A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor

• Autores: Buena Maizon, Héctor; Barrantes, Francisco José
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Briefings in Bioinformatics Vol.23, No.1, 2022
• Materia: INTELIGENCIA ARTIFICIAL; APRENDIZAJE AUTOMÁTICO; APRENDIZAJE PROFUNDO; PROTEÍNA DE MEMBRANA; RECEPTOR DE NEUROTRANSMISORES; RECEPTOR DE ACETILCOLINA; COLESTEROL; SEGUIMIENTO DE PARTÍCULAS INDIVIDUALES; MICROSCOPÍA DE SUPERRESOLUCIÓN
• Institución: Pontificia Universidad Católica Argentina

Fil: Buena Maizon, Héctor. Pontificia Universidad Católica Argentina. Instituto de Investigaciones Biomédicas. Laboratorio de Neurobiología Molecular; Argentina
Fil: Buena Maizon, Héctor. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Barrantes, Francisco José. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Barrantes, Francisco José. Pontificia Universidad Católica Argentina. Instituto de Investigaciones Biomédicas. Laboratorio de Neurobiología Molecular; Argentina
Abstract: We present a concatenated deep-learning multiple neural network system for the analysis of single-molecule trajectories. We apply this machine learning-based analysis to characterize the translational diffusion of the nicotinic acetylcholine receptor at the plasma membrane, experimentally interrogated using superresolution optical microscopy. The receptor protein displays a heterogeneous diffusion behavior that goes beyond the ensemble level, with individual trajectories exhibiting more than one diffusive state, requiring the optimization of the neural networks through a hyperparameter analysis for different numbers of steps and durations, especially for short trajectories (<50 steps) where the accuracy of the models is most sensitive to localization errors. We next use the statistical models to test for Brownian, continuous-time random walk and fractional Brownian motion, and introduce and implement an additional, two-state model combining Brownian walks and obstructed diffusion mechanisms, enabling us to partition the two-state trajectories into segments, each of which is independently subjected to multiple analysis. The concatenated multi-network system evaluates and selects those physical models that most accurately describe the receptor’s translational diffusion. We show that the two-state Brownian-obstructed diffusion model can account for the experimentally observed anomalous diffusion (mostly subdiffusive) of the population and the heterogeneous single-molecule behavior, accurately describing the majority (72.5 to 88.7% for α-bungarotoxin-labeled receptor and between 73.5 and 90.3% for antibody-labeled molecules) of the experimentally observed trajectories, with only ~15% of the trajectories fitting to the fractional Brownian motion model.