Study of the biological coverage of nanosurfaces by computational modeling: application in the development of nanoimunosensors

Study of the biological coverage of nanosurfaces by computational modeling: application in the development of nanoimunoresensors. In this work, computational molecular modeling techniques were applied to describe nanosurfaces functionalized with immune system biomolecules, correlating data from atomic force microscope experiments, molecular dynamics, and steered molecular dynamics simulations. The main goal of this research was to evaluate intermolecular forces involved in the antigen-antibody interaction on the nanosurfaces during the development of nanoimmunosensors for demyelinating diseases detection, especially neuromyelitisoptica. The neuromyelitisoptica is an autoimmune inflammation in which components of the immune system respond against optical nerves and spinal cord, resulting in demyelinating lesions. In the literature, studies have established anti-aquaporin 4 as an important biomarker for neuromyelitisoptica. Then, a nanoimmunosensor for anti-aquaporin 4 antibodies detection in neuromyelitisoptica patients serum via Atomic Force Microscopy is in development. This study requested a computational approach for describing the tridimensional structure of antibodies. The novel approach consisted of computer molecular modeling and engineering to perform successive substitutions in residues of the antigen interaction site. Tests carried out using antibody structures available in specialized data banks demonstrated the similarity of (48 ± 18) % and (65 ± 14) % for light and heavy chains, respectively, of the computationally generated models and experimental 3D structures of antibodies. Additionally, a statistical model was developed to prove the nanoimmunosensor sensing activity, which was useful to treat and interpret the experimental data. This statistical model was efficient to distinguish seropositive patients from seronegative subjects considering specific biomarkers related to neuromyelitisoptica and multiple sclerosis, providing a novel and more precise process for demyelinating disease diagnosis. (AU)