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Computational Modeling for Understanding RAGE Activation

Undergraduate #101
Discipline: Biological Sciences
Subcategory: Microbiology/Immunology/Virology

Isis Christopher - Fisk University
Co-Author(s): Darlean Martin and David Graham, Fisk University, Nashville, TN



The receptor for advanced glycation end products (RAGE) is a critical cell surface pattern recognition receptor that is involved in inflammation. Misregulation of RAGE expression is connected to tumor outgrowths, diabetic complications, and neurodegenerative disorders, which are increasing worldwide concerns. Hence, RAGE is a potential target for therapeutic intervention. Importantly, RAGE is able to bind to a diverse array of ligands such as DNA, glycated proteins, abeta peptide, and several members of the S100 class of EF-hand calcium binding proteins. Ligand binding to RAGE induces a cascade of cellular signaling events, which induces the expression of proinflammatory molecules and upregulates RAGE thus forming a positive feedback loop. Our goal is to elucidate the molecular determinants of ligand binding to RAGE in order to further our understanding of this fundamental signal transduction pathway. This can potentially have a broad impact toward treatment of inflammatory diseases. This project focuses on predicting the structure of the receptor-ligand complex of RAGE and S100A12 using a computational approach, and comparing this model with experimentally determined structures of similar RAGE complexes. The S100A12-RAGE structure was calculated using ZDOCK, a web based interface for protein-protein docking. The ten models with the lowest energy were analyzed using Chimera and PDBePISA, Interestingly, comparisons to other RAGE complex structures show that the binding surface of RAGE is conserved and comprised of a highly basic surface. Arginine 104 is a critical interfacing residue of the RAGE molecule along with glutamine 47, glutamine 67, glycine 69, and glycine 70. Validation of these results will be tested using mutational analysis coupled with a fluorescence-based binding assay.

Funder Acknowledgement(s): This work was supported by the National Science Foundation HBCU UP program, HRD1332491 and HRD1400969. I would also like to thank all of the members of the Damo Lab for insightful discussions.

Faculty Advisor: Steven Damo,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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