Examining the limits of time reweighting and Kramers' rate theory to obtain correct kinetics from accelerated molecular dynamics

Mechanism of Ligand Binding to Theophylline RNA Aptamer

Studying RNA-ligand interactions and quantifying their binding thermodynamics and kinetics are of particular relevance in the field of drug discovery. Here, we combined biochemical binding assays and accelerated molecular simulations to investigate ligand binding and dissociation in RNA using the theophylline-binding RNA as a model system. All-atom simulations using a Ligand Gaussian accelerated Molecular Dynamics method (LiGaMD) have captured repetitive binding and dissociation of theophylline and caffeine to RNA. Theophylline's binding free energy and kinetic rate constants align with our experimental data, while caffeine's binding affinity is over 10,000 times weaker, and its kinetics could not be determined. LiGaMD simulations allowed us to identify distinct low-energy conformations and multiple ligand binding pathways to RNA. Simulations revealed a "conformational selection" mechanism for ligand binding to the flexible RNA aptamer, which provides important mechanistic insights into ligand binding to the theophylline-binding model. Our findings suggest that compound docking using a structural ensemble of representative RNA conformations would be necessary for structure-based drug design of flexible RNA.

Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics

Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with <1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD_Dual simulations were also <1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

Dynamical control of denaturation bubble nucleation in supercoiled DNA minicircles

We examine the behavior of supercoiled DNA minicircles containing between 200 and 400 base-pairs, also named microDNA, in which supercoiling favors thermally assisted DNA denaturation bubbles of nanometer size and controls their lifetime. Mesoscopic modeling and accelerated dynamics simulations allow us to overcome the limitations of atomistic simulations encountered in such systems, and offer detailed insight into the thermodynamic and dynamical properties associated with the nucleation and closure mechanisms of long-lived thermally assisted denaturation bubbles which do not stem from bending- or torque-driven stress. Suitable tuning of the degree of supercoiling and size of specifically designed microDNA is observed to lead to the control of opening characteristic times in the millisecond range, and closure characteristic times ranging over well distinct timescales, from microseconds to several minutes. We discuss how our results can be seen as a dynamical bandwidth which might enhance selectivity for specific DNA binding proteins.

Probing the Conformational and Energy Landscapes of KRAS Membrane Orientation

Membrane reorientation of oncogenic RAS proteins is emerging as an important modulator of their functions. Previous studies have shown that the most common orientations include those with either the three C-terminal α-helices (OS1) or N-terminal β-strands (OS2) of the catalytic domain facing the membrane. OS1 and OS2 differ by the degree to which the effector-interacting surface is occluded by the membrane. However, the relative stability of these states and the rates of transition between them remained undetermined. How mutations might modulate preferences for specific orientation states is also far from clear. The current work attempted to address these questions through a comprehensive analysis of two 20 μs-long atomistic molecular dynamics simulations. The simulations were conducted on the oncogenic G12D and Q61H KRAS mutants bound to an anionic lipid bilayer. G12D and Q61H are among the most prevalent cancer-causing mutations at the P-loop and switch 2 regions of KRAS, respectively. We found that both mutants fluctuate in a similar manner between OS1 and OS2 via an intermediate orientation OS0, and both favor the signaling competent OS1 and OS0 over the occluded OS2. However, they differ in the details, such as in the extent to which they sample OS1. Analysis of the orientation free-energy landscapes estimated from the simulations indicate that OS1 and OS2 are the most stable states. However, the overall free energy surface is rugged, indicating a large diversity of conformations including at least two substates in each orientation state that differ in stability only by about 0.5-1.0 kcal/mol. Reversible transitions between OS1 and OS2 occur via two well-defined pathways that traverse OS0. In the minimum energy path, helix 4 remains close to the membrane as the angle of the catalytic domain from the membrane plane changes, resulting in a barrier of ∼1 kcal/mol for OS1/OS2 interconversions. Estimation of the rates of the various transitions based on survival probabilities yielded two rate constants in the order of 10⁷ and 10⁶ s^-1, which we attribute to intrinsic protein conformational dynamics and transient protein-lipid interactions, respectively. The faster process dominates every transition, confirming a previous suggestion that RAS membrane reorientation is driven by conformational fluctuations rather than protein-lipid interactions.

Accelerating Rare Dissociative Processes in Biomolecules Using Selectively Scaled MD Simulations

Molecular dynamics (MD) simulations can be a powerful tool for modeling complex dissociative processes such as ligand unbinding. However, many biologically relevant dissociative processes occur on timescales that far exceed the timescales of typical MD simulations. Here, we implement and apply an enhanced sampling method in which specific energy terms in the potential energy function are selectively "scaled" to accelerate dissociative events during simulations. Using ligand unbinding as an example of a complex dissociative process, we selectively scaled up ligand-water interactions in an attempt to increase the rate of ligand unbinding. After applying our selectively scaled MD (ssMD) approach to several cyclin-dependent kinase-inhibitor complexes, we discovered that we could accelerate ligand unbinding, thereby allowing, in some cases, unbinding events to occur within as little as 2 ns. Moreover, we found that we could make realistic estimates of the initial unbinding times (τ_unbind^sim) as well as the accompanying change in free energy (ΔG^sim) of the inhibitors from our ssMD simulation data. To accomplish this, we employed a previously described Kramers'-based rate extrapolation method and a newly described free energy extrapolation method. Because our ssMD approach is general, it should find utility as an easy-to-deploy, enhanced sampling method for modeling other dissociative processes.

Acceleration of biomolecular kinetics in Gaussian accelerated molecular dynamics

Recent studies demonstrated that Gaussian accelerated molecular dynamics (GaMD) is a robust computational technique, which provides simultaneous unconstrained enhanced sampling and free energy calculations of biomolecules. However, the exact acceleration of biomolecular dynamics or speedup of kinetic rates in GaMD simulations and, more broadly, in enhanced sampling methods, remains a challenging task to be determined. Here, the GaMD acceleration is examined using alanine dipeptide in explicit solvent as a biomolecular model system. Relative to long conventional molecular dynamics simulation, GaMD simulations exhibited ∼36-67 times speedup for sampling of the backbone dihedral transitions. The acceleration depended on level of the GaMD boost potential. Furthermore, Kramers' rate theory was applied to estimate GaMD acceleration using simulation-derived diffusion coefficients, curvatures and barriers of free energy profiles. In most cases, the calculations also showed significant speedup of dihedral transitions in GaMD, although the GaMD acceleration factors tended to be underestimated by ∼3-96 fold. Because greater boost potential can be applied in GaMD simulations of systems with increased sizes, which potentially leads to higher acceleration, it is subject to future studies on accelerating the dynamics and recovering kinetic rates of larger biomolecules such as proteins and protein-protein/nucleic acid complexes.

Accelerated Molecular Dynamics Study of the Effects of Surface Hydrophilicity on Protein Adsorption

The adsorption of streptavidin is studied on two surfaces, graphite and titanium dioxide, using accelerated molecular dynamics. Adsorption on graphite leads to strong conformational changes while the protein spreads out over the surface. Interestingly, also adsorption on the highly hydrophilic rutile surface induces considerable spreading of the protein. We pin down the cause for this unfolding to the interaction of the protein with the ordered water layers above the rutile surface. For special orientations, the protein penetrates the ordered water layers and comes into direct contact with the surface where the positively charged amino acids settle in places adjacent to the negatively charged top surface atom layer of rutile. We conclude that for both surface materials studied, streptavidin changes its conformation so strongly that it loses its potential for binding biotin. Our results are in good qualitative agreement with available experimental studies.

Time Recovery for a Complex Process Using Accelerated Dynamics

The hyperdynamics method (HD) developed by Voter (J. Chem. Phys. 1996, 106, 4665) sets the theoretical basis to construct an accelerated simulation scheme that holds the time scale information. Since HD is based on transition state theory, pseudoequilibrium conditions (PEC) must be satisfied before any system in a trapped state may be accelerated. As the system evolves, many trapped states may appear, and the PEC must be assumed in each one to accelerate the escape. However, since the system evolution is a priori unknown, the PEC cannot be permanently assumed to be true. Furthermore, the different parameters of the bias function used may need drastic recalibration during this evolution. To overcome these problems, we present a general scheme to switch between HD and conventional molecular dynamics (MD) in an automatic fashion during the simulation. To decide when HD should start and finish, criteria based on the energetic properties of the system are introduced. On the other hand, a very simple bias function is proposed, leading to a straightforward on-the-fly set up of the required parameters. A way to measure the quality of the simulation is suggested. The efficiency of the present hybrid HD-MD method is tested for a two-dimensional model potential and for the coalescence process of two nanoparticles. In spite of the important complexity of the latter system (165 degrees of freedoms), some relevant mechanistic properties were recovered within the present method.

Accelerated molecular dynamics and protein conformational change: a theoretical and practical guide using a membrane embedded model neurotransmitter transporter

Molecular dynamics simulation provides a powerful and accurate method to model protein conformational change, yet timescale limitations often prevent direct assessment of the kinetic properties of interest. A large number of molecular dynamic steps are necessary for rare events to occur, which allow a system to overcome energy barriers and conformationally transition from one potential energy minimum to another. For many proteins, the energy landscape is further complicated by a multitude of potential energy wells, each separated by high free-energy barriers and each potentially representative of a functionally important protein conformation. To overcome these obstacles, accelerated molecular dynamics utilizes a robust bias potential function to simulate the transition between different potential energy minima. This straightforward approach more efficiently samples conformational space in comparison to classical molecular dynamics simulation, does not require advanced knowledge of the potential energy landscape and converges to the proper canonical distribution. Here, we review the theory behind accelerated molecular dynamics and discuss the approach in the context of modeling protein conformational change. As a practical example, we provide a detailed, step-by-step explanation of how to perform an accelerated molecular dynamics simulation using a model neurotransmitter transporter embedded in a lipid cell membrane. Changes in protein conformation of relevance to the substrate transport cycle are then examined using principle component analysis.

Computing ensembles of transitions with molecular dynamics simulations

A molecular understanding of conformational change is important for connecting structure and function. Without the ability to sample on the meaningful large-scale conformational changes, the ability to infer biological function and to understand the effect of mutations and changes in environment is not possible. Our Dynamic Importance Sampling method (DIMS), part of the CHARMM simulation package, is a method that enables sampling over ensembles of transition intermediates. This chapter outlines the context for the method and the usage within the program.

Practical hyperdynamics method for systems with large changes in potential energy

A practical hyperdynamics method is proposed to accelerate systems with highly endothermic and exothermic reactions such as hydrocarbon pyrolysis and oxidation reactions. In this method, referred to as the "adaptive hyperdynamics (AHD) method," the bias potential parameters are adaptively updated according to the change in potential energy. The approach is intensively examined for JP-10 (exo-tetrahydrodicyclopentadiene) pyrolysis simulations using the ReaxFF reactive force field. Valid boost parameter ranges are clarified as a result. It is shown that AHD can be used to model pyrolysis at temperatures as low as 1000 K while achieving a boost factor of around 10(5).

Towards fast, rigorous and efficient conformational sampling of biomolecules: Advances in accelerated molecular dynamics

BACKGROUND

Accelerated molecular dynamics (aMD) has been proven to be a powerful biasing method for enhanced sampling of biomolecular conformations on general-purpose computational platforms. Biologically important long timescale events that are beyond the reach of standard molecular dynamics can be accessed without losing the detailed atomistic description of the system in aMD. Over other biasing methods, aMD offers the advantages of tuning the level of acceleration to access the desired timescale without any advance knowledge of the reaction coordinate.

SCOPE OF REVIEW

Recent advances in the implementation of aMD and its applications to small peptides and biological macromolecules are reviewed here along with a brief account of all the aMD variants introduced in the last decade.

MAJOR CONCLUSIONS

In comparison to the original implementation of aMD, the recent variant in which all the rotatable dihedral angles are accelerated (RaMD) exhibits faster convergence rates and significant improvement in statistical accuracy of retrieved thermodynamic properties. RaMD in conjunction with accelerating diffusive degrees of freedom, i.e. dual boosting, has been rigorously tested for the most difficult conformational sampling problem, protein folding. It has been shown that RaMD with dual boosting is capable of efficiently sampling multiple folding and unfolding events in small fast folding proteins.

GENERAL SIGNIFICANCE

RaMD with the dual boost approach opens exciting possibilities for sampling multiple timescales in biomolecules. While equilibrium properties can be recovered satisfactorily from aMD-based methods, directly obtaining dynamics and kinetic rates for larger systems presents a future challenge. This article is part of a Special Issue entitled Recent developments of molecular dynamics.

On the Application of Molecular-Dynamics Based Markov State Models to Functional Proteins

Owing to recent developments in computational algorithms and architectures, it is now computationally tractable to explore biologically relevant, equilibrium dynamics of realistically sized functional proteins using all-atom molecular dynamics simulations. Molecular dynamics simulations coupled with Markov state models is a nascent but rapidly growing technology that is enabling robust exploration of equilibrium dynamics. The objective of this work is to explore the challenges of coupling molecular dynamics simulations and Markov state models in the study of functional proteins. Using recent studies as a framework, we explore progress in sampling, model building, model selection, and coarse-grained analysis of models. Our goal is to highlight some of the current challenges in applying Markov state models to realistically sized proteins and spur discussion on advances in the field.

Achieving Rigorous Accelerated Conformational Sampling in Explicit Solvent

Molecular dynamics simulations can provide valuable atomistic insights into biomolecular function. However, the accuracy of molecular simulations on general-purpose computers depends on the time scale of the events of interest. Advanced simulation methods, such as accelerated molecular dynamics, have shown tremendous promise in sampling the conformational dynamics of biomolecules, where standard molecular dynamics simulations are nonergodic. Here we present a sampling method based on accelerated molecular dynamics in which rotatable dihedral angles and nonbonded interactions are boosted separately. This method (RaMD-db) is a different implementation of the dual-boost accelerated molecular dynamics, introduced earlier. The advantage is that this method speeds up sampling of the conformational space of biomolecules in explicit solvent, as the degrees of freedom most relevant for conformational transitions are accelerated. We tested RaMD-db on one of the most difficult sampling problems - protein folding. Starting from fully extended polypeptide chains, two fast folding α-helical proteins (Trpcage and the double mutant of C-terminal fragment of Villin headpiece) and a designed β-hairpin (Chignolin) were completely folded to their native structures in very short simulation time. Multiple folding/unfolding transitions could be observed in a single trajectory. Our results show that RaMD-db is a promisingly fast and efficient sampling method for conformational transitions in explicit solvent. RaMD-db thus opens new avenues for understanding biomolecular self-assembly and functional dynamics occurring on long time and length scales.

The dilemma of conformational dynamics in enzyme catalysis: perspectives from theory and experiment

The role of protein dynamics in catalysis is a contemporary issue that has stirred intense debate in the field. This chapter provides a brief overview of the approaches and findings of a wide range of experimental, computational and theoretical studies that have addressed this issue. We summarize the results of our recent atomistic molecular dynamic studies on cis-trans isomerase. Our results help to reconcile the disparate perspectives regarding the complex role of enzyme dynamics in the catalytic step and emphasize the major contribution of transition state stabilization in rate enhancement.

Enhancing protein adsorption simulations by using accelerated molecular dynamics

The atomistic modeling of protein adsorption on surfaces is hampered by the different time scales of the simulation ([Formula: see text][Formula: see text]s) and experiment (up to hours), and the accordingly different 'final' adsorption conformations. We provide evidence that the method of accelerated molecular dynamics is an efficient tool to obtain equilibrated adsorption states. As a model system we study the adsorption of the protein BMP-2 on graphite in an explicit salt water environment. We demonstrate that due to the considerably improved sampling of conformational space, accelerated molecular dynamics allows to observe the complete unfolding and spreading of the protein on the hydrophobic graphite surface. This result is in agreement with the general finding of protein denaturation upon contact with hydrophobic surfaces.

Resolving the complex role of enzyme conformational dynamics in catalytic function

Despite growing evidence suggesting the importance of enzyme conformational dynamics (ECD) in catalysis, a consensus on how precisely ECD influences the chemical step and reaction rates is yet to be reached. Here, we characterize ECD in Cyclophilin A, a well-studied peptidyl-prolyl cis-trans isomerase, using normal and accelerated, atomistic molecular dynamics simulations. Kinetics and free energy landscape of the isomerization reaction in solution and enzyme are explored in unconstrained simulations by allowing significantly lower torsional barriers, but in no way compromising the atomistic description of the system or the explicit solvent. We reveal that the reaction dynamics is intricately coupled to enzymatic motions that span multiple timescales and the enzyme modes are selected based on the energy barrier of the chemical step. We show that Kramers' rate theory can be used to present a clear rationale of how ECD affects the reaction dynamics and catalytic rates. The effects of ECD can be incorporated into the effective diffusion coefficient, which we estimate to be about ten times slower in enzyme than in solution. ECD thereby alters the preexponential factor, effectively impeding the rate enhancement. From our analyses, the trend observed for lower torsional barriers can be extrapolated to actual isomerization barriers, allowing successful prediction of the speedup in rates in the presence of CypA, which is in notable agreement with experimental estimates. Our results further reaffirm transition state stabilization as the main effect in enhancing chemical rates and provide a unified view of ECD's role in catalysis from an atomistic perspective.