Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories

TL;DR

This paper introduces a Bayesian decision tree method utilizing information criteria to classify single-molecule trajectories into different motion modes, enhancing understanding of membrane protein behaviors.

Contribution

It presents a novel decision tree approach combining BIC and AIC for classifying motion types in SMT data, validated through simulations and applied to experimental receptor trajectories.

Findings

BIC effectively distinguishes free from confined motion.

AIC further classifies confining potentials.

Receptor trajectories show confinement and disaggregation dynamics.

Abstract

Membrane proteins move in heterogeneous environments with spatially (sometimes temporally) varying friction and with biochemical interactions with various partners. It is important to reliably distinguish different modes of motion to improve our knowledge of the membrane architecture and to understand the nature of interactions between membrane proteins and their environments. Here, we present an analysis technique for single molecule tracking (SMT) trajectories that can determine the preferred model of motion that best matches observed trajectories. Information theory criteria, such as the Bayesian information criterion (BIC), the Akaike information criterion (AIC), and modified AIC (AICc), are used to select the preferred model. The considered group of models includes free Brownian motion, and confined motion in 2nd or 4th order potentials. We determine the best information criteria for classifying trajectories. We tested its limits through simulations matching large sets of experimental conditions and built a decision tree. This decision tree first uses the BIC to distinguish between free Brownian motion and confined motion. In a second step, it classifies the confining potential further using the AIC. We apply the method to experimental Clostridium Perfingens epsilon-toxin (CPepsilonT) receptor trajectories to show that these receptors are confined by a spring-like potential. An adaptation of this technique was applied on a sliding window in the temporal dimension along the trajectory. We applied this adaptation to experimental CPepsilonT trajectories that lose confinement due to disaggregation of confining domains. This new technique adds another dimension to the discussion of SMT data. The mode of motion of a receptor holds more biologically relevant information than the diffusion coefficient or domain size and may be a better tool to classify and compare different SMT experiments.