Determination of the molecular structures of B cell epitopes recognized by protective antibodies provides essential insights to guide rational vaccine design. Obtaining atomically-resolved maps that comprehensively cover the surface of the antigen represents an ideal goal that would provide invaluable information to guide vaccine development. While X-ray crystallography can provide atomically-resolved maps of epitopes, at the present time, it does not have sufficient throughput for deep analysis of the diversity of the antibody repertoire. Hydrogen exchange-mass spectrometry (HX-MS) is emerging as an effective tool for epitope mapping. While promising, the method is limited both by data analysis bottlenecks and limited spatial resolution that prevent it from achieving its full potential for high resolution, high throughput epitope mapping. This collaborative research between Dr. David Weis and Dr. Jeffrey Gray brings together their complementary expertise in HX-MS and protein modelling to produce new software tools to improve the accuracy, resolution, and throughput of HX-MS-based epitope mapping. The outcome will enable epitope-mapping pipelines capable of generating 10-20 atomically-resolved epitopes per week, allowing researchers to more fully define the repertoire of antibody responses to infectious agents and toxins. This research is significant because it will yield new tools to accelerate the rational design and testing of much-needed vaccines to counteract emerging infectious diseases and biothreat agents within NIAID's portfolio.
The specific aims of this proposal are to develop new algorithms and software for (1) rapid, fully-automated processing of HX-MS data, (2) fully-automated classification of HX-MS results, and (3) obtaining atomically- resolved epitopes from HX-MS data. The product of the proposed research will be new software tools that implement innovative algorithms. To accomplish Aim #1, algorithms that treat HX-MS data as two-dimensional images and adapt image comparison algorithms to identify and extract the shifted mass spectra will be developed. To accomplish Aim #2, significance testing based on the volcano plot method and classify the results using k-means clustering will be used. To accomplish Aim #3, medium-resolution HX-MS-mapped epitope will be used to constrain computational protein docking between the solved antigen structure and the modeled antibody using the Rosetta protein modeling suite. Through an existing collaboration with Dr. Nicholas Mantis sponsored by NIAID, the team has access to VHHs and an expanding library of solved structures of these VHHs bound to ricin toxin. The solved structures present a unique opportunity to independently refine and validate the epitope mapping pipeline based on solved structures.

Public Health Relevance

Vaccines stimulate the immune system to produce antibodies that recognize specific regions called epitopes on bacteria, viruses, and toxins. Information about the molecular structures of these epitopes can provide useful guidance to the development of new vaccines. The proposed research is relevant to public health because it is expected to contribute new tools to determine epitope structures more quickly.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AI135701-02
Application #
9610636
Study Section
Enabling Bioanalytical and Imaging Technologies Study Section (EBIT)
Program Officer
Gondre-Lewis, Timothy A
Project Start
2017-12-15
Project End
2019-11-30
Budget Start
2018-12-01
Budget End
2019-11-30
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of Kansas Lawrence
Department
Chemistry
Type
Schools of Arts and Sciences
DUNS #
076248616
City
Lawrence
State
KS
Country
United States
Zip Code
66045