Umbrella sampling: Umbrella sampling

Localization and discrimination of GG mismatch in duplex DNA by synthetic ligand-enhanced protein nanopore analysis

Mismatched base pairs in DNA are the basis of single-nucleotide polymorphism, one of the major issues in genetic diseases. However, the changes of physical and chemical properties of DNA caused by single-site mismatches are often influenced by the sequence and the structural flexibility of the whole duplex, resulting in a challenge of direct detection of the types and location of mismatches sensitively. In this work, we proposed a synthetic ligand-enhanced protein nanopore analysis of GG mismatch on DNA fragment, inspired by in silico investigation of the specific binding of naphthyridine dimer (ND) on GG mismatch. We demonstrated that both the importing and unzipping processes of the ligand-bound DNA duplex can be efficiently slowed down in α-hemolysin nanopore. This ligand-binding induced slow-down effect of DNA in nanopore is also sensitive to the relative location of the mismatch on DNA duplex. Especially, the GG mismatch close to the end of a DNA fragment, which is hard to be detected by either routine nanopore analysis or tedious nanopore sequencing, can be well differentiated by our ND-enhanced nanopore experiment. These findings provide a promising strategy to localize and discriminate base mismatches in duplex form directly at the single-molecule level.

Elucidating the Mechanism of Helium Evaporation from Liquid Water

We investigate the evaporation of trace amounts of helium solvated in liquid water using molecular dynamics simulations and theory. Consistent with experimental observations, we find a super-Maxwellian distribution of kinetic energies of evaporated helium. This excess of kinetic energy over typical thermal expectations is explained by an effective continuum theory of evaporation based on a Fokker-Planck equation, parametrized molecularly by a potential of mean force and position-dependent friction. Using this description, we find that helium evaporation is strongly influenced by the friction near the interface, which is anomalously small near the Gibbs dividing surface due to the ability of the liquid-vapor interface to deform around the gas particle. Our reduced description provides a mechanistic interpretation of trace gas evaporation as the motion of an underdamped particle in a potential subject to a viscous environment that varies rapidly across the air-water interface. From it we predict the temperature dependence of the excess kinetic energy of evaporation, which is yet to be measured.

Folding of N-terminally acetylated α-synuclein upon interaction with lipid membranes

α-Synuclein (α-syn) is an abundant presynaptic neuronal protein whose aggregation is strongly associated with Parkinson's disease. It has been proposed that lipid membranes significantly affect α-syn's aggregation process. Extensive studies have been conducted to understand the interactions between α-syn and lipid membranes and have demonstrated that the N-terminus plays a critical role. However, the dynamics of the interactions and the conformational transitions of the N-terminus of α-syn at the atomistic scale details are still highly desired. In this study, we performed extensive enhanced sampling molecular dynamics simulations to quantify the folding and interactions of wild-type and N-terminally acetylated α-syn when interacting with lipid structures. We found that N-terminal acetylation significantly increases the helicity of the first few residues in solution or when interacting with lipid membranes. The observations in simulations showed that the binding of α-syn with lipid membranes mainly follows the induced-fit model, where the disordered α-syn binds with the lipid membrane through the electrostatic interactions and hydrophobic contacts with the packing defects; after stable insertion, N-terminal acetylation promotes the helical folding of the N-terminus to enhance the anchoring, thus increasing the binding affinity. We have shown the critical role of the first N-terminal residue methionine for recognition and anchoring to the negatively charged membrane. Although N-terminal acetylation neutralizes the positive charge of Met1 that may affect the electrostatic interactions of α-syn with membranes, the increase in helicity of the N-terminus should compensate for the binding affinity. This study provides detailed insight into the folding dynamics of α-syn's N-terminus with or without acetylation in solution and upon interaction with lipids, which clarifies how the N-terminal acetylation regulates the affinity of α-syn binding to lipid membranes. It also shows how packing defects and electrostatic effects coregulate the N-terminus of α-syn folding and its interaction with membranes.

Membrane Composition Allows the Optimization of Berberine Encapsulation in Liposomes

Berberine (BBR) is a natural molecule with noteworthy pharmacological properties, including the prevention of antibiotic resistance in Gram-negative bacteria. However, its oral bioavailability is poor, thus resulting in an impaired absorption and efficacy in humans. In combination with other drugs, liposomes have been shown to enhance the availability of the drug, representing a smart delivery system to target tissues and reduce negative side effects. To date, there is a lack of studies on BBR and liposomes that enable the rationalization and molecular-based design of such formulations for future use in humans. In this work, the encapsulation of BBR into liposomes is proposed to overcome current limitations using a combination of experimental and computational assays to rationalize the membrane composition of liposomes that maximizes BBR encapsulation. First, the encapsulation efficiency was measured for several membrane compositions, revealing that it is enhanced by cholesteryl hemisuccinate and, to a lesser extent, by cholesterol. The physical basis of the BBR encapsulation efficiency and permeability was clarified using molecular dynamics simulation: using the lipid composition, one can tune the capability of membranes to attract, i.e., to adsorb, the molecules onto their surface. Overall, these findings suggest a rational strategy to maximize the encapsulation efficiency of liposomes by using negatively charged lipids, thus representing the basis for designing delivery systems for BBR, useful to treat, e.g., antibiotic resistance.

Effect of plasma-induced oxidation on NK cell immune checkpoint ligands: A computational-experimental approach

Non-thermal plasma (NTP) shows promise as a potent anti-cancer therapy with both cytotoxic and immunomodulatory effects. In this study, we investigate the chemical and biological effects of NTP-induced oxidation on several key, determinant immune checkpoints of natural killer (NK) cell function. We used molecular dynamics (MD) and umbrella sampling simulations to investigate the effect of NTP-induced oxidative changes on the MHC-I complexes HLA-Cw4 and HLA-E. Our simulations indicate that these chemical alterations do not significantly affect the binding affinity of these markers to their corresponding NK cell receptor, which is supported with experimental read-outs of ligand expression on human head and neck squamous cell carcinoma cells after NTP application. Broadening our scope to other key ligands for NK cell reactivity, we demonstrate rapid reduction in CD155 and CD112, target ligands of the inhibitory TIGIT axis, and in immune checkpoint CD73 immediately after treatment. Besides these transient chemical alterations, the reactive species in NTP cause a cascade of downstream cellular reactions. This is underlined by the upregulation of the stress proteins MICA/B, potent ligands for NK cell activation, 24 h post treatment. Taken together, this work corroborates the immunomodulatory potential of NTP, and sheds light on the interaction mechanisms between NTP and cancer cells.

Mechanistic insights into G-protein coupling with an agonist-bound G-protein-coupled receptor

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by promoting guanine nucleotide exchange. Here, we investigate the coupling of G proteins with GPCRs and describe the events that ultimately lead to the ejection of GDP from its binding pocket in the Gα subunit, the rate-limiting step during G-protein activation. Using molecular dynamics simulations, we investigate the temporal progression of structural rearrangements of GDP-bound Gs protein (Gs·GDP; hereafter GsGDP) upon coupling to the β2-adrenergic receptor (β2AR) in atomic detail. The binding of GsGDP to the β2AR is followed by long-range allosteric effects that significantly reduce the energy needed for GDP release: the opening of α1-αF helices, the displacement of the αG helix and the opening of the α-helical domain. Signal propagation to the Gs occurs through an extended receptor interface, including a lysine-rich motif at the intracellular end of a kinked transmembrane helix 6, which was confirmed by site-directed mutagenesis and functional assays. From this β2AR-GsGDP intermediate, Gs undergoes an in-plane rotation along the receptor axis to approach the β2AR-Gsempty state. The simulations shed light on how the structural elements at the receptor-G-protein interface may interact to transmit the signal over 30 Å to the nucleotide-binding site. Our analysis extends the current limited view of nucleotide-free snapshots to include additional states and structural features responsible for signaling and G-protein coupling specificity.

Hairygami: Analysis of DNA Nanostructures' Conformational Change Driven by Functionalizable Overhangs

DNA origami is a widely used method to construct nanostructures by self-assembling designed DNA strands. These structures are often used as "pegboards" for templated assembly of proteins, gold nanoparticles, aptamers, and other molecules, with applications ranging from therapeutics and diagnostics to plasmonics and photonics. Imaging these structures using atomic force microscopy (AFM) or transmission electron microscope (TEM) does not capture their full conformation ensemble as they only show their shape flattened on a surface. However, certain conformations of the nanostructure can position guest molecules into distances unaccounted for in their intended design, thus leading to spurious interactions between guest molecules that are designed to be separated. Here, we use molecular dynamics simulations to capture a conformational ensemble of two-dimensional (2D) DNA origami tiles and show that introducing single-stranded overhangs, which are typically used for functionalization of the origami with guest molecules, induces a curvature of the tile structure in the bulk. We show that the shape deformation is of entropic origin, with implications for the design of robust DNA origami breadboards as well as a potential approach to modulate structure shape by introducing overhangs. We then verify experimentally that the DNA overhangs introduce curvature into the DNA origami tiles under divalent as well as monovalent salt buffer conditions. We further experimentally verify that DNA origami functionalized with attached proteins also experiences such induced curvature. We provide the developed simulation code implementing the enhanced sampling to characterize the conformational space of DNA origami as open source software.

Effect of peptide hydrophilicity on membrane curvature and permeation

Using a well-developed reaction coordinate in umbrella sampling, we studied the single peptide permeation through a model cancerous cell membrane, varying the hydrophilicity and the charge of the peptides. Two peptides, melittin and pHD108, were studied. The permeation mechanism differs from a barrel-stave-like mechanism to toroidal pore and vesicle formation based on the number and the placement of the hydrophilic amino acids in the peptide. Membrane curvature changes dynamically as the permeation process occurs. In the case of vesicles, the peptide traverses along a smooth, homogenous pathway, whereas a rugged, steep pathway was found when lipid molecules did not line up along the wall of the membrane (barrel-stave-like mechanism). A mechanism similar to a toroidal pore consists of multiple minima. Higher free energy was found for the permeating terminal containing charged amino acid residues. Vesicle formation was found for pHD108 peptide N-terminal with a maximum membrane thinning effect of 54.4% with free energy cost of 8.20 ± 0.10 kcal mol-1 and pore radius of 2.33 ± 0.07 nm. Insights gained from this study can help to build synthetic peptides for drug delivery.

Probing atomic-scale processes at the ferrihydrite-water interface with reactive molecular dynamics

Interfacial processes involving metal (oxyhydr)oxide phases are important for the mobility and bioavailability of nutrients and contaminants in soils, sediments, and water. Consequently, these processes influence ecosystem health and functioning, and have shaped the biological and environmental co-evolution of Earth over geologic time. Here we employ reactive molecular dynamics simulations, supported by synchrotron X-ray spectroscopy to study the molecular-scale interfacial processes that influence surface complexation in ferrihydrite-water systems containing aqueousMoO 4 2 - . We validate the utility of this approach by calculating surface complexation models directly from simulations. The reactive force-field captures the realistic dynamics of surface restructuring, surface charge equilibration, and the evolution of the interfacial water hydrogen bond network in response to adsorption and proton transfer. We find that upon hydration and adsorption, ferrihydrite restructures into a more disordered phase through surface charge equilibration, as revealed by simulations and high-resolution X-ray diffraction. We observed how this restructuring leads to a different interfacial hydrogen bond network compared to bulk water by monitoring water dynamics. Using umbrella sampling, we constructed the free energy landscape of aqueousMoO 4 2 - adsorption at various concentrations and the deprotonation of the ferrihydrite surface. The results demonstrate excellent agreement with the values reported by experimental surface complexation models. These findings are important as reactive molecular dynamics opens new avenues to study mineral-water interfaces, enriching and refining surface complexation models beyond their foundational assumptions.

Tutorial on Umbrella Sampling Simulation with a Combined QM/MM Potential: The Potential of Mean Force for an SN2 Reaction in Water

We present a tutorial to carry out umbrella-sampling free-energy simulations with a combined quantum mechanical and molecular mechanical (QM/MM) potential, which may also be used in a computational or biophysical chemistry curriculum for first-year graduate and undergraduate students. In this article, we choose the Type II SN2 Menshutkin reaction between ammonia and chloromethane to construct the potential of mean force (PMF) for the reaction in aqueous solution. In this exercise, we wish to accomplish three tasks: (1) an understanding of the concept of PMF and the umbrella-sampling free-energy simulation method, (2) the use of a combined QM/MM potential in molecular dynamics simulation of chemical reactions, and (3) an understanding of solvent effects and intermolecular interactions on chemical reactions through comparison with gas-phase results. Analysis of the simulation results allows students to appreciate the difference between the transition state along a PMF from statistical simulations versus the optimized saddle point in the gas phase, the shift of transition state with respect to solvation, and the significance of electronic polarization. Through this tutorial, students will gain the necessary skills to carry out free energy simulations of chemical reactions in solution and in enzymes both in a classroom and in the laboratory.

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. We also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Influence of Wobbling Tryptophan and Mutations on PET Degradation Explored by QM/MM Free Energy Calculations

Plastic-degrading enzymes, particularly poly(ethylene terephthalate) (PET) hydrolases, have garnered significant attention in recent years as potential eco-friendly solutions for recycling plastic waste. However, understanding of their PET-degrading activity and influencing factors remains incomplete, impeding the development of uniform approaches for enhancing PET hydrolases for industrial applications. A key aspect of PET hydrolase engineering is optimizing the PET-hydrolysis reaction by lowering the associated free energy barrier. However, inconsistent findings have complicated these efforts. Therefore, our goal is to elucidate various aspects of enzymatic PET degradation by means of quantum mechanics/molecular mechanics (QM/MM) reaction simulations and analysis, focusing on the initial reaction step, acylation, in two thermophilic PET hydrolases, LCC and PES-H1, along with their highly active variants, LCCIG and PES-H1FY. Our findings highlight the impact of semiempirical QM methods on proton transfer energies, affecting the distinction between a two-step reaction involving a metastable tetrahedral intermediate and a one-step reaction. Moreover, we uncovered a concerted conformational change involving the orientation of the PET benzene ring, altering its interaction with the side-chain of the "wobbling" tryptophan from T-stacking to parallel π-π interactions, a phenomenon overlooked in prior research. Our study thus enhances the understanding of the acylation mechanism of PET hydrolases, in particular by characterizing it for the first time for the promising PES-H1FY using QM/MM simulations. It also provides insights into selecting a suitable QM method and a reaction coordinate, valuable for future studies on PET degradation processes.

Hydronium Ion Transport across the Liquid/Liquid Interface Assisted by a Phase-Transfer Catalyst: Structure and Thermodynamics Using Molecular Dynamics Simulation

Molecular dynamics simulations are used to examine the thermodynamic and structural aspects of the transfer of the classical hydronium ion (H3O+) across a water/1,2-dichloroethane (DCE) interface assisted by the phase-transfer catalyst (PTC) tetrakis(pentafluorophenyl) borate anion (TPFB-). The free energy of transfer from water to DCE of the H3O+-TPFB- ion pair is calculated to be 6 ± 1 kcal/mol, significantly less than that of the free hydronium ion (17 ± 1 kcal/mol). The ion pair is relatively stable at the interface and in the organic phase when it is accompanied by three water molecules with a small barrier to dissociation that supports its utility as a PTC. An examination of the hydration structure that accompanies the transfer of the ion pair shows that the ion pair, like the free hydronium ion, is transferred with the assistance of a finger-like water structure.

Accurate Conformation Sampling via Protein Structural Diffusion

Accurate sampling of protein conformations is pivotal for advances in biology and medicine. Although there has been tremendous progress in protein structure prediction in recent years due to deep learning, models that can predict the different stable conformations of proteins with high accuracy and structural validity are still lacking. Here, we introduce UFConf, a cutting-edge approach designed for robust sampling of diverse protein conformations based solely on amino acid sequences. This method transforms AlphaFold2 into a diffusion model by implementing a conformation-based diffusion process and adapting the architecture to process diffused inputs effectively. To counteract the inherent conformational bias in the Protein Data Bank, we developed a novel hierarchical reweighting protocol based on structural clustering. Our evaluations demonstrate that UFConf outperforms existing methods in terms of successful sampling and structural validity. The comparisons with long-time molecular dynamics show that UFConf can overcome the energy barrier existing in molecular dynamics simulations and perform more efficient sampling. Furthermore, We showcase UFConf's utility in drug discovery through its application in neural protein-ligand docking. In a blind test, it accurately predicted a novel protein-ligand complex, underscoring its potential to impact real-world biological research. Additionally, we present other modes of sampling using UFConf, including partial sampling with fixed motif, Langevin dynamics, and structural interpolation.

Optimizing selectivity via membrane molecular packing manipulation for simultaneous cation and anion screening

Advancing membranes with enhanced solute-solute selectivity is essential for expanding membrane technology applications, yet it presents a notable challenge. Drawing inspiration from the unparalleled selectivity of biological systems, which benefit from the sophisticated spatial organization of functionalities, we posit that manipulating the arrangement of the membrane's building blocks, an aspect previously given limited attention, can address this challenge. We demonstrate that optimizing the face-to-face orientation of building blocks during the assembly of covalent-organic-framework (COF) membranes improves ion-π interactions with multivalent ions. This optimization leads to extraordinary selectivity in differentiating between monovalent cations and anions from their multivalent counterparts, achieving selectivity factors of 214 for K+/Al3+ and 451 for NO3-/PO43-. Leveraging this attribute, the COF membrane facilitates the direct extraction of NaCl from seawater with a purity of 99.57%. These findings offer an alternative approach for designing highly selective membrane materials, offering promising prospects for advancing membrane-based technologies.

Efficient acceleration of the convergence of the minimum free energy path via a path-planning generated initial guess

We demonstrate that combining a shifted clustering algorithm with a fast-marching-based algorithm can generate accurate approximations of the minimum energy path (MEP) given a free energy landscape (FEL). Using this approximation as the initial guess for the MEP, followed by further refinement with the string method (referred to as the fast marching tree (FMT)-string combined approach), significantly reduces the number of iterations required for MEP convergence. This approach saves substantial time compared to using linear interpolation (LI) for the initial guess. Our method offers a viable solution for obtaining an effective initial guess of the MEP when an approximate or converged FEL is available. This work highlights the potential of applying FMT-based approaches to extract the MEP in chemical reactions.

Sickle Cell Hemoglobin "Drugged" with Cyclic Peptides Is Aggregation Incompetent

Sickle cell disease (SCD) is a monogenic blood disorder associated with a mutation in the hemoglobin subunit β gene encoding for the β-globin of normal adult hemoglobin (HbA). This mutation transcribes into a Glu-β6 → Val-β6 substitution in the β-globins, inducing the polymerization of this hemoglobin form (HbS) when in the T-state. Despite advances in stem cell and gene therapy, and the recent approval of a new antisickling drug, therapeutic limitations persist. Herein, we demonstrate through molecular dynamics and umbrella sampling, that (unrestrained) blockage of the hydrophobic pocket involved in the lateral contact of the HbS fibers by 5-mer cyclic peptides, recently proposed as SCD aggregation inhibitors (Neto, V.; J. Med. Chem. 2023, 66, 16062-16074), is enough to turn the dimerization of HbS thermodynamically unfavorable. Among these potential drugs, some exhibit an estimated pocket abandonment probability of around 15-20% within the simulations' time frame, and an impressive specificity toward the mutated Val-β6. Additionally, we show that the dimerization can be thermodynamically unfavored by blocking a nearby region while the pocket remains vacant. These results are compared with curcumin, an antisickling molecule and a pan-assay interference compound, with a good binding affinity for different proteins and protein domains. Our results confirm the potential of some of these cyclic peptides as antisickling drug candidates to reduce the concentration of aggregation-competent HbS.

Molecular Basis of the Substrate Specificity of Phosphotriesterase from Pseudomonas diminuta: A Combined QM/MM MD and Electron Density Study

The occurrence of organophosphorus compounds, pesticides, and flame-retardants in wastes is an emerging ecological problem. Bacterial phosphotriesterases are capable of hydrolyzing some of them. We utilize modern molecular modeling tools to study the hydrolysis mechanism of organophosphorus compounds with good and poor leaving groups by phosphotriesterase from Pseudomonas diminuta (Pd-PTE). We compute Gibbs energy profiles for enzymes with different cations in the active site: native Zn2+cations and Co2+cations, which increase the steady-state rate constant. Hydrolysis occurs in two elementary steps via an associative mechanism and formation of the pentacoordinated intermediate. The first step, a nucleophilic attack, occurs with a low energy barrier independently of the substrate. The second step has a low energy barrier and considerable stabilization of products for substrates with good leaving groups. For substrates with poor leaving groups, the reaction products are destabilized relative to the ES complex that suppresses the reaction. The reaction proceeds with low energy barriers for substrates with good leaving groups with both Zn2+and Co2+cations in the active site; thus, the product release is likely to be a limiting step. Electron density and geometry analysis of the QM/MM MD trajectories of the intermediate states with all considered compounds allow us to discriminate substrates by their ability to be hydrolyzed by the Pd-PTE. For hydrolyzable substrates, the cleaving bond between a phosphorus atom and a leaving group is elongated, and electron density depletion is observed on the Laplacian of electron density maps.

Elucidating the Selective Mechanism of Drugs Targeting Cyclin-Dependent Kinases with Integrated MetaD-US Simulation

Cyclin-dependent kinases (CDKs), including CDK12 and CDK13, play crucial roles in regulating the cell cycle and RNA polymerase II activity, making them vital targets for cancer therapies. SR4835 is a selective inhibitor of CDK12/13, showing significant potential for treating triple-negative breast cancer. To elucidate the selective mechanism of SR4835 among three CDKs (CDK13/12/9), we developed an innovative enhanced sampling method, integrated well-tempered metadynamics-umbrella sampling (IMUS). IMUS synergistically combines the comprehensive pathway exploration capability of well-tempered metadynamics (WT-MetaD) with the precise free energy calculation capability of umbrella sampling, enabling the efficient and accurate characterization of drug-target interactions. The accurate calculation of binding free energy and the detailed analysis of the kinetic mechanism of the drug-target interaction using IMUS successfully elucidate the drug selectivity mechanism targeting the three CDKs, showing that the selectivity is primarily arising from differences in the stability of H-bonds within the Hinge region of the kinases and the interaction patterns during the protein-ligand recognition process. These findings also underscore the utility of IMUS in efficiently and accurately capturing drug-target interaction processes with clear mechanisms.

Entropy-favorable adsorption of polymer-grafted nanoparticles at fluid-fluid interfaces

The adsorption of polymer-grafted nanoparticles at interfaces is a problem of fundamental interest in physics and soft materials. This adsorption behavior is governed by the interplay between interaction potentials and entropic effects. Here, we use molecular dynamics simulations and umbrella sampling methods to study the adsorption behavior of a Janus-like homopolymer-grafted nanoparticle at fluid-fluid interfaces. By calculating the potential of the mean force as the particle moves from fluid A to the interface, the adsorption energy Ea can be obtained. When two homopolymer chains with types A and B are grafted to the opposite poles of the particle, Ea shows a scaling behavior with respect to chain length N: Ea ∝ N0.598. This is determined by the interactions between polymers and fluids. The enthalpy dominates, and the entropy effects mainly come from the rotational entropy loss of the polymer-grafted nanoparticle at interfaces, which disfavors the stabilization of particles at interfaces. When the grafted polymer number m is large, the adsorption energy exhibits a linear dependence on m. While the enthalpy dominates the behavior, the entropy becomes significant at a larger chain length of N = 15, where the configurational entropy of the polymer chains dominates the entropy of the system. The globule-coil transition occurs when polymers move from poor solvents to good solvents, increasing the configurational entropy and favoring the stabilization of particles at interfaces. Our study provides novel insights into the stabilization mechanism of polymer-grafted nanoparticles at interfaces and reveals the stabilization mechanism favored by the configurational entropy of grafted polymer chains.

Contact-Map-Driven Exploration of Heterogeneous Protein-Folding Paths

We have recently shown how physically realizable protein-folding pathways can be generated using directed walks in the space of inter-residue contact-maps; combined with a back-transformation to move from protein contact-maps to Cartesian coordinates, we have demonstrated how this approach can generate protein-folding trajectory ensembles without recourse to molecular dynamics. In this article, we demonstrate that this framework can be used to study a challenging protein-folding problem that is known to exhibit two different folding paths which were previously identified through molecular dynamics simulation at several different temperatures. From the viewpoint of protein-folding mechanism prediction, this particular problem is extremely challenging to address, specifically involving folding to an identical nontrivial compact native structure along distinct pathways defined by heterogeneous secondary structural elements. Here, we show how our previously reported contact-map-based protein-folding strategy can be significantly enhanced to enable accurate and robust prediction of heterogeneous folding paths by (i) introducing a novel topologically informed metric for comparing two protein contact maps, (ii) reformulating our graph-represented folding path generation, and (iii) introducing a new and more reliable structural back-mapping algorithm. These changes improve the reliability of generating structurally sound folding intermediates and dramatically decrease the number of physically irrelevant folding intermediates generated by our previous simulation strategy. Most importantly, we demonstrate how our enhanced folding algorithm can successfully identify the alternative folding mechanisms of a multifolding-pathway protein, in line with direct molecular dynamics simulations.

Insights into the silica scaling behaviors in membrane distillation and anti-scaling mechanism of functional polymers

Silica scaling imposes a significant limitation on the efficacy of membrane distillation (MD) in the treatment of hypersaline wastewater. The complex dynamic behaviors of silica at the membrane-water-air interface and the poor understanding of molecular-level anti-scaling mechanism hampers the development of effective antiscalants for mitigating silica scaling in MD. Despite using functional polymers to prevent silica polymerization, the inhibition mechanisms are unclear. Here, the kinetic process of silica scaling during MD and the potential anti-scaling mechanism of poly-ethylenimine (PEI) were investigated at the molecular level via molecular dynamics simulations. The investigation reveals that silica scales were more likely to adhere to the water-PTFE interface with a free energy potential well of -40.0 kJ mol-1 than that of the water-air interface with a -11.4 kJ mol-1 potential well. Silica scales falling at the water-air interface also migrated on the water-air interface until captured by the PTFE membrane. In this work, a representative functional amino-rich polymer PEI was constructed as silica inhibitors and its scale inhibition mechanism was elucidated. Notably, the inclusion of PEI increased the free-energy barriers for the silica polymerization reaction from 72.0 kJ mol-1 to 86.1 kJ mol-1, compared to scenarios without the antiscalants. Moreover, quantitative structure-activity relationships (QSAR) model of ΔGwater-silica was developed to predict the anti-scaling efficiencies of typical antiscalants based on machine learning method. These findings provide valuable insights into enhancing the efficiency of silica scaling mitigation strategies.

A New Route to the Prebiotic Synthesis of Glycine via Ab Initio-Based Machine Learning Calculations

In this work, we study the synthesis of glycine, the simplest amino acid, using ab initio molecular dynamics and enhanced sampling techniques to explore and quantify novel potential pathways. Our protocol integrates state-of-the-art machine learning approaches, allowing us to sample relevant chemical spaces more efficiently. We discover a novel "oxyglycolate path", distinct from the "standard" Strecker mechanism, identify new intermediates, and provide a full thermodynamic characterization of all reaction steps. This alternative pathway aligns better with meteoritic and experimental observations, paving the way for further investigations. Integrating quantum accuracy and machine learning in prebiotic chemistry represents a methodological milestone advancing the exploration of life's prebiotic origins.

Molecular Dynamics Simulation Investigation of Freezing Point Depression in NaClO4 Electrolyte Solution by CaCl2

The development of inorganic antifreeze electrolytes is of paramount importance for the application of sodium-ion batteries under low-temperature conditions. However, there is little reported about their molecular mechanism for lowering the freezing point of electrolytes. Therefore, this study explores the mechanism by which CaCl2 lowers the freezing point of the NaClO4 electrolyte. Hexagonal ice (ice Ih) was used as the ice seed, and CaCl2 was selected as the antifreeze agent. The coexistence system of ice and solution was constructed to simulate the freezing process. It was found that there is ion rejection at the ice layer, with ions predominantly distributed in the solution. Over time, ions form an ion adsorption layer at the ice-solution interface. The radial distribution function (RDF) and spatial distribution function (SDF) of Na+, ClO4-, Ca2+, and Cl- revealed that ions form the first solvation shells with water molecules. The interaction energy between ions and water molecules is greater than that between ice nuclei and water. Therefore, ions are better able to maintain the stability of their solvation shells and inhibit the growth of ice Ih through a mechanism of competition for water molecules. Furthermore, the dissolution free energy of Na+ and Ca2+ in the aqueous phase was studied. The results indicated that Ca2+ has a stronger affinity for water molecules than Na+, making it more competitive in competing for water with ice Ih. Therefore, CaCl2 in NaClO4 solution can reduce the freezing point. This work provides a molecular-level understanding of how CaCl2 reduces the freezing point of NaClO4 solution, which is beneficial for designing strategies for low-temperature electrolytes.

Effect of ion to ligand ratio on the aqueous to organic relative solubility of a lanthanide-ligand complex

In the solvent extraction of rare earth elements, mechanistic aspects remain unclear regarding where and how extractant molecules coordinate metal ions and transport them from the aqueous phase into the organic phase. Molecular dynamics simulations were used to examine how unprotonated di(2-ethylhexyl)phosphoric acid (DEHP-) ligands that coordinate the Gd3+ ion can transfer the ion across the water-organic interface. Using the umbrella sampling technique, potential of mean force profiles were constructed to quantify the relative solubility of the Gd3+ ion coordinated to 0-3 DEHP- ligands in either water, 1-octanol, or hexane solvents and at the water-organic interfaces. The simulations show the Gd-DEHP- complexes, at varying Ln-ligand ratios, preferentially solvate on water-organic interfaces. While the Gd(DEHP-)3 complex will diffuse past the aqueous-organic interface into the octanol solvent, it is thermodynamically preferred for the Gd(DEHP-)3 complex to remain in the water-hexane interface when there is no amphiphilic layer of excess ligand.

Unleashing the Power of Evolution in Xylanase Engineering: Investigating the Role of Distal Mutation Regulation

The drive to enhance enzyme performance in industrial applications frequently clashes with the practical limitations of exhaustive experimental screening, underscoring the urgency for more refined and strategic methodologies in enzyme engineering. In this study, xylanase Xyl-1 was used as the model, coupling evolutionary insights with energy functions to obtain theoretical potential mutants, which were subsequently validated experimentally. We observed that mutations in the nonloop region primarily aimed at enhancing stability and also encountered selective pressure for activity. Notably, mutations in this region simultaneously boosted the Xyl-1 stability and activity, achieving a 65% success rate. Using a greedy strategy, mutant M4 was developed, achieving a 12 °C higher melting temperature and doubled activity. By integration of spectroscopy, crystallography, and quantum mechanics/molecular mechanics molecular dynamics, the mechanism behind the enhanced thermal stability of M4 was elucidated. It was determined that the activity differences between M4 and the wild type were primarily driven by dynamic factors influenced by distal mutations. In conclusion, the study emphasizes the pivotal role of evolution-based approaches in augmenting the stability and activity of the enzymes. It sheds light on the unique adaptive mechanisms employed by various structural regions of proteins and expands our understanding of the intricate relationship between distant mutations and enzyme dynamics.

Wild-Type α-Synuclein Structure and Aggregation: A Comprehensive Coarse-Grained and All-Atom Molecular Dynamics Study

α-Synuclein (α-syn) is a 140 amino acid intrinsically disordered protein (IDP) and the primary component of cytotoxic oligomers implicated in the etiology of Parkinson's disease (PD). While IDPs lack a stable three-dimensional structure, they sample a heterogeneous ensemble of conformations that can, in principle, be assessed through molecular dynamics simulations. However, describing the structure and aggregation of large IDPs is challenging due to force field (FF) accuracy and sampling limitations. To cope with the latter, coarse-grained (CG) FFs emerge as a potential alternative at the expense of atomic detail loss. Whereas CG models can accurately describe the structure of the monomer, less is known about aggregation. The latter is key for assessing aggregation pathways and designing aggregation inhibitor drugs. Herein, we investigate the structure and dynamics of α-syn using different resolution CG (Martini3 and Sirah2) and all-atom (Amber99sb and Charmm36m) FFs to gain insight into the differences and resemblances between these models. The dependence of the magnitude of protein-water interactions and the putative need for enhanced sampling (replica exchange) methods in CG simulations are analyzed to distinguish between force field accuracy and sampling limitations. The stability of the CG models of an α-syn fibril was also investigated. Additionally, α-syn aggregation was studied through umbrella sampling for the CG models and CG/all-atom models for an 11-mer peptide (NACore) from an amyloidogenic domain of α-syn. Our results show that despite the α-syn structures of Martini3 and Sirah2 with enhanced protein-water interactions being similar, major differences exist concerning aggregation. The Martini3 fibril is not stable, and the binding free energy of α-syn and NACore is positive, opposite to Sirah2. Sirah2 peptides in a zwitterionic form, in turn, display termini interactions that are too strong, resulting in end-to-end orientation. Sirah2, with enhanced protein-water interactions and neutral termini, provides, however, a peptide aggregation free energy profile similar to that found with all-atom models. Overall, we find that Sirah2 with enhanced protein-water interactions is suitable for studying protein-protein and protein-drug aggregation.

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids-including the density response, surface tension and size-dependent cavitation free energies-for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

Parameter Optimization Method in Multidimensional Umbrella Sampling

Umbrella sampling (US) is an effective method for calculating free-energy landscapes (FELs). However, the complexity of controlling the sampling positions complicates multidimensional FEL calculations. In this study, we proposed a method for controlling sampling by optimizing the US parameters. This method comprises the introduction of a target point and the optimization of the parameters to sample a window around this point. We approximated each window to normal distributions using an umbrella integration method and calculated the divergences between the window distributions and the state distributed at the target position by a variationally enhanced sampling method. Thus, the minimization of the divergence facilitated sampling around the target point, after which the parameters could be optimized on the fly while performing equilibration simulation. In practice, our method employs bias potentials with off-diagonal terms, ensuring a more efficient calculation of multidimensional FEL. Additionally, we developed an algorithm for determining the target point for automated FEL search; the algorithm samples in a specified direction while controlling the overlap of distributions. We performed three different FEL calculations as examples: (1) the calculation of the permeation of a water molecule through a lipid bilayer (one-dimensional FEL), (2) the calculation of the internal structural changes in alanine dipeptide in water (two-dimensional FEL), and (3) the calculation of the internal structural changes from a β-strand structure to an α-helix structure in alanine decapeptide (Ala10, 16-dimensional FEL). These results confirmed that our method could control the number of US windows and calculate the high-dimensional FELs that could not be evaluated by the conventional US method.

PepBinding: A Workflow for Predicting Peptide Binding Structures by Combining Peptide Docking and Peptide Gaussian Accelerated Molecular Dynamics Simulations

Predicting protein-peptide interactions is crucial for understanding peptide binding processes and designing peptide drugs. However, traditional computational modeling approaches face challenges in accurately predicting peptide-protein binding structures due to the slow dynamics and high flexibility of the peptides. Here, we introduce a new workflow termed "PepBinding" for predicting peptide binding structures, which combines peptide docking, all-atom enhanced sampling simulations using the Peptide Gaussian accelerated Molecular Dynamics (Pep-GaMD) method, and structural clustering. PepBinding has been demonstrated on seven distinct model peptides. In peptide docking using HPEPDOCK, the peptide backbone root-mean-square deviations (RMSDs) of their bound conformations relative to X-ray structures ranged from 3.8 to 16.0 Å, corresponding to the medium to inaccurate quality models according to the Critical Assessment of PRediction of Interactions (CAPRI) criteria. The Pep-GaMD simulations performed for only 200 ns significantly improved the docking models, resulting in five medium and two acceptable quality models. Therefore, PepBinding is an efficient workflow for predicting peptide binding structures and is publicly available at https://github.com/MiaoLab20/PepBinding.

Multiple Parameter Replica Exchange Gaussian Accelerated Molecular Dynamics for Enhanced Sampling and Free Energy Calculation of Biomolecular Systems

This study introduces a novel method named multiple parameter replica exchange Gaussian accelerated molecular dynamics (MP-Rex-GaMD), building on the Gaussian accelerated molecular dynamics (GaMD) algorithm. GaMD enhances sampling and retrieves free energy information for biomolecular systems by adding a harmonic boost potential to smooth the potential energy surface without the need for predefined reaction coordinates. Our innovative approach advances the acceleration power and energetic reweighting accuracy of GaMD by incorporating a replica exchange algorithm that enables the exchange of multiple parameters, including the GaMD boost parameters of force constant and energy threshold, as well as temperature. Applying MP-Rex-GaMD to the three model systems of dialanine, chignolin, and HIV protease, we demonstrate its superior capability over conventional molecular dynamics and GaMD simulations in exploring protein conformations and effectively navigating various biomolecular states across energy barriers. MP-Rex-GaMD allows users to accurately map free energy landscapes through energetic reweighting, capturing the ensemble of biomolecular states from low-energy conformations to rare high-energy transitions within practical computational time scales.

Molecular Basis of Influence of A501X Mutations in Penicillin-Binding Protein 2 of Neisseria gonorrhoeae Strain 35/02 on Ceftriaxone Resistance

The increase in the resistance of mutant strains of Neisseria gonorrhoeae to the antibiotic ceftriaxone is pronounced in the decrease in the second-order acylation rate constant, k2/KS, by penicillin-binding protein 2 (PBP2). These changes can be caused by both the decrease in the acylation rate constant, k2, and the weakening of the binding affinity, i.e., an increase in the substrate constant, KS. A501X mutations in PBP2 affect second-order acylation rate constants. The PBP2A501V variant exhibits a higher k2/KS value, whereas for PBP2A501R and PBP2A501P variants, these values are lower. We performed molecular dynamic simulations with both classical and QM/MM potentials to model both acylation energy profiles and conformational dynamics of four PBP2 variants to explain the origin of k2/KS changes. The acylation reaction occurs in two elementary steps, specifically, a nucleophilic attack by the oxygen atom of the Ser310 residue and C-N bond cleavage in the β-lactam ring accompanied by the elimination of the leaving group of ceftriaxone. The energy barrier of the first step increases for PBP2 variants with a decrease in the observed k2/KS value. Submicrosecond classic molecular dynamic trajectories with subsequent cluster analysis reveal that the conformation of the β3-β4 loop switches from open to closed and its flexibility decreases for PBP2 variants with a lower k2/KS value. Thus, the experimentally observed decrease in the k2/KS in A501X variants of PBP2 occurs due to both the decrease in the acylation rate constant, k2, and the increase in KS.

Emerging Atomistic Modeling Methods for Heterogeneous Electrocatalysis

Heterogeneous electrocatalysis lies at the center of various technologies that could help enable a sustainable future. However, its complexity makes it challenging to accurately and efficiently model at an atomic level. Here, we review emerging atomistic methods to simulate the electrocatalytic interface with special attention devoted to the components/effects that have been challenging to model, such as solvation, electrolyte ions, electrode potential, reaction kinetics, and pH. Additionally, we review relevant computational spectroscopy methods. Then, we showcase several examples of applying these methods to understand and design catalysts relevant to green hydrogen. We also offer experimental views on how to bridge the gap between theory and experiments. Finally, we provide some perspectives on opportunities to advance the field.

Good Rates From Bad Coordinates: The Exponential Average Time-dependent Rate Approach

Our ability to calculate rate constants of biochemical processes using molecular dynamics simulations is severely limited by the fact that the time scales for reactions, or changes in conformational state, scale exponentially with the relevant free-energy barrier heights. In this work, we improve upon a recently proposed rate estimator that allows us to predict transition times with molecular dynamics simulations biased to rapidly explore one or several collective variables (CVs). This approach relies on the idea that not all bias goes into promoting transitions, and along with the rate, it estimates a concomitant scale factor for the bias termed the "CV biasing efficiency" γ. First, we demonstrate mathematically that our new formulation allows us to derive the commonly used Infrequent Metadynamics (iMetaD) estimator when using a perfect CV, where γ = 1. After testing it on a model potential, we then study the unfolding behavior of a previously well characterized coarse-grained protein, which is sufficiently complex that we can choose many different CVs to bias, but which is sufficiently simple that we are able to compute the unbiased rate directly. For this system, we demonstrate that predictions from our new Exponential Average Time-Dependent Rate (EATR) estimator converge to the true rate constant more rapidly as a function of bias deposition time than does the previous iMetaD approach, even for bias deposition times that are short. We also show that the γ parameter can serve as a good metric for assessing the quality of the biasing coordinate. We demonstrate that these results hold when applying the methods to an atomistic protein folding example. Finally, we demonstrate that our approach works when combining multiple less-than-optimal bias coordinates, and adapt our method to the related "OPES flooding" approach. Overall, our time-dependent rate approach offers a powerful framework for predicting rate constants from biased simulations.

Chemical language modeling with structured state space sequence models

Generative deep learning is reshaping drug design. Chemical language models (CLMs) - which generate molecules in the form of molecular strings - bear particular promise for this endeavor. Here, we introduce a recent deep learning architecture, termed Structured State Space Sequence (S4) model, into de novo drug design. In addition to its unprecedented performance in various fields, S4 has shown remarkable capabilities to learn the global properties of sequences. This aspect is intriguing in chemical language modeling, where complex molecular properties like bioactivity can 'emerge' from separated portions in the molecular string. This observation gives rise to the following question: Can S4 advance chemical language modeling for de novo design? To provide an answer, we systematically benchmark S4 with state-of-the-art CLMs on an array of drug discovery tasks, such as the identification of bioactive compounds, and the design of drug-like molecules and natural products. S4 shows a superior capacity to learn complex molecular properties, while at the same time exploring diverse scaffolds. Finally, when applied prospectively to kinase inhibition, S4 designs eight of out ten molecules that are predicted as highly active by molecular dynamics simulations. Taken together, these findings advocate for the introduction of S4 into chemical language modeling - uncovering its untapped potential in the molecular sciences.

Rational Design of Targeted Gold Nanoclusters with High Affinity to Integrin αvβ3 for Combination Cancer Therapy

The unique attributes of targeted nano-drug delivery systems (TNDDSs) over conventional cancer therapies in suppressing off-target effects make them one of the most promising options for cancer treatment. There is evidence that the density of surface-conjugated ligands is a crucial factor in achieving the desired therapeutic efficacy of TNDDSs, but this is hardly manageable in conventional nanomaterials. In this context, ligand-protected gold nanoclusters (AuNCs) are excellent candidates for developing new TNDDSs with a unique control on their surface functionalities, thus helping to achieve enhanced delivery performance. Here, we study the interactions and binding free energies between ten different functionalized Au144(SR)60 (SR = thiolate ligand) nanoclusters and integrin αvβ3 using molecular dynamics simulations and the umbrella sampling method to obtain the optimal formulations. The AuNCs were functionalized with anticancer drugs (5-fluorouracil or signaling pathways inhibitors, such as capivasertib, linifanib, tanespimycin, and taselisib) and integrin-targeting peptides (RGD4C or QS13), and we identified the optimal mixed ligand layer to enhance their binding affinity to the cancer cell receptor. The results showed that changing the proportions of the same type of ligands on the surface of AuNCs led to differences of up to 38 kcal/mol in computed binding free energies. RGD4C as the targeting peptide resulted in greater affinity for αvβ3, and in most formulations studied, a higher amount of drug than peptide was needed. Polar and charged residues, such as Ser123, Asp150, Tyr178, Arg214, and Asp251 were found to play a significant role in AuNC binding. Our simulations also revealed that Mn2+ cations are crucial for stabilizing the αvβ3-AuNC complex. These findings demonstrate the potential of carefully designing the surface composition of TNDDSs to optimize their target affinity and specificity.

Estimating Free-Energy Surfaces and Their Convergence from Multiple, Independent Static and History-Dependent Biased Molecular-Dynamics Simulations with Mean Force Integration

Addressing the sampling problem is central to obtaining quantitative insight from molecular dynamics simulations. Adaptive biased sampling methods, such as metadynamics, tackle this issue by perturbing the Hamiltonian of a system with a history-dependent bias potential, enhancing the exploration of the ensemble of configurations and estimating the corresponding free energy surface (FES). Nevertheless, efficiently assessing and systematically improving their convergence remains an open problem. Here, building on mean force integration (MFI), we develop and test a metric for estimating the convergence of FESs obtained by combining asynchronous, independent simulations subject to diverse biasing protocols, including static biases, different variants of metadynamics, and various combinations of static and history-dependent biases. The developed metric and the ability to combine independent simulations granted by MFI enable us to devise strategies to systematically improve the quality of FES estimates. We demonstrate our approach by computing FES and assessing the convergence of a range of systems of increasing complexity, including one- and two-dimensional analytical FESs, alanine dipeptide, a Lennard-Jones supersaturated vapor undergoing liquid droplet nucleation, and the model of a colloidal system crystallizing via a two-step mechanism. The methods presented here can be generally applied to biased simulations and are implemented in pyMFI, a publicly accessible, open-source Python library.

Development of a machine learning interatomic potential for exploring pressure-dependent kinetics of phase transitions in germanium

We introduce a data-driven potential aimed at the investigation of pressure-dependent phase transitions in bulk germanium, including the estimate of kinetic barriers. This is achieved by suitably building a database including several configurations along minimum energy paths, as computed using the solid-state nudged elastic band method. After training the model based on density functional theory (DFT)-computed energies, forces, and stresses, we provide validation and rigorously test the potential on unexplored paths. The resulting agreement with the DFT calculations is remarkable in a wide range of pressures. The potential is exploited in large-scale isothermal-isobaric simulations, displaying local nucleation in the R8 to β-Sn pressure-induced phase transformation, taken here as an illustrative example.

Dimerization of Full-Length Aβ-42 Peptide: A Comparison of Different Force Fields and Water Models

Among the two isoforms of amyloid-β i. e., Aβ-40 and Aβ-42, Aβ-42 is more toxic due to its increased aggregation propensity. The oligomerization pathways of amyloid-β may be investigated by studying its dimerization process at an atomic level. Intrinsically disordered proteins (IDPs) lack well-defined structures and are associated with numerous neurodegenerative disorders. Molecular dynamics simulations of these proteins are often limited by the choice of parameters due to inconsistencies in the empirically developed protein force fields and water models. To evaluate the accuracy of recently developed force fields for IDPs, we study the dimerization of full-length Aβ-42 in aqueous solution with three different combinations of AMBER force field parameters and water models such as ff14SB/TIP3P, ff19SB/OPC, and ff19SB/TIP3P using classical MD and Umbrella Sampling method. This work may be used as a benchmark to compare the performance of different force fields for the simulations of IDPs.

The Effects of External Interfaces on Hydrophobic Interactions I: Smooth Surface

External interfaces, such as the air-water and solid-liquid interfaces, are ubiquitous in nature. Hydrophobic interactions are considered the fundamental driving force in many physical and chemical processes occurring in aqueous solutions. It is important to understand the effects of external interfaces on hydrophobic interactions. According to the structural studies on liquid water and the air-water interface, the external interface primarily affects the structure of the topmost water layer (interfacial water). Therefore, an external interface may affect hydrophobic interactions. The effects of interfaces on hydrophobicity are related not only to surface molecular polarity but also to the geometric characteristics of the external interface, such as shape and surface roughness. This study is devoted to understanding the effects of a smooth interface on hydrophobicity. Due to hydrophobic interactions, the solutes tend to accumulate at external interfaces to maximize the hydrogen bonding of water. Additionally, these can be demonstrated by the calculated potential mean forces (PMFs) using molecular dynamic (MD) simulations.

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragment variable (scFv) derived from monoclonal antibodies (mAbs). scFv selection is consequently a fundamental step for CAR construction, to ensure accurate and effective CAR signaling toward tumor antigen binding. However, conventional in vitro and in vivo biological approaches to compare different scFv-derived CARs are expensive and labor-intensive. With the aim to predict the finest scFv binding before CAR-T cell engineering, we performed artificial intelligence (AI)-guided molecular docking and steered molecular dynamics analysis of different anti-CD30 mAb clones. Virtual computational scFv screening showed comparable results to surface plasmon resonance (SPR) and functional CAR-T cell in vitro and in vivo assays, respectively, in terms of binding capacity and anti-tumor efficacy. The proposed fast and low-cost in silico analysis has the potential to advance the development of novel CAR constructs, with a substantial impact on reducing time, costs, and the need for laboratory animal use.

Molecular Dynamics Simulation Study of the Self-Assembly of Tau-Derived PHF6 and Its Inhibition by Oleuropein Aglycone from Extra Virgin Olive Oil

Alzheimer's disease (AD) and other taupathies are neurodegenerative disorders associated with the amyloid deposition of the Tau protein in the brain. This amyloid formation may be inhibited by small molecules, which is recognized as one of the best therapeutic strategies to stop the progression of the disease. This work focuses on the small nucleating segment, hexapeptide-paired helical filament 6 (PHF6), responsible for Tau aggregation. Using computational modeling and classical molecular dynamics simulations, we show that PHF6 monomers collapse in water to form β-sheet rich structures, and the main olive oil polyphenol oleuropein aglycone (OleA) prevents peptide aggregation significantly. We gradually increase the ratio of the PHF6-OleA from 1:1 to 1:3 and find that for the 1:1 ratio, the peptide monomers are prone to form aggregated structures, while for the 1:2 ratio, the formation of the extended β-sheet structure is significantly less. For a 1:3 ratio of protein/OleA, the peptide residues are sufficiently crowded by OleA molecules through hydrogen bonding, hydrophobic interactions, and π-π stacking; hence, the peptide chains prefer to exist in a monomeric random coil conformation.

RNA's Dynamic Conformational Selection and Entropic Allosteric Mechanism in Controlling Cascade Protein Binding Events

In the TAR RNA of immunodeficiency viruses, an allosteric communication exists between a distant loop and a bulge. The bulge interacts with the TAT protein vital for transactivating viral RNA, while the loop interacts with cyclin-T1, contingent on TAT binding. Through extensive atomistic and free energy simulations, we investigate TAR-TAT binding in nonpathogenic bovine immunodeficiency virus (BIV) and pathogenic human immunodeficiency virus (HIV). Thermodynamic analysis reveals enthalpically driven binding in BIV and entropically favored binding in HIV. The broader global basin in HIV is attributed to binding-induced loop fluctuation, corroborated by nuclear magnetic resonance (NMR), indicating classical entropic allostery onset. While this loop fluctuation affects the TAT binding affinity, it generates a binding-competent conformation that aids subsequent effector (cyclin-T1) binding. This study underscores how two structurally similar apo-RNA scaffolds adopt distinct conformational selection mechanisms to drive enthalpic and entropic allostery, influencing protein affinity in the signaling cascade.

Accurate and Efficient Conformer Sampling of Cyclic Drug-Like Molecules with Inverse Kinematics

Identification of all of the influential conformers of biomolecules is a crucial step in many tasks of computational biochemistry. Specifically, molecular docking, a key component of in silico drug development, requires a comprehensive set of conformations for potential candidates in order to generate the optimal ligand-receptor poses and, ultimately, find the best drug candidates. However, the presence of flexible cycles in a molecule complicates the initial search for conformers since exhaustive sampling algorithms via torsional random and systematic searches become very inefficient. The devised inverse-kinematics-based Monte Carlo with refinement (MCR) algorithm identifies independently rotatable dihedral angles in (poly)cyclic molecules and uses them to perform global conformational sampling, outperforming popular alternatives (MacroModel, CREST, and RDKit) in terms of speed and diversity of the resulting conformer ensembles. Moreover, MCR quickly and accurately recovers naturally occurring macrocycle conformations for most of the considered molecules.

The Dependence of Hydrophobic Interactions on the Shape of Solute Surface

According to our recent studies on hydrophobicity, this work is aimed at understanding the dependence of hydrophobic interactions on the shape of a solute's surface. It has been observed that dissolved solutes primarily affect the structure of interfacial water, which refers to the top layer of water at the interface between the solute and water. As solutes aggregate in a solution, hydrophobic interactions become closely related to the transition of water molecules from the interfacial region to the bulk water. It is inferred that hydrophobic interactions may depend on the shape of the solute surface. To enhance the strength of hydrophobic interactions, the solutes tend to aggregate, thereby minimizing their surface area-to-volume ratio. This also suggests that hydrophobic interactions may exhibit directional characteristics. Moreover, this phenomenon can be supported by calculated potential mean forces (PMFs) using molecular dynamics (MD) simulations, where different surfaces, such as convex, flat, or concave, are associated with a sphere. Furthermore, this concept can be extended to comprehend the molecular packing parameter, commonly utilized in studying the self-assembly behavior of amphiphilic molecules in aqueous solutions.

Adsorption of monoclonal antibody fragments at the water-oil interface: A coarse-grained molecular dynamics study

Monoclonal antibodies (mAbs) can undergo structural changes due to interaction with oil-water interfaces during storage. Such changes can lead to aggregation, resulting in a loss of therapeutic efficacy. Therefore, understanding the microscopic mechanism controlling mAb adsorption is crucial to developing strategies that can minimize the impact of interfaces on the therapeutic properties of mAbs. In this study, we used MARTINI coarse-grained molecular dynamics simulations to investigate the adsorption of the Fab and Fc domains of the monoclonal antibody COE3 at the oil-water interface. Our aim was to determine the regions on the protein surface that drive mAb adsorption. We also investigate the role of protein concentration on protein orientation and protrusion to the oil phase. While our structural analyses compare favorably with recent neutron reflectivity measurements, we observe some differences. Unlike the monolayer at the interface predicted by neutron reflectivity experiments, our simulations indicate the presence of a secondary diffused layer near the interface. We also find that under certain conditions, protein-oil interaction can lead to a considerable distortion in the protein structure, resulting in enhanced adsorption behavior.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

Molecular Dynamics Simulation of Complex Reactivity with the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) Software Package

Simulating chemically reactive phenomena such as proton transport on nanosecond to microsecond and beyond time scales is a challenging task. Ab initio methods are unable to currently access these time scales routinely, and traditional molecular dynamics methods feature fixed bonding arrangements that cannot account for changes in the system's bonding topology. The Multiscale Reactive Molecular Dynamics (MS-RMD) method, as implemented in the Rapid Approach for Proton Transport and Other Reactions (RAPTOR) software package for the LAMMPS molecular dynamics code, offers a method to routinely sample longer time scale reactive simulation data with statistical precision. RAPTOR may also be interfaced with enhanced sampling methods to drive simulations toward the analysis of reactive rare events, and a number of collective variables (CVs) have been developed to facilitate this. Key advances to this methodology, including GPU acceleration efforts and novel CVs to model water wire formation are reviewed, along with recent applications of the method which demonstrate its versatility and robustness.

Calculating Binding Free Energies in Model Host-Guest Systems with Unrestrained Advanced Sampling

Host-guest interactions are important to the design of pharmaceuticals and, more broadly, to soft materials as they can enable targeted, strong, and specific interactions between molecules. The binding process between the host and guest may be classified as a "rare event" when viewing the system at atomic scales, such as those explored in molecular dynamics simulations. To obtain equilibrium binding conformations and dissociation constants from these simulations, it is essential to resolve these rare events. Advanced sampling methods such as the adaptive biasing force (ABF) promote the occurrence of less probable configurations in a system, therefore facilitating the sampling of essential collective variables that characterize the host-guest interactions. Here, we present the application of ABF to a rod-cavitand coarse-grained model of host-guest systems to acquire the potential of mean force. We show that the employment of ABF enables the computation of the configurational and thermodynamic properties of bound and unbound states, including the free energy landscape. Moreover, we identify important dynamic bottlenecks that limit sampling and discuss how these may be addressed in more general systems.

Short-Time Infrequent Metadynamics for Improved Kinetics Inference

Infrequent Metadynamics is a popular method to obtain the rates of long time-scale processes from accelerated simulations. The inference procedure is based on rescaling the first-passage times of the Metadynamics trajectories using a bias-dependent acceleration factor. While useful in many cases, it is limited to Poisson kinetics, and a reliable estimation of the unbiased rate requires slow bias deposition and prior knowledge of efficient collective variables. Here, we propose an improved inference scheme, which is based on two key observations: (1) the time-independent rate of Poisson processes can be estimated using short trajectories only. (2) Short trajectories experience minimal bias, and their rescaled first-passage times follow the unbiased distribution even for relatively high deposition rates and suboptimal collective variables. Therefore, by basing the inference procedure on short time scales, we obtain an improved trade-off between speedup and accuracy at no additional computational cost, especially when employing suboptimal collective variables. We demonstrate the improved inference scheme for a model system and two molecular systems.