Processing, analysing and modelling kinetic data in the era of high-throughput single-molecule biophysics

Abstract

Biomolecular reactions are often composed of multiple stochastic, reversible and branched transition paths over intermediates, leading to rich dynamics. Single-molecule biophysics has revolutionized our view of biology by revealing the heterogeneity in realized paths and pointing to the importance of rare events. The recent development of high-throughput single-molecule biophysics techniques now allow to quantitatively study this heterogeneity and characterize even the rarest kinetic events. Processing, analyzing and modelling high-throughput single-molecule data has been the focus of several reports, but are often difficult to implement for non-experts. Here, we provide a guide to extract the most from transitions in single-molecule biophysics data using a first-passage time framework and maximum likelihood estimation. We specifically focused on parameter sweeps in systems with one or two characteristic timescales, and show how they can be analyzed in terms of a minimal kinetic model and its dependence on enzyme/substrate concentration, force and temperature. We introduce a general framework to perform data-driven modelling on single- and two-state models and illustrate it with concrete examples. We also provide programs with graphical user interfaces to perform such analysis on raw data, in the hope that it will empower experimental single-molecule biophysicists to extract the most out of their data.