Quantitatively Characterizing the Ligand Binding Mechanisms of Choline Binding Protein Using Markov State Model Analysis

Abstract

Protein-ligand recognition plays key roles in many biological processes. One of the most fascinating questions about protein-ligand recognition is to understand its underlying mechanism, which often results from a combination of induced fit and conformational selection. In this study, we have developed a three-pronged approach of Markov State Models, Molecular Dynamics simulations, and flux analysis to determine the contribution of each model. Using this approach, we have quantified the recognition mechanism of the choline binding protein (ChoX) to be ∼90% conformational selection dominant under experimental conditions. This is achieved by recovering all the necessary parameters for the flux analysis in combination with available experimental data. Our results also suggest that ChoX has several metastable conformational states, of which an apo-closed state is dominant, consistent with previous experimental findings. Our methodology holds great potential to be widely applied to understand recognition mechanisms underlining many fundamental biological processes.

Rate Constants and Mechanisms of Protein–Ligand Binding

“…This problem was recently addressed by Gu et al (32). Using MSMs constructed from molecular dynamics simulations, they proposed an extension of the four-state model, in which the inactive state comprised four substates.…”

“…Equation 10 proves to be quite accurate, except when both the conformational interconversion rates are very low and the ligand concentration is very high [a condition where the many-body nature of the system is most prominent (106)]. Given this encouraging finding, it will be interesting to construct rate-equation models for more realistic protein–ligand systems (including identifying intermediates and determining the rate constants between states) (32) and test how well they recapitulate the many-body binding kinetics.…”

Theoretical restrictions on longest implicit time scales in Markov state models of biomolecular dynamics

“…In previous studies of specific biomolecular systems with MSMs based on MD simulations, reported longest implicit timescales turned out to be smaller than the aggregate sampling by at least one order of magnitude [5][6][7][8][9][10][11][12][13][14][15][16][17] or comparable to the aggregate sampling. [18][19][20][21][22] In this paper, we analyze several MSMs with different topologies to uncover the reasons for the above empirical rule. These MSMs represent typical cases when slow dynamics is observed in biomolecular systems.…”

Dynamic Graphical Models of Molecular Kinetics

“…The number of states of a macromolecule is then determined by the timescales one wants to resolve 2 , and an MD simulation can be considered converged when all these metastable states have been found and the transitions between them have been sampled in both directions. This state-based view has led to the extensive characterization of folding [11][12][13] , conformation changes 14,15 , ligand binding [16][17][18][19] and association/dissociation 20 in small to medium-sized proteins. Although seemingly conceptually different, reaction-coordinate (RC) based methods, such as umbrella sampling 21 , flooding/metadynamics 22,23 and related analyses are also state-based methods, where the state is characterized by the values of the chosen RCs.…”