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...
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Formato: | Artículo |
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Oxford University Press
2022
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Acceso en línea: | https://repositorio.uca.edu.ar/handle/123456789/14114 |
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I33-R139-123456789-14114 |
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institution |
Universidad Católica Argentina |
institution_str |
I-33 |
repository_str |
R-139 |
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Repositorio Institucional de la Universidad Católica Argentina (UCA) |
language |
Inglés |
topic |
INTELIGENCIA ARTIFICIAL APRENDIZAJE AUTOMÁTICO APRENDIZAJE PROFUNDO PROTEÍNA DE MEMBRANA RECEPTOR DE NEUROTRANSMISORES RECEPTOR DE ACETILCOLINA COLESTEROL SEGUIMIENTO DE PARTÍCULAS INDIVIDUALES MICROSCOPÍA DE SUPERRESOLUCIÓN |
spellingShingle |
INTELIGENCIA ARTIFICIAL APRENDIZAJE AUTOMÁTICO APRENDIZAJE PROFUNDO PROTEÍNA DE MEMBRANA RECEPTOR DE NEUROTRANSMISORES RECEPTOR DE ACETILCOLINA COLESTEROL SEGUIMIENTO DE PARTÍCULAS INDIVIDUALES MICROSCOPÍA DE SUPERRESOLUCIÓN Buena Maizon, Héctor Barrantes, Francisco José A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
topic_facet |
INTELIGENCIA ARTIFICIAL APRENDIZAJE AUTOMÁTICO APRENDIZAJE PROFUNDO PROTEÍNA DE MEMBRANA RECEPTOR DE NEUROTRANSMISORES RECEPTOR DE ACETILCOLINA COLESTEROL SEGUIMIENTO DE PARTÍCULAS INDIVIDUALES MICROSCOPÍA DE SUPERRESOLUCIÓN |
description |
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. |
format |
Artículo |
author |
Buena Maizon, Héctor Barrantes, Francisco José |
author_facet |
Buena Maizon, Héctor Barrantes, Francisco José |
author_sort |
Buena Maizon, Héctor |
title |
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
title_short |
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
title_full |
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
title_fullStr |
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
title_full_unstemmed |
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
title_sort |
deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor |
publisher |
Oxford University Press |
publishDate |
2022 |
url |
https://repositorio.uca.edu.ar/handle/123456789/14114 |
work_keys_str_mv |
AT buenamaizonhector adeeplearningbasedapproachtomodelanomalousdiffusionofmembraneproteinsthecaseofthenicotinicacetylcholinereceptor AT barrantesfranciscojose adeeplearningbasedapproachtomodelanomalousdiffusionofmembraneproteinsthecaseofthenicotinicacetylcholinereceptor AT buenamaizonhector deeplearningbasedapproachtomodelanomalousdiffusionofmembraneproteinsthecaseofthenicotinicacetylcholinereceptor AT barrantesfranciscojose deeplearningbasedapproachtomodelanomalousdiffusionofmembraneproteinsthecaseofthenicotinicacetylcholinereceptor |
bdutipo_str |
Repositorios |
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1764820523904139264 |