Effective data-driven collective variables for free energy calculations from metadynamics of paths.

A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets-ideally incorporating information about physical pathways and transition states-which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of ES simulations in trajectory space via the metadynamics of paths algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in ES simulations. We demonstrate our approach with two numerical examples, a 2D model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice.

How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms.

Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking the potential of more rigorous quantum mechanical/molecular mechanics (QM/MM) models combined with molecular dynamics-based free energy techniques could have a tremendous impact. Indeed, these two relatively old techniques are emerging as promising methods in the field. This has been favored by the exponential growth of computer power and the proliferation of powerful data-driven methods. Here, we briefly review recent advances and applications, and give our perspective on the impact that QM/MM and free-energy methods combined with machine learning-aided algorithms can have on drug design.

Kinetic and structural details of urease inactivation by thiuram disulphides.

This paper reports on the molecular details of the reactivity of urease, a nickel-dependent enzyme that catalyses the last step of organic nitrogen mineralization, with thiuram disulphides, a class of molecules known to inactivate the enzyme with high efficacy but for which the mechanism of action had not been yet established. IC50 values of tetramethylthiuram disulphide (TMTD or Thiram) and tetraethylthiuram disulphide (TETD or Disulfiram) in the low micromolar range were determined for plant and bacterial ureases. The X-ray crystal structure of Sporosarcina pasteurii urease inactivated by Thiram, determined at 1.68 Å resolution, revealed the presence of a covalent modification of the catalytically essential cysteine residue. This is located on the flexible flap that modulates the size of the active site channel and cavity. Formation of a Cys-S-S-C(S)-N(CH3)2 functionality responsible for enzyme inactivation was observed. Quantum-mechanical calculations carried out to rationalise the large reactivity of the active site cysteine support the view that a conserved histidine residue, adjacent to the cysteine in the active site flap, modulates the charge and electron density along the thiol SH bond by shifting electrons towards the sulphur atom and rendering the thiol proton more reactive. We speculate that this proton could be transferred to the nickel-coordinated urea amide group to yield a molecule of ammonia from the generated Curea-NH3+ functionality during catalysis.

Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations.

The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.

MiMiCPy: An Efficient Toolkit for MiMiC-Based QM/MM Simulations.

MiMiC is a highly flexible, extremely scalable multiscale modeling framework. It couples the CPMD (quantum mechanics, QM) and GROMACS (molecular mechanics, MM) codes. The code requires preparing separate input files for the two programs with a selection of the QM region. This can be a tedious procedure prone to human error, especially when dealing with large QM regions. Here, we present MiMiCPy, a user-friendly tool that automatizes the preparation of MiMiC input files. It is written in Python 3 with an object-oriented approach. The main subcommand PrepQM can be used to generate MiMiC inputs directly from the command line or through a PyMOL/VMD plugin for visually selecting the QM region. Many other subcommands are also provided for debugging and fixing MiMiC input files. MiMiCPy is designed with a modular structure that allows seamless extensions to new program formats depending on the requirements of MiMiC.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective.

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Metadynamics of Paths.

We present a method to sample reactive pathways via biased molecular dynamics simulations in trajectory space. We show that the use of enhanced sampling techniques enables unconstrained exploration of multiple reaction routes. Time correlation functions are conveniently computed via reweighted averages along a single trajectory and kinetic rates are accessed at no additional cost. These abilities are illustrated analyzing a model potential and the umbrella inversion of NH_{3} in water. The algorithm allows a parallel implementation and promises to be a powerful tool for the study of rare events.

Friction of physisorbed nanotubes: rolling or sliding?

The structure and motion of carbon and h-BN nanotubes (NTs) deposited on graphene is inquired theoretically by simulations based on state-of-the-art interatomic force fields. Results show that any typical cylinder-over-surface approximation is essentially inaccurate. NTs tend to flatten at the interface with the substrate and upon driving they can either roll or slide depending on their size and on their relative orientation with the substrate. In the epitaxially aligned orientation we find that rolling is always the main mechanism of motion, producing a kinetic friction linearly growing with the number of walls, in turn causing an unprecedented supra-linear scaling with the contact area. A 30 degrees misalignment raises superlubric effects, making sliding favorable against rolling. The resulting rolling-to-sliding transition in misaligned NTs is explained in terms of the faceting appearing in large multi-wall tubes, which is responsible for the increased rotational stiffness. Modifying the geometrical conditions provides an additional means of drastically tailoring the frictional properties in this unique tribological system.

Mechanical and Tribological Properties of Layered Materials under High Pressure: Assessing the Importance of Many-Body Dispersion Effects.

The importance of many-body dispersion effects in layered materials subjected to high external loads is evaluated. State-of-the-art many-body dispersion density functional theory calculations performed for graphite, hexagonal boron nitride, and their heterostructures were used to fit the parameters of a classical registry-dependent interlayer potential. Using the latter, we performed extensive equilibrium molecular dynamics simulations and studied the mechanical response of homogeneous and heterogeneous bulk models under hydrostatic pressures up to 30 GPa. Comparison with experimental data demonstrates that the reliability of the many-body dispersion model extends deep into the subequilibrium regime. Friction simulations demonstrate the importance of many-body dispersion effects for the accurate description of the tribological properties of layered material interfaces under high pressure.

The Princess and the Nanoscale Pea: Long-Range Penetration of Surface Distortions into Layered Materials Stacks.

The penetration of moiré out-of-plane distortions, formed at the heterogeneous interface of graphene and hexagonal boron nitride (h-BN), into the layered h-BN stack is investigated. For aligned contacts, the estimated characteristic penetration length of ∼4.7 nm suggests that even at the far surface of a ∼25 h-BN layer thick slab stacked atop the contact, a corrugation of ∼0.1 Å, well within experimental resolution, should still be clearly evident. The penetration length is found to strongly reduce with increasing misalignment angle of the graphene/h-BN junction, where the effect of thermal fluctuations conceals the moiré-induced corrugation in the bulk. These results can be rationalized by continuum elastic theory arguments for anisotropic media. Our findings, which are expected to generally apply for layered heterojunctions, may serve as a route to control the surface corrugation, adhesive properties, and tribological characteristics of two-dimensional materials.

Negative Friction Coefficients in Superlubric Graphite-Hexagonal Boron Nitride Heterojunctions.

Negative friction coefficients, where friction is reduced upon increasing normal load, are predicted for superlubric graphite-hexagonal boron nitride heterojunctions. The origin of this counterintuitive behavior lies in the load-induced suppression of the moiré superstructure out-of-plane distortions leading to a less dissipative interfacial dynamics. Thermally induced enhancement of the out-of-plane fluctuations leads to an unusual increase of friction with temperature. The highlighted frictional mechanism is of a general nature and is expected to appear in many layered material heterojunctions.

Robust microscale superlubricity in graphite/hexagonal boron nitride layered heterojunctions.

Structural superlubricity is a fascinating tribological phenomenon, in which the lateral interactions between two incommensurate contacting surfaces are effectively cancelled resulting in ultralow sliding friction. Here we report the experimental realization of robust superlubricity in microscale monocrystalline heterojunctions, which constitutes an important step towards the macroscopic scale-up of superlubricity. The results for interfaces between graphite and hexagonal boron nitride clearly demonstrate that structural superlubricity persists even when the aligned contact sustains external loads under ambient conditions. The observed frictional anisotropy in the heterojunctions is found to be orders of magnitude smaller than that measured for their homogeneous counterparts. Atomistic simulations reveal that the underlying frictional mechanisms in the two cases originate from completely different dynamical regimes. Our results are expected to be of a general nature and should be applicable to other van der Waals heterostructures.

Nanoserpents: Graphene Nanoribbon Motion on Two-Dimensional Hexagonal Materials.

We demonstrate snake-like motion of graphene nanoribbons atop graphene and hexagonal boron nitride ( h-BN) substrates using fully atomistic nonequilibrium molecular dynamics simulations. The sliding dynamics of the edge-pulled nanoribbons is found to be determined by the interplay between in-plane ribbon elasticity and interfacial lattice mismatch. This results in an unusual dependence of the friction-force on the ribbon's length, exhibiting an initial linear rise that levels-off above a junction-dependent threshold value dictated by the pre-slip stress distribution within the slider. As part of this letter, we present the LAMMPS implementation of the registry-dependent interlayer potentials for graphene, h-BN, and their heterojunctions that were used herein, which provides enhanced performance and accuracy.

Friction boosted by equilibrium misalignment of incommensurate two-dimensional colloid monolayers.

Colloidal two-dimensional monolayers sliding in an optical lattice are of recent importance as a frictional system. In the general case when the monolayer and optical lattices are incommensurate, we predict two important novelties, one in the static equilibrium structure, the other in the frictional behavior under sliding. Structurally, realistic simulations show that the colloid layer should possess in full equilibrium a small misalignment rotation angle relative to the optical lattice, an effect so far unnoticed but visible in some published experimental moiré patterns. Under sliding, this misalignment has the effect of boosting the colloid monolayer friction by a considerable factor over the hypothetical aligned case discussed so far. A frictional increase of similar origin must generally affect other incommensurate adsorbed monolayers and contacts, to be sought out case by case.

Static friction scaling of physisorbed islands: the key is in the edge.

The static friction preventing the free sliding of nanosized rare gas solid islands physisorbed on incommensurate crystalline surfaces is not completely understood. Simulations modeled on Kr/Pb(111) highlight the importance and the scaling behavior of the island's edge contribution to static friction.

Posterior spinal instrumentation: biomechanical study on the role of rods on hardware response to axial load.

Posterior spinal instrumentation is frequently used for the treatment of spine disorders. Importantly, different requirements have to be considered for the optimal use of these systems in various clinical scenarios. In this work, we focused on the role of rods diameter on hardware's stiffness. For this purpose, we established an in vitro model and compared the response to axial load of a posterior stabilization system, characterized by rods of different diameter (4, 5, 6 mm), with that of Dynesys®. Intuitively, the higher the stiffness of the hardware, the lower the load is transferred to the disc. However, the 4 hardware tested showed a different trend in the response to the load regimens: when increasing the load, more flexible systems display a progressive reduction in the percentage of load which is transferred to the disc while more rigid system display the opposite trend. Considering that the load which is transferred, and not by-passed by the hardware, influences the healing of a fracture; the integration of a bone graft or a cage; the fusion process, these data have a relevant impact on clinical practice and highlight features that have to be considered in the choice for the optimal posterior spinal instrumentation.

Greater peripheral blood flow but less bleeding with propofol versus sevoflurane during spine surgery: a possible physiologic model?

STUDY DESIGN: Prospective, randomized, single blind. OBJECTIVE: To compare the effects of sevoflurane and propofol on lumbar-paraspinal-muscles regional blood flow, as well as bleeding when controlled hypotension is used. SUMMARY OF BACKGROUND DATA: Controlled hypotension is the technique of choice to reduce blood loss during spine surgery, but changes in blood flow occurring to lumbar paraspinal muscles during controlled hypotension with propofol and sevoflurane, as well as the entity of bleeding, are unknown. METHODS: Blood flow was assessed by means of a laser Doppler flowmeter during the prehypotensive and hypotensive (defined as a 15% reduction of baseline mean arterial pressure) period in 28 patients (aged 28-73 years, American Society of Anesthesiologists (ASA) I-II) undergoing lumbar spine surgery. Patients were randomized to receive either sevoflurane or propofol as main anesthetic agent to achieve hypotension. At the end of the surgery, blood loss was calculated and intraoperative bleeding (Visual Analogue Scale ranging from 0 to 100) was evaluated by the surgeon. RESULTS.: Peripheral Blood flow was significantly greater in the propofol group both before and during the hypotensive period (median values of 32.7 FU vs. 7.7 and 38.5 FU vs. 10.5, respectively). Despite this fact, blood loss and intraoperative bleeding were significantly reduced when propofol had been used (P < 0.05). CONCLUSION: Despite the greater blood flow when it is used, propofol causes less bleeding than sevoflurane during spine surgery and could be more indicated to produce hypotension during anesthesia. Moreover, it is possible to explain our findings hypothesizing a selective vasodilation of propofol (postcapillary, venous vasodilation), different from that of sevoflurane (precapillary, arteriolar vasodilation).