Abstract
Molecular recognition is determined by the structure and dynamics of both a protein and its ligand, but it is difficult to directly assess the role of each of these players. In this study, we use Markov State Models (MSMs) built from atomistic simulations to elucidate the mechanism by which the Lysine-, Arginine-, Ornithine-binding (LAO) protein binds to its ligand. We show that our model can predict the bound state, binding free energy, and association rate with reasonable accuracy and then use the model to dissect the binding mechanism. In the past, this binding event has often been assumed to occur via an induced fit mechanism because the protein's binding site is completely closed in the bound state, making it impossible for the ligand to enter the binding site after the protein has adopted the closed conformation. More complex mechanisms have also been hypothesized, but these have remained controversial. Here, we are able to directly observe roles for both the conformational selection and induced fit mechanisms in LAO binding. First, the LAO protein tends to form a partially closed encounter complex via conformational selection (that is, the apo protein can sample this state), though the induced fit mechanism can also play a role here. Then, interactions with the ligand can induce a transition to the bound state. Based on these results, we propose that MSMs built from atomistic simulations may be a powerful way of dissecting ligand-binding mechanisms and may eventually facilitate a deeper understanding of allostery as well as the prediction of new protein-ligand interactions, an important step in drug discovery.
Highlights
Molecular recognition plays important roles in many biological processes
In the induced fit model—introduced by Koshland [1]—the ligand first binds to the protein in its unbound conformation and this binding event induces the protein to transition to the bound state
We describe protein conformations by the opening and twisting angles between their two domains [44] and the location of the ligand because these degrees of freedom describe the slowest dynamics of the system
Summary
Molecular recognition plays important roles in many biological processes. For example, enzymes must recognize their substrates and drugs must be designed to have specific binding partners. Two popular models for protein-ligand binding are the induced fit and conformational selection mechanisms Both attempt to explain how a protein could transition from an unbound conformation to a bound conformation in complex with a ligand. In the induced fit model—introduced by Koshland [1]—the ligand first binds to the protein in its unbound conformation and this binding event induces the protein to transition to the bound state. The conformational selection (or population shift) model [5,6,7,8,9,10,11,12] is a popular alternative to the induced fit mechanism In this model, the intrinsic dynamics of the protein lead it to constantly transition between a stable unbound conformation and a less stable bound conformation. Some docking studies have tried to exploit conformational selection by generating an ensemble of protein structures and docking small molecules against each of them in the hopes of identifying a transiently populated bound conformation that will be stabilized by the ligand [13]
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