Systematic Parametrization of Divalent Metal Ions for the OPC3, OPC, TIP3P-FB, and TIP4P-FB Water Models

Dynamic conformational equilibria in the active states of KRAS and NRAS

The design of potent RAS inhibitors benefits from a molecular understanding of the dynamics in KRAS and NRAS and their oncogenic mutants. Here we characterize switch-1 dynamics in GTP-state KRAS and NRAS by 31P NMR, by 15N relaxation dispersion NMR, hydrogen-deuterium exchange mass spectrometry (HDX-MS), and molecular dynamics simulations. In GMPPNP-bound KRAS and NRAS, we see the co-existence of two conformational states, corresponding to an "inactive" state-1 and an "active" state-2, as previously reported. The KRAS oncogenic mutations G12D, G12C and G12V only slightly affect this equilibrium towards the "inactive" state-1, with rank order wt < G12C < G12D < G12V. In contrast, the NRAS Q61R oncogenic mutation shifts the equilibrium fully towards the "active" state-2. Our molecular dynamics simulations explain this by the observation of a transient hydrogen bond between the Arg61 side chain and the Thr35 backbone carbonyl oxygen. NMR relaxation dispersion experiments with GTP-bound KRAS Q61R confirm a drastic decrease in the population of state-1, but still detect a small residual population (1.8%) of this conformer. HDX-MS indicates that higher populations of state-1 correspond to increased hydrogen-deuterium exchange rates in some regions and increased flexibility, whereas low state-1 populations are associated with KRAS rigidification. We elucidated the mechanism of action of a potent KRAS G12D inhibitor, MRTX1133. Binding of this inhibitor to the switch-2 pocket causes a complete shift of KRAS G12D towards the "inactive" conformation and prevents binding of effector RAS-binding domain (RBD) at physiological concentrations, by signaling through an allosteric network.

Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based self-supervised machine learning framework, MutComputeX, with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher expression yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40 % improvement in the TTN (218±3) as compared to WT LDO (TTN=160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Exploring CRISPR-Cas9 HNH-Domain-Catalyzed DNA Cleavage Using Accelerated Quantum Mechanical Molecular Mechanical Free Energy Simulation

The target DNA (tDNA) cleavage catalyzed by the CRISPR Cas9 enzyme is a critical step in the Cas9-based genome editing technologies. Previously, the tDNA cleavage from an active SpyCas9 enzyme conformation was modeled by Palermo and co-workers (Nierzwicki et al., Nat. Catal. 2022 5, 912) using ab initio quantum mechanical molecular mechanical (ai-QM/MM) free energy simulations, where the free energy barrier was found to be more favorable than that from a pseudoactive enzyme conformation. In this work, we performed ai-QM/MM simulations based on another catalytically active conformation (PDB 7Z4J) of the Cas9 HNH domain from cryo-electron microscopy experiments. For the wildtype enzyme, we acquired a free energy profile for the tDNA cleavage that is largely consistent with the previous report. Furthermore, we explored the role of the active-site K866 residue on the catalytic efficiency by modeling the K866A mutant and found that the K866A mutation increased the reaction free energy barrier, which is consistent with the experimentally observed reduction in the enzyme activity.

Accuracy of Discrete-Continuum Solvation Model for Cations: A Benchmark Study

Metal ions play important roles in chemistry, biochemistry, and material sciences. Accurately modeling ion solvation is crucial for simulating ion-containing systems. There are different models for ion solvation in computational chemistry, such as the explicit model, continuum model, and discrete-continuum model. Compared to the explicit model and continuum model, the discrete-continuum model of solvation is a hybrid solvation model in which the first solvation shell is described explicitly, and the remainder of the bulk liquid is characterized by a continuum model, which provides an excellent balance between accuracy and computational costs. This work serves as a systematic benchmark of the discrete-continuum model for the solvation of cations with +2, +3, and +4 charges. The calculated hydration free energies (HFEs) of ions were compared to those obtained by the SMD continuum model alone and the available experimental data. The discrete-continuum model showed improved performance over the continuum model alone via a smaller overall error and more consistent performance. Experimentally observed trends, such as the Irving-Williams series, are generally reproduced. In contrast, greater overall error was obtained for Ln3+ ions, and the HFE trend along the Ln3+ series was more difficult to reproduce, indicating these ions are challenging to model by the discrete-continuum model and continuum model. Overall, the discrete-continuum model is recommended to calculate the HFEs of cations when experimental data are not available.

Making Interfacial Solar Evaporation of Seawater Faster than Fresh Water

Interfacial solar evaporation-based seawater desalination is regarded as one of the most promising strategies to alleviate freshwater scarcity. However, the solar evaporation rate of real seawater is significantly constricted by the ubiquitous salts present in seawater. In addition to the common issue of salt accumulation on the evaporation surface during solar evaporation, strong hydration between salt ions and water molecules leads to a lower evaporation rate for real seawater compared to pure water. Here a facile and general strategy is developed to reverse this occurrence, that is, making real seawater evaporation faster than pure water. By simply introducing specific mineral materials into the floating photothermal evaporator, ion exchange at air-water interfaces directly results in a decrease in seawater evaporation enthalpy, and consequently achieves much higher seawater evaporation rates compared to pure water. This process is spontaneously realized during seawater solar evaporation. Considering the current enormous clean water production from evaporation-based desalination plants, such an evaporation performance improvement can remarkably increase annual clean water production, benefiting millions of people worldwide.

Development of Multiscale Force Field for Actinide (An3+) Solutions

A multiscale force field (FF) is developed for an aqueous solution of trivalent actinide cations An3+ (An = U, Np, Pu, Am, Cm, Bk, and Cf) by using a 12-6-4 Lennard-Jones type potential considering ion-induced dipole interaction. Potential parameters are rigorously and automatically optimized by the meta-multilinear interpolation parametrization (meta-MIP) algorithm via matching the experimental properties, including ion-oxygen distance (IOD) and coordination number (CN) in the first solvation shell and hydration free energy (HFE). The water solvent models incorporate an especially developed polar coarse-grained (CG) water scheme named PW32 and three widely used all-atom (AA) level SPC/E, TIP3P, and TIP4P water schemes. Each PW32 is modeled as two bonded beads to represent three neighboring water molecules, the simulation efficiency of which is 1 to 2 orders of magnitude higher than that of AA waters. The newly developed FF shows high accuracy and transferability in reproducing the IOD, CN, and HFE of An3+. The molecular structure and water exchange dynamics of the first An3+ hydration shell and the ionic (van der Waals) radii are reinvestigated in this work. Moreover, the new FF can readily be transferred to other popular FFs, as it has practicably predicted the permeability of An3+ in a graphene oxide filter within the framework of optimized potentials for liquid simulations (OPLS)-AA FF. It holds promise for applications in exploring actinide aqueous solutions with multiscale computational overhead.

Modeling Infrared Spectroscopy of Nucleic Acids: Integrating Vibrational Non-Condon Effects with Machine Learning Schemes

Vibrational non-Condon effects, which describe how molecular vibrational transitions are influenced by a system's rotational and translational degrees of freedom, are often overlooked in spectroscopy studies of biological macromolecules. In this work, we explore these effects in the modeling of infrared (IR) spectra for nucleic acids in the 1600-1800 cm-1 region. Through electronic structure calculations, we reveal that the transition dipole moments of the C═O and C═C stretching modes in nucleobases are highly sensitive to solvation, hydrogen bonding, and base stacking conditions. To incorporate vibrational non-Condon effects into spectroscopy modeling, we use local electric fields on chromophore atoms as collective coordinates and leverage experimental IR spectra of oligonucleotides to develop deep neural network-based transition dipole strength (TDS) maps for the C═O and C═C chromophores. By integrating molecular dynamics simulations with a mixed quantum/classical treatment of the line shape theory, we apply the TDS maps to calculate the IR spectra of nucleoside 5'-monophosphates, DNA double helices and yeast phenylalanine tRNA. The resulting theoretical spectra show quantitative agreement with experimental measurements. While the predictions for nucleoside 5'-monophosphates are comparable to baseline performance, the TDS maps yield significantly improved IR peak intensities across all oligonucleotides. This theoretical framework effectively bridges atomistic simulations and IR spectroscopy experiments, offering molecular insights into how vibrational non-Condon effects impact the observed spectral features.

Mg2+-Ion Dependence Revealed for a BAHD 13-O-β-Aminoacyltransferase from Taxus Plants

A Taxus baccatin III:3-amino-3-phenylpropanoyltransferase (BAPT, Accession: AY082804) in clade 6 of the BAHD family catalyzed a Mg2+-dependent transfer of isoserines from their corresponding CoA thioesters. An advanced taxane baccatin III on the paclitaxel biosynthetic pathway in Taxus plants was incubated BAPT and phenylisoserine CoA or isobutenylisoserinyl CoA with and without MgCl2. BAPT biocatalytically converted baccatin III to its 13-O-phenylisoserinyl and 3-(1',1'-dimethylvinyl)isoserinyl analogs, an activity that abrogated when Mg2+ ions were omitted. Baccatin III analogs that are precursors to new generation taxanes were also assayed with BAPT, the Mg2+ cofactor, and 3-(1',1'-dimethylvinyl)isoserinyl CoA to make paclitaxel derivatives at k cat/K M ranging between 27 and 234 s-1 M-1. Molecular dynamics simulations of the BAPT active site modeled on the crystal structure of a BAHD family member (PDB: 4G0B) suggest that Mg2+ causes BAPT to use an unconventional active site space compared to those of other BAHD catalysts, studied over the last 25 years, that use a conserved catalytic histidine residue that is glycine in BAPT. The simulated six-membered Mg2+-coordination complex includes an interaction that disrupts an intramolecular hydrogen bond between the C13-hydroxyl and the carbonyl oxygen of the C4-acetate of baccatin III. A simulation snapshot captured an active site conformation showing the liberated C13-hydroxyl of baccatin III poised for acylation by BAPT through a potential substrate-assisted mechanism.

Unveiling Nucleosome Dynamics: A Comparative Study Using All-Atom and Coarse-Grained Simulations Enhanced by Principal Component Analysis

This study investigates nucleosome dynamics using both all-atom and coarse-grained (CG) molecular dynamics simulations, focusing on the SIRAH force field. Simulations are performed for two nucleosomal DNA sequences-ASP and Widom-601-over six microseconds at physiological salt concentrations. Comparative analysis of structural parameters, such as groove widths and base pair geometries, reveals good agreement between atomistic and CG models, though CG simulations exhibit broader conformational sampling and greater breathing motion of DNA ends. Principal component analysis (PCA) is applied to DNA structural parameters, revealing multiple free energy minima, especially in CG simulations. These findings highlight the potential of the SIRAH CG force field for studying large-scale nucleosome dynamics, offering insights into DNA repositioning and sequence-dependent behavior.

Atomistic Insights into Sequence-Mediated Spontaneous Association of Short RNA Chains

RNA-RNA association and phase separation appear to be essential for the assembly of stress granules and underlie RNA foci formation in repeat expansion disorders. RNA molecules are found to play a significant role in gene-regulatory functions via condensate formation among themselves or with RNA-binding proteins. The interplay between driven versus spontaneous processes is likely to be an important factor for controlling the formation of RNA-mediated biomolecular condensate. However, the sequence-specific interactions and molecular mechanisms that drive the spontaneous RNA-RNA association and help to form RNA-mediated phase-separated condensate remain unclear. With microseconds-long atomistic molecular simulations here, we report how essential aspects of RNA chains, namely, base composition, metal ion binding, and hydration properties, contribute to the association of the series of simplest biologically relevant homopolymeric and heteropolymeric short RNA chains. We show that spontaneous processes make the key contributions governed by the sequence-intrinsic properties of RNA chains, where the definite roles of base-specific hydrogen bonding and stacking interactions are prominent in the association of the RNA chains. Purine versus pyrimidine contents of RNA chains can directly influence the association properties of RNA chains by modulating hydrogen bonding and base stacking interactions. This study determines the impact of ionic environment in sequence-specific spontaneous association of short RNA chains, hydration features, and base-specific interactions of Na+, K+, and Mg2+ ions with RNA chains.

Metallicious: Automated Force-Field Parameterization of Covalently Bound Metals for Supramolecular Structures

Metal ions play a central, functional, and structural role in many molecular structures, from small catalysts to metal-organic frameworks (MOFs) and proteins. Computational studies of these systems typically employ classical or quantum mechanical approaches or a combination of both. Among classical models, only the covalent metal model reproduces both geometries and charge transfer effects but requires time-consuming parameterization, especially for supramolecular systems containing repetitive units. To streamline this process, we introduce metallicious, a Python tool designed for efficient force-field parameterization of supramolecular structures. Metallicious has been tested on diverse systems including supramolecular cages, knots, and MOFs. Our benchmarks demonstrate that parameters accurately reproduce the reference properties obtained from quantum calculations and crystal structures. Molecular dynamics simulations of the generated structures consistently yield stable simulations in explicit solvent, in contrast to similar simulations performed with nonbonded and cationic dummy models. Overall, metallicious facilitates the atomistic modeling of supramolecular systems, key for understanding their dynamic properties and host-guest interactions. The tool is freely available on GitHub (https://github.com/duartegroup/metallicious).

NMR Coupling Constants, Karplus Equations, and Adjusted MD Statistics: Detecting Diagnostic Torsion Angles for the Solution Geometry of 6-[α-d-Mannopyranosyl]-d-Mannopyranose (Mannobiose)

The quantitative solution conformations of 2-(hydroxymethyl)-tetrahydropyran, α-methyl-d-mannopyranoside, and 6-[α-d-mannopyranosyl]-d-mannopyranose (mannobiose) are described. Parametrized Karplus equations for redundant spin pairs across the terminal ω-torsion and the glycosidic ω-torsion for mannobiose are developed, including ω/θ-hypersurfaces for the terminal hydroxymethylene group. Experimental NMR data, algorithmic spectral simulation (clustered Hamiltonian method), molecular dynamics (MD) simulations (GLYCAM06), energy minimizations by DFT, and adjusted torsion angle populations weighted over the Karplus-type equations are used. We demonstrate that spectral simulation is a powerful tool in the refinement of initial J values obtained from static GAIO DFT calculations. We also show that only as few as one of multiple redundant torsions can be diagnostic for conformational analysis of the disaccharide.

Exploring Ion Polarizabilities and Their Correlation with van der Waals Radii: A Theoretical Investigation

Polarizability (α) is a fundamental property which measures the tendency of the electron cloud of an atom, ion, or molecule to be distorted by electric field. Polarizability contributes to important physical properties such as molecular interactions or dielectric constants; thus, it is essential to have accurate polarizabilities in molecular simulations. However, it remains a challenge to develop polarizable force fields (FFs) for ions in computational chemistry. In particular, a comprehensive set of polarizabilities for ions has not been derived. Herein, we derived a systematic set of polarizabilities for atoms and ions across the periodic table based on high-level quantum mechanics calculations. These values have excellent agreement with experimental data. Furthermore, we examined the relationship between the obtained polarizabilities and the van der Waals (VDW) radii (RVDW) that we previously determined (J. Chem. Theory Comput., 2023, 19, 2064). Two relationships, RVDW ∝ α1/7 and RVDW ∝ α1/3, proposed in previous studies were examined in the present work. Our results indicated the former relationship, which was derived based on the quantum harmonic oscillator model, prevails for atoms and cations, but neither relationship provides a satisfactory fit for anions. This is consistent with the tight-binding nature of the electrons in atoms and cations, while it is more challenging to quantify the polarizabilities of anions because of their more dispersed electron clouds. Moreover, we compared different approaches to determine the dispersion coefficients, including the London equation, Slater-Kirkwood equation, symmetry-adapted perturbation theory (SAPT) calculations, and time-dependent density functional theory method, along with the approach based on VDW constants. Our results indicated that although different approaches predict deviated magnitudes for the dispersion coefficients, their predictions are highly correlated, implying that each of these approaches can be used to evaluate dispersion interactions after proper scaling. Finally, we have developed a parametrization strategy for the 12-6-4 model based on the obtained insights. We specifically compared the performance of the 12-6-4 model with SAPT and SobEDA analyses to model interactions involving Na+/Mg2+ and various ligands containing He, Ne, Ar, H2O, NH3, [H2PO4]-, and [HPO4]2-. Our results demonstrate that the 12-6-4 parameters effectively reproduce both the total interaction energy and the individual energy components (electrostatics, exchange-repulsion, dispersion, and induction), highlighting the physical robustness of the 12-6-4 model and the effectiveness of our parametrization approach. This study has significant implications for advancing the development of next-generation ion models and polarizable FFs.

Modelling ligand exchange in metal complexes with machine learning potentials

Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal-ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies.

Unraveling the Structure-Spectrum Relationship of Yeast Phenylalanine Transfer RNA: Insights from Theoretical Modeling of Infrared Spectroscopy

Yeast phenylalanine tRNA (tRNAphe) is a paradigmatic model in structural biology. In this work, we combine molecular dynamics simulations and spectroscopy modeling to establish a direct link between its structure, conformational dynamics, and infrared (IR) spectra. Employing recently developed vibrational frequency maps and coupling models, we apply a mixed quantum/classical treatment of the line shape theory to simulate the IR spectra of tRNAphe in the 1600-1800 cm-1 region across its folded and unfolded conformations and under varying concentrations of Mg2+ ions. The predicted IR spectra of folded and unfolded tRNAphe are in good agreement with experimental measurements, validating our theoretical framework. We then elucidate how the characteristic L-shaped tertiary structure of the tRNA and its modulation in response to diverse chemical environments give rise to distinct IR absorption peaks and line shapes. These calculations effectively bridge IR spectroscopy experiments and atomistic molecular simulations, unraveling the molecular origins of the observed IR spectra of tRNAphe. This work presents a robust theoretical protocol for modeling the IR spectroscopy of nucleic acids, which will facilitate its application as a sensitive probe for detecting the fluctuating secondary and tertiary structures of these essential biological macromolecules.

BAT2: an Open-Source Tool for Flexible, Automated, and Low Cost Absolute Binding Free Energy Calculations

Absolute binding free energy (ABFE) calculations with all-atom molecular dynamics (MD) have the potential to greatly reduce costs in the first stages of drug discovery. Here, we introduce BAT2, the new version of the Binding Affinity Tool (BAT.py), designed to combine full automation of ABFE calculations with high-performance MD simulations, making it a potential tool for virtual screening. We describe and test several changes and new features that were incorporated into the code, such as relative restraints between the protein and the ligand instead of using fixed dummy atoms, support for the OpenMM simulation engine, a merged approach to the application/release of restraints, support for cobinders and proteins with multiple chains, and many others. We also reduced the simulation times for each ABFE calculation, assessing the effect on the expected robustness and accuracy of the calculations.

Simulating Metal-Imidazole Complexes

One commonly observed binding motif in metalloproteins involves the interaction between a metal ion and histidine's imidazole side chains. Although previous imidazole-M(II) parameters established the flexibility and reliability of the 12-6-4 Lennard-Jones (LJ)-type nonbonded model by simply tuning the ligating atom's polarizability, they have not been applied to multiple-imidazole complexes. To fill this gap, we systematically simulate multiple-imidazole complexes (ranging from one to six) for five metal ions (Co(II), Cu(II), Mn(II), Ni(II), and Zn(II)) which commonly appear in metalloproteins. Using extensive (40 ns per PMF window) sampling to assemble free energy association profiles (using OPC water and standard HID imidazole charge models from AMBER) and comparing the equilibrium distances to DFT calculations, a new set of parameters was developed to focus on energetic and geometric features of multiple-imidazole complexes. The obtained free energy profiles agree with the experimental binding free energy and DFT calculated distances. To validate our model, we show that we can close the thermodynamic cycle for metal-imidazole complexes with up to six imidazole molecules in the first solvation shell. Given the success in closing the thermodynamic cycles, we then used the same extended sampling method for six other metal ions (Ag(I), Ca(II), Cd(II), Cu(I), Fe(II), and Mg(II)) to obtain new parameters. Since these new parameters can reproduce the one-imidazole geometry and energy accurately, we hypothesize that they will reasonably predict the binding free energy of higher-level coordination numbers. Hence, we did not extend the analysis of these ions up to six imidazole complexes. Overall, the results shed light on metal-protein interactions by emphasizing the importance of ligand-ligand interaction and metal-π-stacking within metalloproteins.

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

Because most proteins have buried active sites, protein tunnels or channels play a crucial role in the transport of small molecules into buried cavities for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, a recent example being energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasted the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a nontrivial system of a haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The most knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting drug development efforts and the rational design of enzymes with buried active sites.

Structural mechanism underlying variations in DNA binding by the androgen receptor

The androgen receptor (AR) is a steroid activated transcription factor which recognizes DNA motifs resembling inverted repeats of a conserved 5'-AGAACA-3'-like hexanucleotides separated by a three-nucleotide spacer from a similar, but less conserved hexanucleotide. Here, we report the structures of the human AR DNA binding domain (DBD) bound to two natural AREs (C3 and MTV) in head-to-head dimer conformations, diffracting at 2.05 Å and 2.25 Å, respectively. These structures help to explain the impact of androgen insensitivity mutations on the structure integrity, DNA binding and DBD dimerization. The binding affinity of the AR DBD to different DNA motifs were measured by the BioLayer Interferometry (BLI) and further validated by Molecular Dynamics (MD) simulations. This shows that the high binding affinity of the first DBD to the upstream 5'-AGAACA-3' motif induces the cooperative binding of the second DBD to the second hexanucleotide. Our data indicate identical interaction of the DBDs to the upstream hexanucleotides, while forming an induced closer contact of the second DBD on the non-canonical hexanucleotides. The variation in binding between the DBD monomers are the result of differences in DNA occupancy, protein-protein interactions, DNA binding affinity, and DNA binding energy profiles. We propose this has functional consequences.

Identifying Key Binding Interactions Between the Cardiac L-Type Calcium Channel and Calmodulin Using Molecular Dynamics Simulations

Defects in the binding of the calcium sensing protein calmodulin (CaM) to the L-type calcium channel (CaV1.2) or to the ryanodine receptor type 2 (RyR2) can lead to dangerous cardiac arrhythmias with distinct phenotypes, such as long-QT syndrome (LQTS) and catecholaminergic ventricular tachycardia (CPVT). Certain CaM mutations lead to LQTS while other mutations lead to CPVT, but the mechanisms by which a specific mutation can lead to each disease phenotype are not well-understood. In this study, we use long, 2 μs molecular dynamics simulations and a multitrajectory approach to identify the key binding interactions between the IQ domain of CaV1.2 and CaM. Five key interactions are found between CaV1.2 and CaM in the C-lobe, 1 in the central linker, and 2 in the N-lobe. In addition, while 5 key interactions appear between residues 120-149 in the C-lobe of CaM when it interacts with CaV1.2, only 1 key interaction is found within this region of CaM when it interacts with the RyR2. We show that this difference in the distribution of key interactions correlates with the known distribution of CaM mutations that lead to LQTS or CPVT. This correlation suggests that a disruption of key binding interactions is a plausible mechanism that can lead to these two different disease phenotypes.

Conformational Dynamics of the Nucleosomal Histone H2B Tails Revealed by Molecular Dynamics Simulations

Epigenetic modifications of histone N-terminal tails play a critical role in regulating the chromatin structure and biological processes such as transcription and DNA repair. One of the key post-translational modifications (PTMs) is the acetylation of lysine residues on histone tails. Epigenetic modifications are ubiquitous in the development of diseases, such as cancer and neurological disorders. Histone H2B tails are critical regulators of nucleosome dynamics, biological processes, and certain diseases. Here, we report all-atomistic molecular dynamics (MD) simulations of the nucleosome to demonstrate that acetylation of the histone tails changes their conformational space and interaction with DNA. We perform simulations of H2B tails, critical regulators of gene regulation, in both the lysine-acetylated (ACK) and unacetylated wild type (WT) states. To explore the effects of salt concentration, we use two different NaCl concentrations to perform simulations at microsecond time scales. Salt can modulate the effects of electrostatic interactions between the DNA phosphate backbone and histone tails. Upon acetylation, H2B tails shift their secondary structure helical propensity. The number of contacts between the DNA and the H2B tail decreases. We characterize the conformational dynamics of the H2B tails by principal component analysis (PCA). The ACK tails become more compact at increased salt concentrations, but conformations from the WT tails display the most contacts with DNA at both salt concentrations. Mainly, H2B acetylation may increase the DNA accessibility for regulatory proteins to bind, which can aid in gene regulation and NCP stability.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

Binding of Phosphate Species to Ca2+ and Mg2+ in Aqueous Solution

Phosphate derivatives and their interaction with metal cations are involved in many important biological phenomena, so an accurate characterization of the phosphate-metal interaction is necessary to properly understand the role of phosphate-metal contacts in mediating biological function. Herein, we improved the standard 12-6 Lennard-Jones (LJ) potential via the usage of the 12-6-4 LJ model, which incorporates ion-induced dipole interactions. Via parameter scanning, we fine-tuned the 12-6-4 LJ polarizability values to obtain accurate absolute binding free energies for the phosphate anions H2PO4-, HPO42-, PO43- coordinating with Ca2+ and Mg2+. First, we modified the phosphate 12-6-4 LJ parameters to reproduce the solvation free energies of the series of phosphate anions using the thermodynamic integration (TI) method. Then, using the potential mean force (PMF) method, the polarizability of the metal-phosphate interaction was obtained. We show that the free energy profiles of phosphate ions coordinated to Ca2+ and Mg2+ generally show similar trends at longer metal-phosphate distances, while the absolute binding energy values increased with deprotonation. The resulting parameters demonstrate the flexibility of the 12-6-4 LJ-type nonbonded model and its usefulness in accurately describing cation-anion interactions.

Evidence of Sharp Transitions between Octahedral and Capped Trigonal Prism States of the Solvation Shell of the Aqueous Fe3+ Ion

The structure of the solvation shell of the aqueous Fe3+ ion has been a subject of controversy due to discrepancies between experiments and different levels of theory. We address this issue by performing simulations for a wide range of ion concentrations, using various potential energy functions, supplemented by density functional theory calculations of selected configurations. The solvation shell undergoes abrupt transitions between two states: a hexacoordinated octahedral (OH) state and a capped trigonal prism (CTP) state with 7-fold coordination. The lifetime of these states is dependent on concentration. In dilute FeCl3 solutions, the lifetimes of both are similar (≈1 ns). However, the lifetime of the OH state increases with ion concentration, while that of the CTP state decreases slightly. When a uniform negative background charge is used instead of explicit counterions, the lifetime of the OH state is greatly overestimated. These findings underscore the need for further experimental measurements and higher-level simulations.

Sequence Dependence in Nucleosome Dynamics

The basic packaging unit of eukaryotic chromatin is the nucleosome that contains 145-147 base pair duplex DNA wrapped around an octameric histone protein. While the DNA sequence plays a crucial role in controlling the positioning of the nucleosome, the molecular details behind the interplay between DNA sequence and nucleosome dynamics remain relatively unexplored. This study analyzes this interplay in detail by performing all-atom molecular dynamics simulations of nucleosomes, comparing the human α-satellite palindromic (ASP) and the strong positioning "Widom-601" DNA sequence at time scales of 12 μs. The simulations are performed at salt concentrations 10-20 times higher than physiological salt concentrations to screen the electrostatic interactions and promote unwrapping. These microsecond-long simulations give insight into the molecular-level sequence-dependent events that dictate the pathway of DNA unwrapping. We find that the "ASP" sequence forms a loop around SHL ± 5 for three sets of simulations. Coincident with loop formation is a cooperative increase in contacts with the neighboring N-terminal H2B tail and C-terminal H2A tail and the release of neighboring counterions. We find that the Widom-601 sequence exhibits a strong breathing motion of the nucleic acid ends. Coincident with the breathing motion is the collapse of the full N-terminal H3 tail and formation of an α-helix that interacts with the H3 histone core. We postulate that the dynamics of these histone tails and their modification with post-translational modifications (PTMs) may play a key role in governing this dynamics.

Influence of ion and hydration atmospheres on RNA structure and dynamics: insights from advanced theoretical and computational methods

RNA, a highly charged biopolymer composed of negatively charged phosphate groups, defies electrostatic repulsion to adopt well-defined, compact structures. Hence, the presence of positively charged metal ions is crucial not only for RNA's charge neutralization, but they also coherently decorate the ion atmosphere of RNA to stabilize its compact fold. This feature article elucidates various modes of close RNA-ion interactions, with a special emphasis on Mg2+ as an outer-sphere and inner-sphere ion. Through examples, we highlight how inner-sphere chelated Mg2+ stabilizes RNA pseudoknots, while outer-sphere ions can also exert long-range electrostatic interactions, inducing groove narrowing, coaxial helical stacking, and RNA ring formation. In addition to investigating the RNA's ion environment, we note that the RNA's hydration environment is relatively underexplored. Our study delves into its profound interplay with the structural dynamics of RNA, employing state-of-the-art atomistic simulation techniques. Through examples, we illustrate how specific ions and water molecules are associated with RNA functions, leveraging atomistic simulations to identify preferential ion binding and hydration sites. However, understanding their impact(s) on the RNA structure remains challenging due to the involvement of large length and long time scales associated with RNA's dynamic nature. Nevertheless, our contributions and recent advances in coarse-grained simulation techniques offer insights into large-scale structural changes dynamically linked to the RNA ion atmosphere. In this connection, we also review how different cutting-edge computational simulation methods provide a microscopic lens into the influence of ions and hydration on RNA structure and dynamics, elucidating distinct ion atmospheric components and specific hydration layers and their individual and collective impacts.

Lanthanide transport in angstrom-scale MoS2-based two-dimensional channels

Rare earth elements (REEs), critical to modern industry, are difficult to separate and purify, given their similar physicochemical properties originating from the lanthanide contraction. Here, we systematically study the transport of lanthanide ions (Ln3+) in artificially confined angstrom-scale two-dimensional channels using MoS2-based building blocks in an aqueous environment. The results show that the uptake and permeability of Ln3+ assume a well-defined volcano shape peaked at Sm3+. This transport behavior is rooted from the tradeoff between the barrier for dehydration and the strength of interactions of lanthanide ions in the confinement channels, reminiscent of the Sabatier principle. Molecular dynamics simulations reveal that Sm3+, with moderate hydration free energy and intermediate affinity for channel interaction, exhibit the smallest dehydration degree, consequently resulting in the highest permeability. Our work not only highlights the distinct mass transport properties under extreme confinement but also demonstrates the potential of dialing confinement dimension and chemistry for greener REEs separation.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

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.

Development of a Fluctuating Charge Model for Zinc-Containing Metalloproteins

Metalloproteins widely exist in biology and play important roles in various processes. To accurately simulate metalloprotein systems, modeling polarization and charge transfer effects is vital. The fluctuating charge (FQ) model can efficiently generate atomic charges and simulate the charge transfer effect; it has been developed for a wide range of applications, but few models have been specifically tailored for metalloproteins. In this study, we present a fluctuating charge model specifically for zinc-containing metalloproteins based on the extended charge equilibration (EQeq) scheme. Our model was parametrized to reproduce CM5 charges instead of RESP/CHELPG charges because the former is less dependent on the conformation or basis set, does not suffer from unphysical charges for buried atoms, and is still being able to well reproduce the molecular dipoles. During our study, we found that adding the Pauling-bond-order-like term (referred to as the "+C term" in a previous study) between the zinc ion and ligating atoms significantly improves the model's performance. Although our model was trained for four-coordinated zinc sites only, our results indicated it can well describe the atomic charges in diverse zinc sites. Morever, our model was used to generate partial charges for the metal sites in three different zinc-containing metalloproteins (with four-, five-, and six-coordinated metal sites, respectively). These charges exhibited performance comparable to that of the RESP charges in molecular dynamics (MD) simulations. Additional tests indicated our model could also well reproduce the CM5 charges when geometric changes were involved. Those results indicate that our model can efficiently calculate the atomic charges for metal sites and well simulate the charge transfer effect, which marks an important step toward developing versatile polarizable force fields for metalloproteins.

Thermodynamics of Metal-Acetate Interactions

Metal ions play crucial roles in protein- and ligand-mediated interactions. They not only act as catalysts to facilitate biological processes but are also important as protein structural elements. Accurately predicting metal ion interactions in computational studies has always been a challenge, and various methods have been suggested to improve these interactions. One such method is the 12-6-4 Lennard-Jones (LJ)-type nonbonded model. Using this model, it has been possible to successfully reproduce the experimental properties of metal ions in aqueous solution. The model includes induced dipole interactions typically ignored in the standard 12-6 LJ nonbonded model. In this we expand the applicability of this model to metal ion-carboxylate interactions. Using 12-6-4 parameters that reproduce the solvation free energies of the metal ions leads to an overestimation of metal ion-acetate interactions, thus, prompting us to fine-tune the model to specifically handle the latter. We also show that the standard 12-6 LJ model significantly falls short in reproducing the experimental binding free energy between acetate and 11 metal ions (Ni(II), Mg(II), Cu(II), Zn(II), Co(II), Cu(I), Fe(II), Mn(II), Cd(II), Ca(II), and Ag(I)). In this study, we describe optimized C4 parameters for the 12-6-4 LJ nonbonded model to be used with three widely employed water models (Transferable Intermolecular Potential with 3 Points (TIP3P), Simple Point Charge Extended (SPC/E), and Optimal Point Charge (OPC) water models). These parameters can accurately match the experimental binding free energy between 11 metal ions and acetate. These parameters can be applied to the study of metalloproteins and transition metal ion channels and transporters, as acetate serves as a representative of the negatively charged amino acid side chains from aspartate and glutamate.

Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations

Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B2), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch. Here, the refinement stage was crucial to identify the native binding configuration, as several other binding configurations were also found by McMD-based docking simulations. RBF initially binds the interface between P4 and P6 including U61 and G62, which forms a gateway where the ligand lingers until this gateway opens sufficiently to allow the ligand to pass through and slip into the hidden binding site including A48, A49, and A85.

Modeling Lanthanide Ions in Solution: A Versatile Force Field in Aqueous and Organic Solvents

In this paper, we propose a new nonpolarizable force field for describing the Ln3+ (Ln = lanthanide) series based on a 12-6-4 Lennard-Jones potential. The development of the force field was performed in pure water by adjusting both the ion-oxygen distance and the hydration free energy. This force field accurately reproduces the Ln3+ hydration properties through the series, especially the coordination number that is hardly accessible using a nonpolarizable force field. Then, the validity and the transferability of the current force field were evaluated for two different systems containing Ln3+ in various solvents, namely, 0.1 mol L-1 La(NO3)3 salts in methanol and Eu(NO3)3 salts in solvent organic phases composed of DMDOHEMA molecules in n-heptane. The good agreement between our simulations and the data available in the literature confirms the accuracy of the force field for describing the lanthanide cations in both aqueous and nonaqueous media.

Dynamics and Function of sRNA/mRNAs Under the Scrutiny of Computational Simulation Methods

Molecular dynamics simulations have proved extremely useful in investigating the functioning of proteins with atomic-scale resolution. Many applications to the study of RNA also exist, and their number increases by the day. However, implementing MD simulations for RNA molecules in solution faces challenges that the MD practitioner must be aware of for the appropriate use of this tool. In this chapter, we present the fundamentals of MD simulations, in general, and the peculiarities of RNA simulations, in particular. We discuss the strengths and limitations of the technique and provide examples of its application to elucidate small RNA's performance.

Understanding ATP Binding to DosS Catalytic Domain with a Short ATP-Lid

DosS is a heme-containing histidine kinase that triggers dormancy transformation inMycobacterium tuberculosis. Sequence comparison of the catalytic ATP-binding (CA) domain of DosS to other well-studied histidine kinases reveals a short ATP-lid. This feature has been thought to block binding of ATP to DosS's CA domain in the absence of interactions with DosS's dimerization and histidine phospho-transfer (DHp) domain. Here, we use a combination of computational modeling, structural biology, and biophysical studies to re-examine ATP-binding modalities in DosS. We show that the closed-lid conformation observed in crystal structures of DosS CA is caused by the presence of Zn2+ in the ATP binding pocket that coordinates with Glu537 on the ATP-lid. Furthermore, circular dichroism studies and comparisons of DosS CA's crystal structure with its AlphaFold model and homologous DesK reveal that residues 503-507 that appear as a random coil in the Zn2+-coordinated crystal structure are in fact part of the N-box α helix needed for efficient ATP binding. Such random-coil transformation of an N-box α helix turn and the closed-lid conformation are both artifacts arising from large millimolar Zn2+ concentrations used in DosS CA crystallization buffers. In contrast, in the absence of Zn2+, the short ATP-lid of DosS CA has significant conformational flexibility and can effectively bind AMP-PNP (Kd = 53 ± 13 μM), a non-hydrolyzable ATP analog. Furthermore, the nucleotide affinity remains unchanged when CA is conjugated to the DHp domain (Kd = 51 ± 6 μM). In all, our findings reveal that the short ATP-lid of DosS CA does not hinder ATP binding and provide insights that extend to 2988 homologous bacterial proteins containing such ATP-lids.

Amyloid-β Tetramers and Divalent Cations at the Membrane/Water Interface: Simple Models Support a Functional Role

Charge polarization at the membrane interface is a fundamental process in biology. Despite the lower concentration compared to the abundant monovalent ions, the relative abundance of divalent cations (Ca2+, Mg2+, Zn2+, Fe2+, Cu2+) in particular spaces, such as the neuron synapse, raised many questions on the possible effects of free multivalent ions and of the required protection of membranes by the eventual defects caused by the free forms of the cations. In this work, we first applied a recent realistic model of divalent cations to a well-investigated model of a polar lipid bilayer, di-myristoyl phosphatidyl choline (DMPC). The full atomistic model allows a fairly good description of changes in the hydration of charged and polar groups upon the association of cations to lipid atoms. The lipid-bound configurations were analyzed in detail. In parallel, amyloid-β 1-42 (Aβ42) peptides assembled into tetramers were modeled at the surface of the same bilayer. Two of the protein tetramers' models were loaded with four Cu2+ ions, the latter bound as in DMPC-free Aβ42 oligomers. The two Cu-bound models differ in the binding topology: one with each Cu ion binding each of the monomers in the tetramer; one with pairs of Cu ions linking two monomers into dimers, forming tetramers as dimers of dimers. The models here described provide hints on the possible role of Cu ions in synaptic plasticity and of Aβ42 oligomers in storing the same ions away from lipids. The release of structurally disordered peptides in the synapse can be a mechanism to recover ion homeostasis and lipid membranes from changes in the divalent cation concentration.

Tuning the ATP-ATP and ATP-disordered protein interactions in high ATP concentration by altering water models

The adenosine triphosphate (ATP)-protein interactions have been of great interest since the recent experimental finding of ATP's role as a hydrotrope. The interaction between ATP and disordered proteins is fundamental to the dissolution of protein aggregates and the regulation of liquid-liquid phase separation by ATP. Molecular dynamics simulation is a powerful tool for analyzing these interactions in molecular detail but often suffers from inaccuracies in describing disordered proteins and ATPs in high concentrations. Recently, several water models have been proposed to improve the description of the protein-disordered states, yet how these models work with ATP has not been explored. To this end, here, we study how water models affect ATP and alter the ATP-ATP and ATP-protein interactions for the intrinsically disordered protein, α-Synuclein. Three water models, TIP4P-D, OPC, and TIP3P, are compared, while the protein force field is fixed to ff99SBildn. The results show that ATP over-aggregates into a single cluster in TIP3P water, but monomers and smaller clusters are found in TIP4P-D and OPC waters. ATP-protein interaction is also over-stabilized in TIP3P, whereas repeated binding/unbinding of ATP to α-Synuclein is observed in OPC and TIP4P-D waters, which is in line with the recent nuclear magnetic resonance experiment. The adenine ring-mediated interaction is found to play a major role in ATP-ATP and ATP-protein contacts. Interestingly, changing Mg2+ into Na+ strengthened the electrostatic interaction and promoted ATP oligomerization and ATP-α-Synuclein binding. Overall, this study shows that changing the water model can be an effective approach to improve the properties of ATP in high concentration.

Comparison of force fields to study the zinc-finger containing protein NPL4, a target for disulfiram in cancer therapy

Molecular dynamics (MD) simulations are a powerful approach to studying the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging. NPL4 is a zinc-binding protein and works as a cofactor for p97 to regulate protein homeostasis. NPL4 is of biomedical importance and has been proposed as the target of disulfiram, a drug recently repurposed for cancer treatment. Experimental studies proposed that the disulfiram metabolites, bis-(diethyldithiocarbamate)‑copper and cupric ions, induce NPL4 misfolding and aggregation. However, the molecular details of their interactions with NPL4 and consequent structural effects are still elusive. Here, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We examined different sets of non-bonded parameters because we want to study the misfolding mechanism and cannot rule out that the zinc may detach from the protein during the process and copper replaces it. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of NPL4. Furthermore, we investigated the performance of a force field including bonded parameters to treat copper ions in NPL4 that we obtained based on QM calculations.

Chemistry and Dynamics of Supercritical Carbon Dioxide and Methane in the Slit Pores of Layered Silicates

ConspectusIn the mid 2010s, high-pressure diffraction and spectroscopic tools opened a window into the molecular-scale behavior of fluids under the conditions of many CO₂ sequestration and shale/tight gas reservoirs, conditions where CO₂ and CH₄ are present as variably wet supercritical fluids. Integrating high-pressure spectroscopy and diffraction with molecular modeling has revealed much about the ways that supercritical CO₂ and CH₄ behave in reservoir components, particularly in the slit-shaped micro- and mesopores of layered silicates (phyllosilicates) abundant in caprocks and shales. This Account summarizes how supercritical CO₂ and CH₄ behave in the slit pores of swelling phyllosilicates as functions of the H₂O activity, framework structural features, and charge-balancing cation properties at 90 bar and 323 K, conditions similar to a reservoir at ∼1 km depth. Slit pores containing cations with large radii, low hydration energy, and large polarizability readily interact with CO₂, allowing CO₂ and H₂O to adsorb and coexist in these interlayer pores over a wide range of fluid humidities. In contrast, cations with small radii, high hydration energy, and low polarizability weakly interact with CO₂, leading to reduced CO₂ uptake and a tendency to exclude CO₂ from interlayers when H₂O is abundant. The reorientation dynamics of confined CO₂ depends on the interlayer pore height, which is strongly influenced by the cation properties, framework properties, and fluid humidity. The silicate structural framework also influences CO₂ uptake and behavior; for example, smectites with increasing F-for-OH substitution in the framework take up greater quantities of CO₂. Reactions that trap CO₂ in carbonate phases have been observed in thin H₂O films near smectite surfaces, including a dissolution-reprecipitation mechanism when the edge surface area is large and an ion exchange-precipitation mechanism when the interlayer cation can form a highly insoluble carbonate. In contrast, supercritical CH₄ does not readily associate with cations, does not react with smectites, and is only incorporated into interlayer slit mesopores when (i) the pore has a z-dimension large enough to accommodate CH₄, (ii) the smectite has low charge, and (iii) the H₂O activity is low. The adsorption and displacement of CH₄ by CO₂ and vice versa have been studied on the molecular scale in one shale, but opportunities remain to examine behavioral details in this more complicated, slit-pore inclusive system.

Understanding ATP binding to DosS catalytic domain with a short ATP-lid

DosS is a heme-sensor histidine kinase that responds to redox-active stimuli in mycobacterial environments by triggering dormancy transformation. Sequence comparison of the catalytic ATP-binding (CA) domain of DosS to other well-studied histidine kinases suggests that it possesses a rather short ATP-lid. This feature has been thought to inhibit DosS kinase activity by blocking ATP binding in the absence of interdomain interactions with the dimerization and histidine phospho-transfer (DHp) domain of full-length DosS. Here, we use a combination of computational modeling, structural biology, and biophysical studies to re-examine ATP-binding modalities in DosS's CA domain. We show that the closed lid conformation observed in protein crystal structures of DosS CA is caused by the presence of a zinc cation in the ATP binding pocket that coordinates with a glutamate residue on the ATP-lid. Furthermore, circular dichroism (CD) studies and comparisons of DosS CA crystal structure with its AlphaFold model and homologous DesK reveal that a key N-box alpha-helix turn of the ATP pocket manifests as a random coil in the zinc-coordinated protein crystal structure. We note that this closed lid conformation and the random-coil transformation of an N-box alpha-helix turn are artifacts arising from the millimolar zinc concentration used in DosS CA crystallization conditions. In contrast, in the absence of zinc, we find that the short ATP-lid of DosS CA has significant conformational flexibility and can bind ATP (Kd = 53 ± 13 μM). We conclude that DosS CA is almost always bound to ATP under physiological conditions (1-5 mM ATP, sub-nanomolar free zinc) in the bacterial environment. Our findings elucidate the conformational adaptability of the short ATP-lid, its relevance to ATP binding in DosS CA and provide insights that extends to 2988 homologous bacterial proteins containing such ATP-lids.

Enabling selective zinc-ion intercalation by a eutectic electrolyte for practical anodeless zinc batteries

Two major challenges hinder the advance of aqueous zinc metal batteries for sustainable stationary storage: (1) achieving predominant Zn-ion (de)intercalation at the oxide cathode by suppressing adventitious proton co-intercalation and dissolution, and (2) simultaneously overcoming Zn dendrite growth at the anode that triggers parasitic electrolyte reactions. Here, we reveal the competition between Zn²⁺ vs proton intercalation chemistry of a typical oxide cathode using ex-situ/operando techniques, and alleviate side reactions by developing a cost-effective and non-flammable hybrid eutectic electrolyte. A fully hydrated Zn²⁺ solvation structure facilitates fast charge transfer at the solid/electrolyte interface, enabling dendrite-free Zn plating/stripping with a remarkably high average coulombic efficiency of 99.8% at commercially relevant areal capacities of 4 mAh cm^-2 and function up to 1600 h at 8 mAh cm^-2. By concurrently stabilizing Zn redox at both electrodes, we achieve a new benchmark in Zn-ion battery performance of 4 mAh cm^-2 anode-free cells that retain 85% capacity over 100 cycles at 25 °C. Using this eutectic-design electrolyte, Zn | |Iodine full cells are further realized with 86% capacity retention over 2500 cycles. The approach represents a new avenue for long-duration energy storage.

Machines on Genes through the Computational Microscope

Macromolecular machines acting on genes are at the core of life's fundamental processes, including DNA replication and repair, gene transcription and regulation, chromatin packaging, RNA splicing, and genome editing. Here, we report the increasing role of computational biophysics in characterizing the mechanisms of "machines on genes", focusing on innovative applications of computational methods and their integration with structural and biophysical experiments. We showcase how state-of-the-art computational methods, including classical and ab initio molecular dynamics to enhanced sampling techniques, and coarse-grained approaches are used for understanding and exploring gene machines for real-world applications. As this review unfolds, advanced computational methods describe the biophysical function that is unseen through experimental techniques, accomplishing the power of the "computational microscope", an expression coined by Klaus Schulten to highlight the extraordinary capability of computer simulations. Pushing the frontiers of computational biophysics toward a pragmatic representation of large multimegadalton biomolecular complexes is instrumental in bridging the gap between experimentally obtained macroscopic observables and the molecular principles playing at the microscopic level. This understanding will help harness molecular machines for medical, pharmaceutical, and biotechnological purposes.

Comparison of Empirical Zn2+ Models in Protein–DNA Complexes

Zinc ions are the second most abundant ions found in humans. Their role in proteins can be merely structural but also catalytic, owing to their transition metal character. Modelling their geometric–coordination versatility by empirical force fields is, thus, a challenging task. In this work, we evaluated three popular models, specifically designed to represent zinc ions with regard to their capability of preserving structural integrity. To this end, we performed molecular dynamics simulations of two zinc-containing protein–DNA complexes, which differed in their zinc coordination, i.e., four cysteines or two cysteines and two histidines. The most flexible non-bonded 12-6-4 Lennard–Jones-type model shows a preference for six-fold coordination of the Zn2+-ions in contradiction to the crystal structure. The cationic dummy atom model favours tetrahedral geometry, whereas the bonded extended zinc AMBER force field model, by construction, best preserves the initial geometry of a regular or slightly distorted tetrahedron. Our data renders the extended zinc AMBER force field the best model for structural zinc ions in a given geometry. In more complicated cases, though, more flexible models may be advantageous.

Tailoring water structure with high-tetrahedral-entropy for antifreezing electrolytes and energy storage at -80 °C

One of unsolved puzzles about water lies in how ion-water interplay affects its freezing point. Here, we report the direct link between tetrahedral entropy and the freezing behavior of water in Zn²⁺-based electrolytes by analyzing experimental spectra and molecular simulation results. A higher tetrahedral entropy leads to lower freezing point, and the freezing temperature is directly related to the entropy value. By tailoring the entropy of water using different anions, we develop an ultralow temperature aqueous polyaniline| |Zn battery that exhibits a high capacity (74.17 mAh g^-1) at 1 A g^-1 and -80 °C with ~85% capacity retention after 1200 cycles due to the high electrolyte ionic conductivity (1.12 mS cm^-1). Moreover, an improved cycling life is achieved with ~100% capacity retention after 5000 cycles at -70 °C. The fabricated battery delivers appreciably enhanced performance in terms of frost resistance and stability. This work serves to provide guidance for the design of ultralow temperature aqueous batteries by precisely tuning the water structure within electrolytes.

Sequence-Specific Structural Features and Solvation Properties of Transcription Factor Binding DNA Motifs: Insights from Molecular Dynamics Simulation

Sequence-specific recognition of transcription factor (TF) binding motifs in the target site of DNA over the vast amount of non-target DNA is of primary importance for the transcriptional regulation of gene expression by the TFs. Binding of TFs to the target site of DNA relies not only on the direct contact formation but also on the structural and conformational features of DNA. Recognition of DNA structural features or shape readout by proteins is an important factor in the context of TF-DNA interaction. Based on the atomistic molecular simulation, here we report the sequence-dependent unique structural features, solvation, and ion-binding properties of biologically relevant AT- and GC-rich human TF binding motifs in DNA. Counterion and water distribution around the motif is found to be sensitive to the motif sequence, which is accompanied with the DNA shape features. The motif sequence affects the electrostatic potential along the grooves, and cytosine methylation alters the DNA shape features. Characteristic solvation properties of TF binding motif DNA fragments infer that an ionic environment and hydration influences are essential to describe TF-DNA interactions.

Free-energy differences of OPC-Water and SPC/HW-Heavy-Water Models using the Bennett Acceptance Ratio

Surface tension, vapor density of OPC-water and SPC/HW-heavy-water models have been estimated at low temperatures using the scaled model. The free-energy difference, -ΔF, of n-molecules and (n-1)-molecules plus a free probe has been calculated using the Bennett acceptance ratio with the aid of Monte-Carlo simulations. Our results show that the relation between the free-energy difference divided by kBT and the number of molecules to the power minus one-third is linear for n>6. Consequently, the surface tension can be extracted from the straight line slope, whereas the vapor density can be extracted from the intercept, which is proportional to the logarithmic ratio of liquid density to that of vapor density. By scaling the free-energy differences, for at least three different temperatures, to TCT−1, we estimated the critical temperature and hence the surface tension and the vapor density at a wide range of temperatures. The free-energy differences have been calculated at 240K, 260K, and 280K for OPC-water, and at 260K, 280K, and 300K for the SPC/HW-heavy water model.

Impacts of targeting different hydration free energy references on the development of ion potentials

Hydration free energy (HFE) as the most important solvation parameter is often targeted in ion model development, even though the reported values differ by dozens of kcal mol^-1 mainly due to the experimentally undetermined HFE of the proton ΔG°(H⁺). The choice of ΔG°(H⁺) obviously affects the hydration of single ions and the relative HFE between the ions with different (magnitude or sign) charges, and the impacts of targeted HFEs on the ion solvation and ion-ion interactions are largely unrevealed. Here we designed point charge models of K⁺, Mg²⁺, Al³⁺, and Cl^- ions targeting a variety of HFE references and then investigated the HFE influences on the simulations of dilute and concentrated ion solutions and of the salt ion pairs in gas, liquid, and solid phases. Targeting one more property of ion-water oxygen distances (IOD) leaves the ion-water binding distance invariant, while the binding strength increases with the decreasing (more negative) HFE of ions as a result of a decrease in ΔG°(H⁺) for the cation and an increase in ΔG°(H⁺) for the anion. The increase in ΔG°(H⁺) leads to strengthened cation-anion interactions and thus to close ion-ion contacts, low osmotic pressures, and small activity derivatives in concentrated ion solutions as well as too stable ion pairs of the salts in different phases. The ion diffusivity and water exchange rates around the ions are simply not HFE dependent but rather more complex. Targeting both the aqueous IOD and salt crystal properties of KCl was also attempted and the comparison between different models indicates the complexity and challenge in obtaining a balanced performance between different phases using classical force fields. Our results also support that a real ΔG°(H⁺) value of -259.8 kcal mol^-1 recommended by Hünenberger and Reif guides ion models to reproduce ion-water and ion-ion interactions reasonably at relatively low salt concentrations. Simulations of a metalloprotein show that a relatively more positive ΔG°(H⁺) for Mg²⁺ model is better for a reasonable description of the metal binding network.

Neglected N-Truncated Amyloid-β Peptide and Its Mixed Cu-Zn Complexes

Amyloid-β (Aβ) peptides are involved in Alzheimer's disease (AD) development. The interactions of these peptides with copper and zinc ions also seem to be crucial for this pathology. Although Cu(II) and Zn(II) ions binding by Aβ peptides has been scrupulously investigated, surprisingly, this phenomenon has not been so thoroughly elucidated for N-truncated Aβ_4-x-probably the most common version of this biomolecule. This negligence also applies to mixed Cu-Zn complexes. From the structural in silico analysis presented in this work, it appears that there are two possible mixed Cu-Zn(Aβ_4-x) complexes with different stoichiometries and, consequently, distinct properties. The Cu-Zn(Aβ_4-x) complex with 1:1:1 stoichiometry may have a neuroprotective superoxide dismutase-like activity. On the other hand, another mixed 2:1:2 Cu-Zn(Aβ_4-x) complex is perhaps a seed for toxic oligomers. Hence, this work proposes a novel research direction for our better understanding of AD development.