Cross-reactive immunity is a process by which an individual who was vaccinated by an unrelated vaccine or who has recovered from an infection from an unconnected pathogen in the past is seemingly protected against an infection by SARS-CoV-2. A key step for an individual to mount a successful immune response to a pathogenic infection is for a human antibody to recognize and bind to a specific epitope (fragment) from an antigenic protein from the infecting pathogen. Cross-reactivity by molecular mimicry may occur when an antibody fortuitously binds to an epitope from SARS-CoV-2 because of a structural similarity at the binding interface with the epitope for which it was intended. If verified, a rapid repurposing of drugs and vaccines designed for the other pathogens can be quickly validated and applied to the current pandemic. This project plans to use bioinformatic techniques to investigate how molecular mimicry may play a role in cross-reactive immunity.
The software pipeline will use the high-performance computing resources in the Chameleon cloud computing platform to run computationally-intensive molecular dynamics simulations within a machine learning framework and help identify occurrences of molecular mimicry in SARS-CoV-2. The pipeline can be divided into two main parts. The first part involves extracting useful features from structures of known complexes available from public databases such as Protein Data Bank (PDB). The second part involves building machine learning models from these features so that molecular mimicry, if present, can be detected in SARS-CoV-2.
The machine learning framework will result in reusable models of molecular mimicry and is expected to assist in vaccine development. If successful, the project can potentially (a) explain global disparities in hospitalizations and death rates; (b) lead to quick repurposing of drugs to fight the current pandemic; (c) be replicated for other pathogens; (d) lead to faster vaccine development; (e) impact development of novel bioinformatic strategies for the current and future pandemics.
An interdisciplinary team with expertise in computational biophysics, bioinformatics, machine learning, evolutionary biology, infectious diseases, computational epigenetics, glycobioogy, high-performance computing and software engineering will drive this project. All results will be made available through the project website at: http://biorg.cs.fiu.edu/lemom, including examples of molecular mimicry, software for replicating the experiments, and performance benchmarking results on Chameleon.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
