Atomistic Resolution Research Articles

Adaptive resolution force probe simulations: Coarse graining in the ideal gas approximation.

The unfolding of molecular complexes or biomolecules under the influence of external mechanical forces can routinely be simulated with atomistic resolution. To obtain a match of the characteristic time scales with those of experimental force spectroscopy, often coarse graining procedures are employed. Here, building on a previous study, we apply the adaptive resolution scheme (AdResS) to force probe molecular dynamics (FPMD) simulations using two model systems as examples: One system is the previously investigated calix[4]arene dimer that shows reversible one-step unfolding, and the other example is provided by a small peptide, a β-alanine octamer in methanol solvent. The mechanical unfolding of this peptide proceeds via a metastable intermediate and, therefore, represents a first step toward a complex unfolding pathway. We show that the average number of native contacts serves as a robust order parameter for the forced unfolding of this small peptide. In addition to increasing the complexity of the relevant conformational changes, we study the impact of the methodology used for coarse graining. Apart from the iterative Boltzmann inversion method, we apply an ideal gas approximation, and therefore, we replace the solvent by a non-interacting system of spherical particles. In all cases, we find excellent agreement between the results of FPMD simulations performed fully atomistically and those of the AdResS simulations also in the case of fast pulling. This holds for all details of the unfolding pathways, such as the distributions of the characteristic forces and also the sequence of hydrogen-bond opening in case of the β-alanine octamer. Therefore, the methodology is very well suited to simulate the mechanical unfolding of systems of experimental relevance.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.

Predicting Molecular Ordering in Deposited Molecular Films

AbstractThin films of molecular materials are commonly employed in organic light‐emitting diodes, field‐effect transistors, and solar cells. The morphology of these organic films is shown to depend heavily on the processing used during manufacturing, such as vapor co‐deposition. However, the prediction of processing‐dependent morphologies has until now posed a significant challenge, particularly in cases where self‐assembly and ordering are involved. In this work, a method is developed based on coarse‐graining that is capable of predicting molecular ordering in vapor‐deposited films of organic materials. The method is tested on an extensive database of novel and known organic semiconductors. A good agreement between the anisotropy of the refractive indices of the simulated and experimental vapor‐deposited films suggests that the method is quantitative and can predict the molecular orientations in organic films at an atomistic resolution. The methodology can be readily utilized for screening materials for organic light‐emitting diodes.

Multiresolution molecular dynamics simulations reveal the interplay between conformational variability and functional interactions in membrane-bound cytochrome P450 2B4.

Cytochrome P450 2B4 (CYP 2B4) is one of the best-characterized CYPs and serves as a key model system for understanding the mechanisms of microsomal class II CYPs, which metabolize most known drugs. The highly flexible nature of CYP 2B4 is apparent from crystal structures that show the active site with either a wide open or a closed heme binding cavity. Here, we investigated the conformational ensemble of the full-length CYP 2B4 in a phospholipid bilayer, using multiresolution molecular dynamics (MD) simulations. Coarse-grained MD simulations revealed two predominant orientations of CYP 2B4's globular domain with respect to the bilayer. Their refinement by atomistic resolution MD showed adaptation of the enzyme's interaction with the lipid bilayer, leading to open configurations that facilitate ligand access to the heme binding cavity. CAVER analysis of enzyme tunnels, AquaDuct analysis of water routes, and Random Acceleration Molecular Dynamics simulations of ligand dissociation support the conformation-dependent passage of molecules between the active site and the protein surroundings. Furthermore, simulation of the re-entry of the inhibitor bifonazole into the open conformation of CYP 2B4 resulted in binding at a transient hydrophobic pocket within the active site cavity that may play a role in substrate binding or allosteric regulation. Together, these results show how the open conformation of CYP 2B4 facilitates the binding of substrates from and release of products to the membrane, whereas the closed conformation prolongs the residence time of substrates or inhibitors and selectively allows the passage of smaller reactants via the solvent and water channels.

Neural network potential for Zr-H

The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases. Understanding the various effects of H requires studies with atomistic resolution but at scales that incorporate defects such as cracks, interfaces, and dislocations. Such studies thus demand accurate interatomic potentials. Here, a neural network potential (NNP) for the Zr-H system is developed within the Behler-Parrinello framework. The Zr-H NNP retains the accuracy of a recent NNP for hcp Zr and exhibits excellent agreement with first-principles density functional theory (DFT) for (i) H interstitials and their diffusion in hcp Zr, (ii) formation energies, elastic constants, and surface energies of relevant Zr hydrides, and (iii) energetics of a common Zr/Zr-H interface. The Zr-H NNP shows physical behavior for many different crack orientations in the most-stable ε-hydride, and structures and reasonable relative energetics for the 〈a〉 screw dislocation in pure Zr. This Zr-H NNP should thus be very powerful for future study of many phenomena driving H degradation in Zr that require atomistic detail at scales far above those accessible by first-principles.

Targeting toll-like receptor 7 as a therapeutic development strategy for systemic lupus erythematosus

Endosomal TLRs (TLR3/7/8/9) are highly analogous innate immunity sensors for various viral or bacterial RNA/DNA molecular patterns. Among them, TLR7, in particular, has been suggested to be a target for various inflammatory disorders and autoimmune diseases including systemic lupus erythematosus (SLE); but few small-molecule inhibitors with elaborated mechanism have been reported in literature. Here, we reported a well-characterized human TLR7-specific small-molecule inhibitor, TH-407b, with promising potency and negligible cytotoxicity through a novel binding mechanism. Notably, TH-407b not only effectively inhibited TLR7-mediated pro-inflammatory signaling in a variety of cultured cell lines but also demonstrated potent inflammation suppressing activities in primary peripheral blood mononuclear cells (PBMCs) derived from SLE patients. Furthermore, TH-407b showed prominent efficacy in vivo, improved survival rate and ameliorated symptoms of SLE model mice. To obtain molecular insights into the TH-407b derivatives’ inhibition mechanism, we performed the structural analysis of TLR7/TH-407b complex using cryogenic electron microscopy (cryo-EM) method. As an atomistic resolution cryo-EM structure of the TLR family, it not only of value to facilitate structure-based drug design, but also shed light to methodology development of small proteins using EM. Significantly, TH-407b has unveiled an inhibition strategy for TLR7 via stabilizing its resting/inactivated state. Such a resting state could be generally applicable to all TLRs, rendering a useful method for targeting this group of important immunological receptors.

Generalized quantum master equations can improve the accuracy of semiclassical predictions of multitime correlation functions

Multitime quantum correlation functions are central objects in physical science, offering a direct link between the experimental observables and the dynamics of an underlying model. While experiments such as 2D spectroscopy and quantum control can now measure such quantities, the accurate simulation of such responses remains computationally expensive and sometimes impossible, depending on the system’s complexity. A natural tool to employ is the generalized quantum master equation (GQME), which can offer computational savings by extending reference dynamics at a comparatively trivial cost. However, dynamical methods that can tackle chemical systems with atomistic resolution, such as those in the semiclassical hierarchy, often suffer from poor accuracy, limiting the credence one might lend to their results. By combining work on the accuracy-boosting formulation of semiclassical memory kernels with recent work on the multitime GQME, here we show for the first time that one can exploit a multitime semiclassical GQME to dramatically improve both the accuracy of coarse mean-field Ehrenfest dynamics and obtain orders of magnitude efficiency gains.

Molecular modeling of reactive systems with REACTER

From batteries to biology, many important technologies and physical phenomena operate as out-of-equilibrium reactive systems. Accurately modeling the nanoscale dynamics of non-equilibrium reactive systems and how they respond to external stimuli is challenging, especially if both atomistic resolution and large scales (>105 atoms) are required. REACTER is a protocol for modeling chemical reactions during classical molecular dynamics (MD) simulations. Coupling traditional fixed-valence force fields with heuristic reactive MD is advantageous for large-scale simulations of dynamic systems that can include the complex reaction mechanisms common in organic chemistry. This paper details the current features of the LAMMPS implementation of REACTER, known as fix bond/react, and surveys recent applications of the protocol in a variety of fields, including photopolymers, high-performance composites, and membranes. Conceived as a tool for modeling polymerization processes, the scope of REACTER is expanding as it is applied to new materials and supporting features are implemented. Three new case studies are presented that highlight the capabilities of REACTER, including modeling hierarchical materials, the mechanics of molecular machines, and large-scale dynamics of heterogeneous catalysis.

CGeNArate: a sequence-dependent coarse-grained model of DNA for accurate atomistic MD simulations of kb-long duplexes.

We present CGeNArate, a new model for molecular dynamics simulations of very long segments of B-DNA in the context of biotechnological or chromatin studies. The developed method uses a coarse-grained Hamiltonian with trajectories that are back-mapped to the atomistic resolution level with extreme accuracy by means of Machine Learning Approaches. The method is sequence-dependent and reproduces very well not only local, but also global physical properties of DNA. The efficiency of the method allows us to recover with a reduced computational effort high-quality atomic-resolution ensembles of segments containing many kilobases of DNA, entering into the gene range or even the entire DNA of certain cellular organelles.

Tertiary structure and conformational dynamics of the anti-amyloidogenic chaperone DNAJB6b at atomistic resolution

DNAJB6b is a molecular chaperone of the heat shock protein network, shown to play a crucial role in preventing aggregation of several disease-related intrinsically disordered proteins. Using homology modeling and microsecond-long all-atom molecular dynamics (MD) simulations, we show that monomeric DNAJB6b is a transiently interconverting protein cycling between three states: a closed state, an open state (both abundant), and a less abundant extended state. Interestingly, the reported regulatory autoinhibitory anchor between helix V in the G/F1 region and helices II/III of the J-domain, which obstructs the access of Hsp70 to the J-domain remains present in all three states. This possibly suggests a mechanistically intriguing regulation in which DNAJB6b only becomes exposed when loaded with substrates that require Hsp70 processing. Our MD results of DNAJB6b carrying mutations in the G/F1 region that are linked to limb-girdle muscular dystrophy type D1 (LGMDD1) show that this G/F1 region becomes highly dynamic, pointing towards a spontaneous release of the autoinhibitory helix V from helices II/III. This would increase the probability of non-functional Hsp70 interactions to DNAJB6b without substrates. Our cellular data indeed confirm that non-substrate loaded LGMDD1 mutants have aberrant interactions with Hsp70.

Enhanced mobility of dislocation network nodes and its effect on dislocation multiplication and strain hardening

Understanding plastic deformation of crystals in terms of the fundamental physics of dislocations has remained a grand challenge in materials science for decades. To overcome this, the Discrete Dislocation Dynamics (DDD) method has been developed, but its lack of atomistic resolution leaves open the possibility that certain key mechanisms may be overlooked. By comparing large-scale Molecular Dynamics (MD) with DDD simulations performed under identical conditions we uncover significant discrepancies in the predicted strength and microstructure evolution in BCC crystals under high-strain rate conditions. These are traced to unexpected behaviors of dislocation network nodes forming at dislocation intersections, that can move in ways not previously anticipated as revealed by MD. Once these newfound freedoms of nodal motion are incorporated, DDD simulations begin to closely match plastic evolution observed in MD. This additional mechanism of motion whereby non-screw dislocations can change their glide plane profoundly affects fundamental processes of dislocation multiplication, recovery and storage that define strength of metals.

Mechanistic insights into interactions between ionizable lipid nanodroplets and biomembranes

Delivery of RNA into cells using lipid nanoparticles (LNPs) has been a significant breakthrough in RNA-based medicine, with clinical applicability expanded through the use of ionizable lipids (ILs). These unique lipids can alter their charge state in response to pH changes, which is crucial for pH-triggered endosomal escape and effective lipid-mediated RNA delivery. In this study, we conducted a comprehensive set of molecular dynamics (MD) simulations to investigate interactions between IL-containing lipid nanodroplets (LNDs) and cell membrane models. Using an atomistic resolution model, we investigated the merging process of LNDs with cell membrane models under neutral conditions relevant to an intercellular environment and acidic pH conditions found in late endosomes. Our observations revealed that at neutral pH, LNDs merged with lipid membranes while preserving the bilayer structure. Under acidic conditions, the LNDs remained attached to the bilayer without fusing into the membranes. Importantly, the presence of ILs did not disrupt the original biomembrane structure during the simulation period. The MD simulations provided valuable atomistic insights into the mechanism of interaction between IL-containing nanodroplets and biomembranes, which could aid the rational design of ILs to develop more efficient LNPs for RNA therapies. Communicated by Ramaswamy H. Sarma

Force matching and iterative Boltzmann inversion coarse grained force fields for ZIF-8.

Despite the intense activity at electronic and atomistic resolutions, coarse grained (CG) modeling of metal-organic frameworks remains largely unexplored. One of the main reasons for this is the lack of adequate CG force fields. In this work, we present iterative Boltzmann inversion and force matching (FM) force fields for modeling ZIF-8 at three different coarse grained resolutions. Their ability to reproduce structure, elastic tensor, and thermal expansion is evaluated and compared with that of MARTINI force fields considered in previous work [Alvares et al., J. Chem. Phys. 158, 194107 (2023)]. Moreover, MARTINI and FM are evaluated for their ability to depict the swing effect, a subtle phase transition ZIF-8 undergoes when loaded with guest molecules. Overall, we found that all our force fields reproduce structure reasonably well. Elastic constants and volume expansion results are analyzed, and the technical and conceptual challenges of reproducing them are explained. Force matching exhibits promising results for capturing the swing effect. This is the first time these CG methods, widely applied in polymer and biomolecule communities, are deployed to model porous solids. We highlight the challenges of fitting CG force fields for these materials.

Computational insights into dynamics and conformational stability of N-acetylmannosamine kinase mutations

The activity of UDP-GlcNAc 2-epimerase/ManNAc kinase (GNE) is essential for the biosynthesis of sialic acid, which is involved in cellular processes in health and diseases. GNE contains an N-terminal epimerase domain and a C-terminal kinase domain (N-acetylmannosamine kinase, MNK). Mutations of the GNE protein led to hypoactivity of the enzyme and cause sialurea or autosomal recessive inclusion body myopathy/Nonaka myopathy. Here, we used all-atom molecular dynamics (MD) simulations to comprehend the folding, dynamics and conformational stability of MNK variants, including the wild type (WT) and three mutants (H677R, V696M and H677R/V696M). The deleterious and destabilizing nature of MNK mutants were predicted using different prediction tools. Results predicted that mutations modulate the stability, flexibility and function of MNK. The effect of mutations on the conformational stability and dynamics of MNK was next studied through the free-energy landscape (FEL), hydrogen-bonds and secondary structure changes. The FEL results show that the mutations interfere with various conformational transitions in both WT and mutants, exposing the structural underpinnings of protein destabilization and unfolding brought on by mutation. We discover that, when compared to the other two mutations, V696M and H677R/V696M, H677R has the most harmful effects. These findings have a strong correlation with published experimental studies that demonstrate how these mutations disrupt MNK activity. Hence, this computational study describes the structural details to unravel the mutant effects at the atomistic resolution and has implications for understanding the GNE's physiological and pathological role. Communicated by Ramaswamy H. Sarma

Atomistic ensemble resolution of an intrinsically disordered peptide which undergoes proline cis/trans isomerisation

Atomistic ensemble resolution of an intrinsically disordered peptide which undergoes proline cis/trans isomerisation

The near field response of molecules coupled with plasmons at atomistic resolution

This study contrasts and quantifies the near-field enhancements under varying resonance conditions and at molecular coverage, validating the self-focusing effect of single molecules at the atomic level.

Nanomatrix: Scalable Construction of Crowded Biological Environments.

We present a novel method for the interactive construction and rendering of extremely large molecular scenes, capable of representing multiple biological cells in atomistic detail. Our method is tailored for scenes, which are procedurally constructed, based on a given set of building rules. Rendering of large scenes normally requires the entire scene available in-core, or alternatively, it requires out-of-core management to load data into the memory hierarchy as a part of the rendering loop. Instead of out-of-core memory management, we propose to procedurally generate the scene on-demand on the fly. The key idea is a positional- and view-dependent procedural scene-construction strategy, where only a fraction of the atomistic scene around the camera is available in the GPU memory at any given time. The atomistic detail is populated into a uniform-space partitioning using a grid that covers the entire scene. Most of the grid cells are not filled with geometry, only those are populated that are potentially seen by the camera. The atomistic detail is populated in a compute shader and its representation is connected with acceleration data structures for hardware ray-tracing of modern GPUs. Objects which are far away, where atomistic detail is not perceivable from a given viewpoint, are represented by a triangle mesh mapped with a seamless texture, generated from the rendering of geometry from atomistic detail. The algorithm consists of two pipelines, the construction-compute pipeline, and the rendering pipeline, which work together to render molecular scenes at an atomistic resolution far beyond the limit of the GPU memory containing trillions of atoms. We demonstrate our technique on multiple models of SARS-CoV-2 and the red blood cell.

One bead per residue can describe all-atom protein structures

Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.

Gaussian representation of coarse-grained interactions of liquids: Theory, parametrization, and transferability.

Coarse-grained (CG) interactions determined via bottom-up methodologies can faithfully reproduce the structural correlations observed in fine-grained (atomistic resolution) systems, yet they can suffer from limited extensibility due to complex many-body correlations. As part of an ongoing effort to understand and improve the applicability of bottom-up CG models, we propose an alternative approach to address both accuracy and transferability. Our main idea draws from classical perturbation theory to partition the hard sphere repulsive term from effective CG interactions. We then introduce Gaussian basis functions corresponding to the system's characteristic length by linking these Gaussian sub-interactions to the local particle densities at each coordination shell. The remaining perturbative long-range interaction can be treated as a collective solvation interaction, which we show exhibits a Gaussian form derived from integral equation theories. By applying this numerical parametrization protocol to CG liquid systems, our microscopic theory elucidates the emergence of Gaussian interactions in common phenomenological CG models. To facilitate transferability for these reduced descriptions, we further infer equations of state to determine the sub-interaction parameter as a function of the system variables. The reduced models exhibit excellent transferability across the thermodynamic state points. Furthermore, we propose a new strategy to design the cross-interactions between distinct CG sites in liquid mixtures. This involves combining each Gaussian in the proper radial domain, yielding accurate CG potentials of mean force and structural correlations for multi-component systems. Overall, our findings establish a solid foundation for constructing transferable bottom-up CG models of liquids with enhanced extensibility.

Effect of Protonation on the Molecular Structure of Adenosine 5'-Triphosphate: A Combined Theoretical and Near Edge X-ray Absorption Fine Structure Study.

The present work combines the near edge X-ray absorption mass spectrometry of a protonated adenosine 5'-triphosphate (ATP) molecule isolated in an ion trap with (time-dependent) density functional theory calculations. Our study unravels the effect of protonation on the ATP structure and its spectral properties, providing structure-property relationships at atomistic resolution for protonated ATP (ATPH) isolated in the gas-phase conditions. On the other hand, the present C and N K-edge X-ray absorption spectra of isolated ATPH appear closely like those previously reported for solvated ATP at low pH. Therefore, the present work should be relevant for further investigation and modeling of structure-function properties of protonated adenine and ATP in complex biological environments.