to a Small-Molecule Viral Entry Inhibitor. Journal of Virology, 2020. 94(21): p. 24.

Patabandige, M.W., et al., Quantitative clinical glycomics strategies: A guide for selecting the best analysis approach. Mass Spectrometry Reviews: p. 21.

Awards & Honors

William T. Kemper Award for Excellence in Teaching


NSF Career Award


American Society for Mass Spectrometry (ASMS) Research Award


Eli Lilly Analytical Chemistry Academic Contacts Committee Travel Award