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

Abstract

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.

Unleashing the Power of Evolution in Xylanase Engineering: Investigating the Role of Distal Mutation Regulation

“…To validate our hypothesis, Gaussian Accelerated Molecular Dynamics (GaMD) and Markov State Models (MSMs) were performed to discern key metastable states and their conformational population shifts. 53 Figure S8 illustrates that WT possessed a wider range of conformational states, suggesting potential dynamic instability due to its extensive conformational diversity. 54 Our MSMs analysis successfully identified three metastable states in the WT complex: S1, S2, and S3 (Figure 7).…”

Incorporating Prior Knowledge in the Seeds of Adaptive Sampling Molecular Dynamics Simulations of Ligand Transport in Enzymes with Buried Active Sites

“…Finally, most ASMD protocols suffer from exploitation-exploration trade-off, meaning that already visited states will less likely be respawn for simulations in the next epochs, potentially limiting the sampling of some metastable states required for accurate description of processes . Given the rising success of ASMD simulations in ligand transport studies, the impact of designing individual components in an ASMD workflow on the efficacy of sampling relevant regions of the protein–ligand configurational space is of interest. ,,,, Betz and Dror investigated the role of a scoring function for selecting the configuration for successive iterations to partially overcome the exploration–exploitation trade-off using the well-known test system of trypsin with a benzamidine inhibitor and a more complex yet realistic system of membrane-bound adrenergic receptor β 2 with dihydroalprenolol inhibitor . They compared three scoring functions: simple counts, in which states were resampled with a probability inversely proportional to their occurrence in the simulation; population scores, which prefer states with smaller populations in MSMs; and hub scores, which select states with lower connectivity in MSMs, as the measure of connectivity of states in MSMs.…”

Incorporating prior knowledge to seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

“…Given the rising success of ASMD simulations in ligand transport studies, the impact of designing individual components in ASMD workflow on the efficacy of sampling relevant regions of proteinligand configurational space is of interest 24,31,[33][34][35] . Betz and Dror investigated the role of a scoring function for selecting the configuration for the successive iterations to partially overcome the exploration-exploitation tradeoff using the well-known test system of trypsin with benzamidine inhibitor and a more complex yet realistic system of membrane-bound adrenergic receptor β2 with dihydroalprenolol inhibitor 33 .…”