Abstract
Atomic-level information is essential to explain the specific interactions governing protein–protein recognition in terms of structure and dynamics. Of particular interest is a characterization of the time-dependent kinetic aspects of protein–protein association and dissociation. A powerful framework to characterize the dynamics of complex molecular systems is provided by Markov State Models (MSMs). The central idea is to construct a reduced stochastic model of the full system by defining a set of conformational featured microstates and determining the matrix of transition probabilities between them. While a MSM framework can sometimes be very effective, different combinations of input featurization and simulation methods can significantly affect the robustness and the quality of the information generated from MSMs in the context of protein association. Here, a systematic examination of a variety of MSMs methodologies is undertaken to clarify these issues. To circumvent the uncertainties caused by sampling issues, we use a simplified coarse-grained model of the barnase–barstar protein complex. A sensitivity analysis is proposed to identify the microstates of an MSM that contribute most to the error in conjunction with the transition-based reweighting analysis method for a more efficient and accurate MSM construction.
Highlights
One of the most important questions in biology is how living cells communicate and respond to the flow of information at the molecule level
The free energies, mean first passage times (MFPT), and resultant binding constants and rates are within agreement, indicating that using just the one-dimensional COM distance can adequately capture the binding process of this protein complex
The kinetic rates and binding constants were calculated from the Markov State Models (MSMs) and demonstrate that the MSM methodology is robust and consistent
Summary
One of the most important questions in biology is how living cells communicate and respond to the flow of information at the molecule level. While the protein–protein equilibrium binding affinity and specificity are certainly important, a characterization of the kinetic aspects of association and dissociation is perhaps of even greater significance to understand the time-course of biological processes.. Markov state models (MSMs) provide a powerful framework for characterizing the kinetics of complex molecular systems.. MSMs are discrete state and discrete time stochastic master equation models. Building an MSM involves defining a set of discrete microstates within a subspace of collective variables (features), and estimating the hopping transition probabilities between such states at a fixed lag-time interval from the information generated by detailed dynamical simulations.. Assuming that the resulting MSM constructed is representative of the system of interest, the framework can be used as a generator to predict any equilibrium or long-term kinetic properties at low computational cost.
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