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

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.

Bibliographic Details

Main Authors:	Buena Maizon, Héctor, Barrantes, Francisco José
Format:	Artículo biblioteca
Language:	eng
Published:	Oxford University Press 2022
Subjects:	INTELIGENCIA ARTIFICIAL, APRENDIZAJE AUTOMÁTICO, APRENDIZAJE PROFUNDO, PROTEÍNA DE MEMBRANA, RECEPTOR DE NEUROTRANSMISORES, RECEPTOR DE ACETILCOLINA, COLESTEROL, SEGUIMIENTO DE PARTÍCULAS INDIVIDUALES, MICROSCOPÍA DE SUPERRESOLUCIÓN,
Online Access:	https://repositorio.uca.edu.ar/handle/123456789/14114
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