Conformational Fluctuations in GTP-Bound K-Ras: A Metadynamics Perspective with Harmonic Linear Discriminant Analysis

Abstract

Biomacromolecules often undergo significant conformational rearrangements during function. In proteins, these motions typically consist in nontrivial, concerted rearrangement of multiple flexible regions. Mechanistic, thermodynamics, and kinetic predictions can be obtained via molecular dynamics simulations, provided that the simulation time is at least comparable to the relevant time scale of the process of interest. Because of the substantial computational cost, however, plain MD simulations often have difficulty in obtaining sufficient statistics for converged estimates, requiring the use of more-advanced techniques. Central in many enhanced sampling methods is the definition of a small set of relevant degrees of freedom (collective variables) that are able to describe the transitions between different metastable states of the system. The harmonic linear discriminant analysis (HLDA) has been shown to be useful for constructing low-dimensional collective variables in various complex systems. Here, we apply HLDA to study the free-energy landscape of a monomeric protein around its native state. More precisely, we study the K-Ras protein bound to GTP, focusing on two flexible loops and on the region associated with oncogenic mutations. We perform microsecond-long biased simulations on the wild type and on G12C, G12D, G12 V mutants, describe the resulting free-energy landscapes, and compare our predictions with previous experimental and computational studies. The fast interconversion between open and closed macroscopic states and their similar thermodynamic stabilities are observed. The mutation-induced effects include the alternations of the relative stabilities of different conformational states and the introduction of many microscopic metastable states. Together, our results demonstrate the applicability of the HLDA-based protocol for the conformational sampling of multiple flexible regions in folded proteins.

Insights into the Cross Talk between Effector and Allosteric Lobes of KRAS from Methyl Conformational Dynamics

“…Although 31 P NMR studies 10,11 on RAS proteins provide valuable information regarding the population equilibrium of the two major states in the active GMPPNP-bound form, new approaches need to be explored to quantitatively reveal the potentially complex functional effects elicited by conformational dynamics in RAS proteins, as suggested by high-pressure NMR studies 20,21 and enhanced molecular dynamic (MD) simulations. 6,7,22 Here, we utilize selectively isotope-labeled methyl groups in a highly deuterated background to quantitatively study the dynamic exchange among multiple conformational states of the G-domain of KRAS4b (referred from hereon as KRAS) in solution. First, combining different methyl relaxation dispersion techniques, 23−25 we demonstrate that selected methyl groups in KRAS allow the simultaneous probing of conformational exchange of the switch I region in both GDP-bound and GMPPNP-bound forms.…”

“…Consequently, the V29G mutation favors the state I conformation likely by increasing the switch I region’s flexibility. Although 31 P NMR studies , on RAS proteins provide valuable information regarding the population equilibrium of the two major states in the active GMPPNP-bound form, new approaches need to be explored to quantitatively reveal the potentially complex functional effects elicited by conformational dynamics in RAS proteins, as suggested by high-pressure NMR studies , and enhanced molecular dynamic (MD) simulations. ,, …”

A General Picture of Cucurbit[8]uril Host–Guest Binding

“…Thermodynamic and kinetic information could be extracted via statistical analyses. Biologically relevant processes generally happen at time scales of μs, ms, and even longer, which are inaccessible in modern computational modeling. − Enhanced sampling techniques are often employed to deal with the time-scale problem. ,− Modifications of the Hamiltonian of the simulated system are added to accelerate the phase space exploration, and post-simulation analyses are used to recover the statistics in the original unperturbed ensemble. − The Hamiltonian or model employed to describe the system under investigation determines the accuracy of the simulation outcome. − Although detailed descriptions such as quantum mechanics (QM) calculations are accurate, their practical use is quite limited due to high computational costs. − To obtain sufficient data for post-processing analyses, the approximated mean-field molecular mechanics (MM) force fields are widely employed in molecular dynamics (MD) simulations of biomolecular systems. − …”

Long-time methods for molecular dynamics simulations: Markov State Models and Milestoning

“…( 4)) is zero at the transition state. Milestones (14,15), (15,16), (16,17), (16,18) and (17,18) have transition function value close to zero, that is, T e i  0, see Figure 5.10. A representative structure for transition state estimated from transition function is shown in Figure 5.11.…”

“…[9][10][11][12][13] Many computational and experimental efforts have been made to understand the conformational dynamics of K-Ras, effects of mutations and to find mutation specific drugs. [8,14,15] Just like K-Ras, mutations in ABL, a kinase, is also associated with certain cancers like chronic myelogenous leukaemia. Kinases are enzymes that catalyse the transfer of the ɤ-phosphate group from an ATP molecule to the hydroxyl group of the serine, threonine or tyrosine residue.…”