Increasing the accuracy of single-molecule data analysis using tMAVEN

Abstract

Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system. Unfortunately, researchers rarely know the physico-chemical details of this molecular mechanism a priori, which raises questions about how to select the most appropriate kinetic model for a given single-molecule dataset and what consequences arise if the wrong model is chosen. To address these questions, we have developed and used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), a comprehensive, open-source, and extensible software platform. tMAVEN can perform each step of the single-molecule analysis pipeline, from pre-processing to kinetic modeling to plotting, and has been designed to enable the analysis of a single-molecule dataset with multiple types of kinetic models. Using tMAVEN, we have systematically investigated mismatches between kinetic models and molecular mechanisms by analyzing simulated examples of prototypical single-molecule datasets exhibiting common experimental complications, such as molecular heterogeneity, with a series of different types of HMMs. Our results show that no single kinetic modeling strategy is mathematically appropriate for all experimental contexts. Indeed, HMMs only correctly capture the underlying molecular mechanism in the simplest of cases. As such, researchers must modify HMMs using physico-chemical principles to avoid the risk of missing the significant biological and biophysical insights into molecular heterogeneity that their experiments provide. By enabling the facile, side-by-side application of multiple types of kinetic models to individual single-molecule datasets, tMAVEN allows researchers to carefully tailor their modeling approach to match the complexity of the underlying biomolecular dynamics and increase the accuracy of their single-molecule data analyses. Statement of SignificanceThe power of time-dependent single-molecule biophysical experiments lies in their ability to uncover the molecular mechanisms governing experimental systems by computationally applying kinetic models to the data. While many software solutions have been developed to estimate the optimal parameters of such models, the results reported here show that the models themselves are often inherently mismatched with the molecular mechanisms they are being used to analyze. To investigate these mismatches and demonstrate how to best model the kinetics of a molecular mechanism, we have used time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), an open-source software platform we have developed that, among other features, enables the analysis of single-molecule datasets using different kinetic models within a single, extensible, and customizable pipeline.