Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event.

Ras GTPase is an enzyme that catalyzes the hydrolysis of guanosine triphosphate (GTP) and plays an important role in controlling crucial cellular signaling pathways. However, this enzyme has always been believed to be undruggable due to its strong binding affinity with its native substrate GTP. To understand the potential origin of high GTPase/GTP recognition, here we reconstruct the complete process of GTP binding to Ras GTPase via building Markov state models (MSMs) using a 0.1 ms long all-atom molecular dynamics (MD) simulation. The kinetic network model, derived from the MSM, identifies multiple pathways of GTP en route to its binding pocket. While the substrate stalls onto a set of non-native metastable GTPase/GTP encounter complexes, the MSM accurately discovers the native pose of GTP at its designated catalytic site in crystallographic precision. However, the series of events exhibit signatures of conformational plasticity in which the protein remains trapped in multiple non-native conformations even when GTP has already located itself in its native binding site. The investigation demonstrates mechanistic relays pertaining to simultaneous fluctuations of switch 1 and switch 2 residues which remain most instrumental in maneuvering the GTP-binding process. Scanning of the crystallographic database reveals close resemblance between observed non-native GTP binding poses and precedent crystal structures of substrate-bound GTPase, suggesting potential roles of these binding-competent intermediates in allosteric regulation of the recognition process.

Selective and rapid water transportation across a self-assembled peptide-diol channel via the formation of a dual water array.

Achieving superfast water transport by using synthetically designed molecular artifacts, which exclude salts and protons, is a challenging task in separation science today, as it requires the concomitant presence of a proper water-binding site and necessary selectivity filter for transporting water. Here, we demonstrate the water channel behavior of two configurationally different peptide diol isomers that mimic the natural water channel system, i.e., aquaporins. The solid-state morphology studies showed the formation of a self-assembled aggregated structure, and X-ray crystal structure analysis confirmed the formation of a nanotubular assembly that comprises two distinct water channels. The water permeabilities of all six compounds were evaluated and are found to transport water by excluding salts and protons with a water permeability rate of 5.05 × 108 water molecules per s per channel, which is around one order of magnitude less than the water permeability rate of aquaporins. MD simulation studies showed that the system forms a stable water channel inside the bilayer membrane under ambient conditions, with a 2 × 8 layered assembly, and efficiently transports water molecules by forming two distinct water arrays within the channel.

Reconciling conformational heterogeneity and substrate recognition in cytochrome P450.

Cytochrome P450, the ubiquitous metalloenzyme involved in detoxification of foreign components, has remained one of the most popular systems for substrate-recognition process. However, despite being known for its high substrate specificity, the mechanistic basis of substrate-binding by archetypal system cytochrome P450cam has remained at odds with the contrasting reports of multiple diverse crystallographic structures of its substrate-free form. Here, we address this issue by elucidating the probability of mutual dynamical transition to the other crystallographic pose of cytochrome P450cam and vice versa via unbiased all-atom computer simulation. A robust Markov state model, constructed using adaptively sampled 84-µs-long molecular dynamics simulation trajectories, maps the broad and heterogenous P450cam conformational landscape into five key substates. In particular, the Markov state model identifies an intermediate-assisted dynamic equilibrium between a pair of conformations of P450cam, in which the substrate-recognition sites remain "closed" and "open," respectively. However, the estimate of a significantly higher stationary population of closed conformation, coupled with faster rate of open → closed transition than its reverse process, dictates that the net conformational equilibrium would be swayed in favor of "closed" conformation. Together, the investigation quantitatively infers that although a potential substrate of cytochrome P450cam would, in principle, explore a diverse array of conformations of substrate-free protein, it would mostly encounter a "closed" or solvent-occluded conformation and hence would follow an induced-fit-based recognition process. Overall, the work reconciles multiple precedent crystallographic, spectroscopic investigations and establishes how a statistical elucidation of conformational heterogeneity in protein would provide crucial insights in the mechanism of potential substrate-recognition process.

Capturing Protein-Ligand Recognition Pathways in Coarse-Grained Simulation.

Protein-ligand recognition is dynamic and complex. A key approach in deciphering the mechanism underlying the recognition process is to capture the kinetic process of the ligand in its act of binding to its designated protein cavity. Toward this end, ultralong all-atom molecular dynamics simulation has recently emerged as a popular method of choice because of its ability to record these events at high spatial and temporal resolution. However, success via this route comes at an exorbitant computational cost. Herein, we demonstrate that coarse-grained models of the protein, when systematically optimized to maintain its tertiary fold, can capture the complete process of spontaneous protein-ligand binding from bulk media to the cavity at crystallographic precision and within wall clock time that is orders of magnitude shorter than that of all-atom simulations. The exhaustive sampling of ligand exploration in protein and solvent, harnessed by coarse-grained simulation, leads to elucidation of new ligand recognition pathways and discovery of non-native binding poses.