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Computational modeling and description of autoantigens recognition process in Neuromyelitis Optica Spectrum Disorders

Grant number: 19/23403-0
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): June 01, 2020
Effective date (End): May 31, 2021
Field of knowledge:Biological Sciences - Biophysics - Molecular Biophysics
Principal researcher:Fabio de Lima Leite
Grantee:Bruna Cavalcante Galo
Home Institution: Centro de Ciências e Tecnologias para a Sustentabilidade (CCTS). Universidade Federal de São Carlos (UFSCAR). Sorocaba , SP, Brazil

Abstract

Neuromyelitis optica spectrum disorders (NMOSD) involve neurodegenerative and demyelinating diseases that affect the central nervous system (CNS). An important molecule recognized as an NMOSD biomarker is the anti-aquaporin-4 (anti-AQP4) antibody, which is associated with the initial autoimmune response during NMOSD against the aquaporin-4 (AQP4) water channel in CNS typical cells. However, some lacunae in the understanding of the mechanism underlying these diseases remain, especially regarding target regions of proteins injured during the exacerbated autoimmune response. In consideration of the experimental and computational contribution of nanotechnology in disease investigation, this project aims to characterize the molecular regions comprised of the antigen-antibody interaction related to NMOSD by computer methods. More precisely, we plan to detail the most interactive regions of the AQP4 protein during the recognition by the anti-AQP4 for the immunodominant epitope definition. This project will involve molecular modeling, docking molecular, and steered molecular dynamics (SMD) approaches for describing, at the nanoscale, the antigen-antibody complex and interaction. The expected results comprise refined models of NMOSD-related molecules, especially in terms of anti-AQP4 and its actuation during the CNS autoimmune response. This will directly contribute to the understanding of this important biomarker, which will provide paths to nanotechnological sensor development for patient diagnosis and novel strategies for treatment.

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