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Increasing the accuracy of single-molecule data analysis using tMAVEN.

Anjali R Verma¹, Korak Kumar Ray¹, Maya Bodick¹

¹Department of Chemistry, Columbia University, New York, NY 10027 USA.

Biorxiv : the Preprint Server for Biology

|August 30, 2023

Summary

Choosing the right kinetic model is crucial for analyzing single-molecule experiments. The tMAVEN software helps researchers select appropriate hidden Markov models (HMMs) to accurately interpret biomolecular dynamics and avoid misinterpretations.

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Area of Science:

Biophysics
Computational Biology
Biochemistry

Background:

Single-molecule experiments provide detailed kinetic information on biomolecular functional dynamics.
Accurate kinetic modeling, often using hidden Markov models (HMMs), is essential for interpreting this data.
The physico-chemical details of molecular mechanisms are often unknown, complicating model selection.

Approach:

Developed time-series Modeling, Analysis, and Visualization ENvironment (tMAVEN), an open-source software platform for single-molecule data analysis.
tMAVEN integrates pre-processing, kinetic modeling with multiple HMM types, and plotting capabilities.
Systematically investigated model-mechanism mismatches using simulated datasets with complications like molecular heterogeneity.

Key Points:

No single kinetic modeling strategy is universally appropriate for all experimental contexts.
Standard HMMs may only accurately capture the simplest molecular mechanisms.
Physico-chemical principles are needed to modify HMMs and avoid misinterpreting molecular heterogeneity.

Conclusions:

tMAVEN facilitates the side-by-side application of various kinetic models to single-molecule datasets.
Researchers can tailor modeling approaches to match biomolecular dynamics complexity.
This enhances the accuracy of single-molecule data analysis and reveals biophysical insights.