Antibody-mediated biorecognition of myelin oligodendrocyte glycoprotein: computational evidence of demyelination-related epitopes

• Autor(a) principal: Ierich, Jéssica Cristiane Magalhães
• Data de Publicação: 2019
• Outros Autores: Brum, Doralina Guimarães [UNESP], Moraes, Ariana de Souza, Higa, Akemi Martins, Garcia, Pâmela Soto, Miyazaki, Celina Massumi, Ferreira, Marystela, Peroni, Luís Antonio, Oliveira, Guedmiller Souza de, Franca, Eduardo de Faria, Freitas, Luiz Carlos Gomide, Leite, Fabio Lima
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1038/s41598-018-36578-8; http://hdl.handle.net/11449/188729

Antigen-antibody interaction is crucial in autoimmune disease pathogenesis, as multiple sclerosis and neuromyelitis optica. Given that, autoantibodies are essential biomolecules, of which the myelin oligodendrocyte glycoprotein (MOG) can figure as a target. Here we combined Molecular Dynamics (MD), Steered Molecular Dynamics (SMD), and Atomic Force Microscope (AFM) to detail MOG recognition by its specific antibody. The complex model consisted of the MOG external domain interacting with an experimental anti-MOG antibody from the Protein Data Bank (1PKQ). Computational data demonstrated thirteen MOG residues with a robust contribution to the antigen-antibody interaction. Comprising five of the thirteen anchor residues (ASP 102 , HIS 103 , SER 104 , TYR 105 , and GLN 106 ), the well-known MOG 92–106 peptide in complex with the anti-MOG was analysed by AFM and SMD. These analyses evidenced similar force values of 780 pN and 765 pN for computational and experimental MOG 92–106 and anti-MOG detachment, respectively. MOG 92–106 was responsible for 75% of the total force measured between MOG external domain and anti-MOG, holding the interaction with the antibody. The antigen-antibody binding was confirmed by Surface Plasmon Resonance (SPR) measurements. Combined approaches presented here can conveniently be adjusted to detail novel molecules in diseases research. This can optimize pre-clinical steps, guiding experiments, reducing costs, and animal model usage.