Calculating potentials of mean force from steered molecular dynamics simulations

Dynamic Mechanism of Norepinephrine Reuptake and Antidepressants Blockade Regulated by Membrane Potential

During nerve signaling, changes in membrane potential are key to regulating neuronal activity. The norepinephrine transporter (NET) plays a crucial role in the reuptake of norepinephrine (NE), which is essential for maintaining neurotransmitter homeostasis. However, the impact of membrane potential on NET function has long been understudied. Despite the great biological significance of NET, the dynamic molecular mechanisms of NE transport and the blockade effects of antidepressants on this process remain unclear. Here, we reveal the structural, electrostatic, and dynamic characteristics of the NET-NE/antidepressants systems, indicating the dynamic voltage dependence of the NET function. By analyzing the structure and electrostatic properties of the central binding pocket, we find that a hydrophobic network stabilizes the localization of NE, while the dynamic hydrogen bond and salt bridge network plays a crucial role in facilitating the inward transport of NE. Changes in membrane potential significantly affect the reuptake of NE through an electrostatically driven substrate transport pathway, primarily influencing the substrate entrance, the hydrophilic channel leading to the central site, and the exit region. The hyperpolarized state favors NE reuptake, exhibiting a marked preference for inward movement, which aligns with the physiological need for neurons to regulate neurotransmitter concentration in the synaptic cleft via reuptake. Conversely, in the depolarized state, which corresponds to the generation of nerve impulses, NE reuptake may not peak. Furthermore, antidepressants, with their larger molecular size and longer charged amino groups, initially anchor to the essential residue E382 required for NE reuptake. They subsequently occupy the same binding pathway as NE, creating spatial hindrance that effectively blocks NE binding to the central pocket. Additionally, their binding/dissociation behaviors exhibit significant voltage dependence. Under the hyperpolarized state, antidepressants can better block NE entry through more flexible electrostatic and hydrophobic interactions with NET, while the depolarized state raises the binding barrier for antidepressants, facilitating their dissociation. And with this work, a computational strategy for membrane protein-ligand is proposed to emphasize that considering the effects of electric fields in the calculations can reveal more underlying mechanisms and key interactions.

Understanding Protein Adsorption on Carbon Nanotube Inner and Outer Surfaces by Molecular Dynamics Simulations

Biomolecules, such as proteins, can form complexes with carbon nanotubes (CNTs), which have numerous applications in nanobiotechnology. Proteins can be adsorbed onto either the inner walls or outer surfaces of CNTs via van der Waals interactions; however, the differences between these two processes remain poorly understood. In this work, we performed classical all-atom molecular dynamics simulations with explicit solvents to investigate the interaction between a model protein, the Yap65 WW domain, and (22,22) CNTs and larger. The Yap65 WW domain comprises three β-sheet segments and contains three key aromatic residues: TRP17, TYR28, and TRP39. Our findings reveal distinct interaction mechanisms for the inner and outer surfaces of large CNTs. The protein's interaction with the inner surface is governed by the interplay between surface curvature and adsorption orientation. In the confined space of the CNT channel, variations in tube curvature and adsorption orientation give rise to specific binding modes, resulting in varying degrees of protein conformational change. In contrast, on the outer surface of large CNTs, where space is less restricted, the adsorption orientation plays a more dominant role. Specifically, the orientation in which more aromatic residues directly interact with the surface suffer from the greater structural loss, regardless of the tube curvature. Finally, protein-CNT binding free energies were calculated using the Poisson-Boltzmann surface area (MM-PBSA) method and steered molecular dynamics simulations based on Jarzynski equality, demonstrating that protein desorption from CNTs is highly dependent on binding configurations. This study reveals the influence of confined space on protein adsorption and the critical role of CNT curvature in modulating β-sheet stability.

Computational Insights into Membrane Disruption by Cell-Penetrating Peptides

Cell-penetrating peptides (CPPs) can translocate into cells without inducing cytotoxicity. The internalization process implies several steps at different time scales ranging from microseconds to minutes. We combine adaptive Steered Molecular Dynamics (aSMD) with conventional Molecular Dynamics (cMD) to observe nonequilibrium and equilibrium states to study the early mechanisms of peptide-bilayer interaction leading to CPPs internalization. We define three membrane compositions representing bilayer sections, neutral lipids (i.e., upper leaflet), neutral lipids with cholesterol (i.e., hydrophobic core), and neutral/negatively charged lipids with cholesterol (i.e., lower leaflet) to study the energy barriers and disruption mechanisms of Arg9, MAP, and TP2, representing cationic, amphiphilic, and hydrophobic CPPs, respectively. Cholesterol and negatively charged lipids increase the energetic barriers for the peptide-bilayer crossing. TP2 interacts with the bilayer by hydrophobic insertion, while Arg9 disrupts the bilayer by forming transient or stable pores. MAP has shown both behaviors. Collectively, these findings underscore the significance of innovative computational approaches in studying membrane-disruptive peptides and, more specifically, in harnessing their potential for cell penetration.

Permeability Benchmarking: Guidelines for Comparing in Silico, in Vitro, and in Vivo Measurements

Permeability is a measure of the degree to which cells can transport molecules across biological barriers. Units of permeability are distance per unit time (typically cm/s), where accurate measurements are needed to define drug delivery in homeostasis and to model dysfunction occurring during disease. This perspective offers a set of community-led guidelines to benchmark permeability data across multidisciplinary approaches and different biological contexts. First, we lay out the analytical framework for three methodologies to calculate permeability: in silico assays using either transition-based counting or the inhomogeneous-solubility diffusion approaches, in vitro permeability assays using cells cultured in 2D or 3D geometries, and in vivo assays utilizing in situ brain perfusion or multiple time-point regression analysis. Then, we demonstrate a systematic benchmarking of in silico to both in vitro and in vivo, depicting the ways in which each benchmarking is sensitive to the choices of assay design. Finally, we outline seven recommendations for best practices in permeability benchmarking and underscore the significance of tailored permeability assays in driving advancements in drug delivery research and development. Our exploration encompasses a discussion of "generic" and tissue-specific biological barriers, including the blood-brain barrier (BBB), which is a major hurdle for the delivery of therapeutic agents into the brain. By addressing challenges in reconciling simulated data with experimental assays, we aim to provide insights essential for optimizing accuracy and reliability in permeability modeling.

Molecular Insights into the Adsorption of Deposit Control Additives from Hydrocarbon Fuels

Engine deposits can reduce performance and increase emissions, particularly for modern direct-injection fuel delivery systems. Surfactants known as deposit control additives (DCAs) adsorb and self-assemble on the surface of deposit precursors to keep them suspended in the fuel. Here, we show how molecular simulations can be used to virtually screen the ability of surfactants to bind to polyaromatic hydrocarbons, comprising a major class of carbonaceous deposits. We use molecular dynamics with the adaptive biasing force method to generate the potential of mean force as a function of the vertical distance between the surfactants and deposits in gasoline and diesel fuel surrogates. We find that a zwitterionic surfactant outperforms a conventional polyisobutylene succinimide for binding to these aromatic species. The amine groups in the succinimide headgroup only weakly adsorb on the polyaromatic deposit, while additional functional groups in the zwitterionic surfactant, particularly the quarternary ammonium ion, markedly enhance the binding strength. We decompose the adsorption free energies of the surfactants into their entropic and enthalpic components, to find that the latter dominates the attraction from these non-aqueous solvents. The adsorption free energy of both surfactants is slightly weaker from n-hexadecane (diesel) than iso-octane (gasoline), which is due to the larger steric barrier from stronger molecular layering of the former on the deposit. Density functional theory calculations of the adsorption of DCA fragments validate the force field used in the molecular dynamics simulations and provide further insights into the nature of the intermolecular interactions. The approach introduced here shows considerable promise for accelerating the discovery of novel DCAs to facilitate more advanced fuel formulations to reduce emissions.

Hydration and Antibiofouling Behavior of Zwitterionic Polycarboxybetaine-Grafted Surfaces Studied with Atomistic Simulations

Fouling-resistant coating materials have important applications in marine industry and biomedicine. Zwitterionic carboxybetaine polymers have demonstrated robust antibiofouling functionalities in experiments. In this work, we performed atomistic molecular dynamics simulations to study the molecular mechanism of hydration and antibiofouling of poly(carboxybetaine acrylamide) (polyCBAA) brush surfaces. We focused on the zwitterionic carboxybetaine, which has only a short methylene spacer between the positive quaternary ammonium and the negative carboxylate groups. Our study shows that a large amount of water is present within the polyCBAA surface, and a condensed water layer of single-molecular thickness covers the top of the polymer surface. Moreover, the clustering of the zwitterionic chains results in an amorphous structure of the polymer surface, a reduced degree of order in the interfacial water molecules, and weak protein attachment. The low protein desorption free energy demonstrates that the polyCBAA surface exhibits strong fouling resistance due to its significant interfacial hydration and the small dipole moment of the carboxybetaine group, minimizing protein-surface electrostatic interactions. Our study at the molecular level will be important to the future development of zwitterionic materials.

Modified Jarzynski equality in a microcanonical ensemble

We show that the conventional Jarzynski equality does not hold for a system prepared in a microcanonical ensemble. We derive a modified equality that connects microcanonical work fluctuations to entropy production, in an analogous way to the Jarzynski equality, but with reference to an inverse temperature that depends on the path of the work protocol. For close to isothermal processes the modified equality can improve on the bound 〈W〉≥ΔF. Our result is a special case of a general expression for the microcanonical moment-generating function for any extensive quantity, which enables calculation of the breakdown of ensemble equivalence for thermodynamic fluctuations. We demonstrate our microcanonical Jarzynski equality in a system of driven two-level spins.

Role of sequence length and functionalization in interactions of bioconjugated peptides with mitomembranes

Cell-penetrating peptides are efficient tools for intracellular delivery of a variety of cargoes. In this study, we explored the effect of chain length, side chain chemistry, and the locations of conjugated molecules on the interaction between iron-chelating peptides and a mitochondrial-mimicking membrane. We report that a longer chain length enhanced peptide/membrane interactions, and conjugation at the N-terminus lowered the free-energy barrier for peptide translocation across the membrane. Peptides containing Phe side chains and those containing modified Phe (cyclohexane) side chains showed comparable peptide/membrane energetics and translocation energy barriers. Using steered molecular dynamics (SMD) simulations, we further probed the mechanistic details of translocation of each N-terminated peptide across the membrane and compared their metastable states. At a higher steering velocity, the peptide adopted a compact structure due to frequent π-π interactions among conjugated molecules, but at lower steering velocities, each N-terminated peptide adopted an extended structure. This structure allowed cationic residues to maximize their interactions with phosphate headgroups in the mitomembrane. The hydrophobic residues also formed interactions with the lipid acyl tails, facilitating the passage of peptides across the membrane with decreased free energy barriers. Our results highlight the significance of peptide chain length and conjugation in facilitating peptide transport across the membrane.

Use of Steered Molecular Dynamics to Explore the Conformational Changes of SNARE Proteins

Steered Molecular Dynamics (SMD) simulation is a powerful computational simulation technique that enables the controlled manipulation of molecular systems by applying external forces. This method is frequently utilized to investigate the slow processes of biomolecular systems that occur within sub-second to second time scales, achieved through SMD simulations that only span nanoseconds. SMD simulation can be utilized to study the detailed mechanism of protein conformational changes, protein unfolding, and ligand dissociation, etc. Here, we introduce the application of SMD in the study of intra-molecular interactions and regulatory mechanisms in SNAREs by analyzing their conformational change pathways.

Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution

Methods are needed to mitigate microplastic (MP) pollution to minimize their harm to the environment and human health. Given the ability of polypeptides to adsorb strongly to materials of micro- or nanometer size, plastic-binding peptides (PBPs) could help create bio-based tools for detecting, filtering, or degrading MNP pollution. However, the development of such tools is prevented by the lack of PBPs. In this work, we discover and evaluate PBPs for several common plastics by combining biophysical modeling, molecular dynamics (MD), quantum computing, and reinforcement learning. We frame peptide affinity for a given plastic through a Potts model that is a function of the amino acid sequence and then search for the amino acid sequences with the greatest predicted affinity using quantum annealing. We also use proximal policy optimization to find PBPs with a broader range of physicochemical properties, such as isoelectric point or solubility. Evaluation of the discovered PBPs in MD simulations demonstrates that the peptides have high affinity for two of the plastics: polyethylene and polypropylene. We conclude by describing how our computational approach could be paired with experimental approaches to create a nexus for designing and optimizing peptide-based tools that aid the detection, capture, or biodegradation of MPs. We thus hope that this study will aid in the fight against MP pollution.

Integrated computational biophysics approach for drug discovery against Nipah virus

The Nipah virus (NiV) poses a pressing global threat to public health due to its high mortality rate, multiple modes of transmission, and lack of effective treatments. NiV glycoprotein G (NiV-G) emerges as a promising target for the discovery of NiV drugs because of its essential role in viral entry and membrane fusion. Therefore, in this study, we applied an integrated computational and biophysics approach to identify potential inhibitors of NiV-G within a curated dataset of Peruvian phytochemicals. The virtual screening results indicated that these compounds could represent a natural source of potential NiV-G inhibitors with ΔG values ranging from -8 to -11 kcal/mol. Among them, procyanidin B2, B3, B7, and C1 exhibited the highest binding affinities and formed the most molecular interactions with NiV-G. Molecular dynamics simulations revealed the induced-fit mechanism of NiV-G pocket interaction with these procyanidins, primarily driven by its hydrophobic nature. Non-equilibrium free energy calculations were used to determine binding affinities, highlighting Procyanidin B3 and B2 as the ligands with the most substantial interactions. In general, this work underscores the potential of Peruvian phytochemicals, particularly procyanidins B2, B3, B7, and C1, as lead compounds for developing anti-NiV drugs through an integrated computational biophysics approach.

Comediation of voltage gating and ion charge in MXene membrane for controllable and selective monovalent cation separation

Artificial ion channels with controllable mono/monovalent cation separation fulfill important roles in biomedicine, ion separation, and energy conversion. However, it remains a daunting challenge to develop an artificial ion channel similar to biological ion channels due to ion-ion competitive transport and lack of ion-gating ability of channels. Here, we report a conductive MXene membrane with polydopamine-confined angstrom-scale channels and propose a voltage gating and ion charge comediation strategy to concurrently achieve gated and selective mono/monovalent cation separation. The membrane shows a highly switchable "on-off" ratio of ∼9.9 for K+ transport and an excellent K+/Li+ selectivity of 40.9, outperforming the ion selectivity of reported membranes with electrical gating (typically 1.5 to 6). Theoretical simulations reveal that the introduced high-charge cations such as Mg2+ enable the preferential distribution of target K+ over competing Li+ at the channel entrance, and the surface potential reduces the ionic transport energy barrier for allowing K+ to pass quickly through the channel.

PME Switching in Confinement Simulations of Charged Solutes

The confinement method is a reaction coordinate-free enhanced sampling method for the calculation of conformational free energy differences. We show that in explicit solvent, artifacts occur when treating charged solutes. These artifacts are resolved by switching off the particle mesh Ewald (PME) method at the start of confinement. Calculations of the free energy cost of this switching converge rapidly, with small statistical error. The effectiveness and accuracy of confinement with PME switching is demonstrated by its application to a series of solutes of different charge; its ability to treat complex systems is illustrated by evaluating the free energy difference between B and Z-DNA.

Elucidating Protein Structures in the Gas Phase: Traversing Configuration Space with Biasing Methods

Achieving accurate characterization of protein structures in the gas phase continues to be a formidable challenge. To tackle this issue, the present study employs Molecular Dynamics (MD) simulations in tandem with enhanced sampling techniques (methods designed to efficiently explore protein conformations). The objective is to identify suitable structures of proteins by contrasting their calculated Collision Cross-Section (CCS) with those observed experimentally. Significant discrepancies were observed between the initial MD-simulated and experimentally measured CCS values through Ion Mobility-Mass Spectrometry (IMS-MS). To bridge this gap, we employed two distinct enhanced sampling methods, Harmonic Biasing Potential and Adaptive Biasing Force, which help the proteins overcome energy barriers to adopt more compact configurations. These techniques leverage the radius of gyration as a reaction coordinate (guiding parameter), guiding the system toward compressed states that potentially match experimental configurations more closely. The guiding forces are only employed to overcome existing barriers and are removed to allow the protein to naturally arrive at a potential gas phase configuration. The results demonstrated close alignment (within ∼4%) between simulated and experimental CCS values despite using different strengths and/or methods, validating their efficacy. This work lays the groundwork for future studies aimed at optimizing biasing methods and expanding the collective variables used for more accurate gas-phase structural predictions.

Molecular Dynamics Simulation Study on the Structural and Thermodynamic Analysis of Oxidized and Unoxidized Forms of Polyaniline

The conducting polymer polyaniline (PANI) has shown significant interest for the development of electrified membranes (EMs) with superior antifouling characteristics. However, the blending and doping of PANI with other polymers and nanomaterials highly influence the properties of the membrane surface. PANI exists in two forms: oxidized, known as emeraldine salt (ES), and unoxidized, referred to as emeraldine base (EB). Therefore, understanding the different forms of PANI and the variations between the oxidized and unoxidized forms along the length of the polymer chain is intriguing. In this paper, we present the design of a novel copolymer consisting of EB and ES monomers with varying charge densities and different segmental arrangements. We present various intra- and intermolecular structural properties of the PANI chains using all-atom molecular dynamics (MD) simulations. Herein, we present a detailed conformational free energy analysis to understand the conformational transitions of the PANI chains. Our results show increased radius of gyration (Rg) values with increased charge density. Furthermore, we also present the H-bonding, free energy analysis, reduced density gradient (RDG), and solvent-accessible surface area (SASA) values for the observed conformational transitions of PANI. Therefore, these observations are crucial in understanding the complex behavior of chains for designing target-specific polymeric materials.

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

On the Control of Directionality of Myosin

The origin of the unique directionality of myosin has been a problem of fundamental and practical importance. This work establishes in a conclusive way that the directionality is controlled by tuning the barrier for the rate-determining step, namely, the ADP release step. This conclusion is based on exploring the molecular origin behind the reverse directionality of myosins V and VI and the determination of the origin of the change in the barriers of the ADP release for the forward and backward motions. Our investigation is performed by combining different simulation methods such as steer molecular dynamics (SMD), umbrella sampling, renormalization method, and automated path searching method. It is found that in the case of myosin V, the ADP release from the postrigor (trailing head) state overcomes a lower barrier than the prepowerstroke (leading head) state, which is also evident from experimental observation. In the case of myosin VI, we noticed a different trend when compared to myosin V. Since the directionality of myosins V and VI follows a reverse trend, we conclude that such differences in the directionality are controlled by the free energy barrier for the ADP release. Overall, the proof that the directionality of myosin is determined by the activation barrier of the rate-determining step in the cycle, rather than by some unspecified dynamical effects, has general importance.

Computational assessment of lipid facilitated membrane permeation of vancomycin using force-probe molecular dynamic simulation

In this study the efficacy of different edible lipids for drug permeation enhancement of vancomycin through biological membrane was investigated using molecular dynamic simulation. In this regard, at first the ability of the lipids for complex formation with the drug was evaluated for number of most common edible lipids including tripalmitin (TPA), trimyristin (TMY), labrafil (LAB), glycerol monostearate (GMS), glycerol monooleate (GMO), Distearoylphosphorylethanolamine (DSPE), dipalmitoylphosphatidylethanolamine (DPPE), Dipalmitoylphosphatidylcholine (DPPC), cholesterol (CL), stearic acid (SA), palmitic acid (PA) and oleic acid (OA). Then the complexes were pulled thorough a bilayer membrane while the changes in force were probed. The results showed that besides the SA, PA and OA the other examined lipids were able to perform a perfect molecular complex with the drug. Also the results of pulling simulation revealed that the least of force was needed for drug transmittance through the membrane when it was covered by LAB, TMY and DSPE. These results indicated that these lipids can be the excellent materials of choice as permeation enhancer for preparing a proper oral formulation of vancomycin.Communicated by Ramaswamy H. Sarma.

Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives

More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

Distinct Characteristic Binding Modes of Benzofuran Core Inhibitors to Diverse Genotypes of Hepatitis C Virus NS5B Polymerase: A Molecular Simulation Study

The benzofuran core inhibitors HCV-796, BMS-929075, MK-8876, compound 2, and compound 9B exhibit good pan-genotypic activity against various genotypes of NS5B polymerase. To elucidate their mechanism of action, multiple molecular simulation methods were used to investigate the complex systems of these inhibitors binding to GT1a, 1b, 2a, and 2b NS5B polymerases. The calculation results indicated that these five inhibitors can not only interact with the residues in the palm II subdomain of NS5B polymerase, but also with the residues in the palm I subdomain or the palm I/III overlap region. Interestingly, the binding of inhibitors with longer substituents at the C5 position (BMS-929075, MK-8876, compound 2, and compound 9B) to the GT1a and 2b NS5B polymerases exhibits different binding patterns compared to the binding to the GT1b and 2a NS5B polymerases. The interactions between the para-fluorophenyl groups at the C2 positions of the inhibitors and the residues at the binding pockets, together with the interactions between the substituents at the C5 positions and the residues at the reverse β-fold (residues 441-456), play a key role in recognition and the induction of the binding. The relevant studies could provide valuable information for further research and development of novel anti-HCV benzofuran core pan-genotypic inhibitors.

Tribo-Chemical Reactions in DLC-Zirconia Sliding under Ethanol Gas Environment

A reactive force field (ReaxFF) molecular dynamics simulation is performed for the sliding of diamond-like carbon (DLC) and yttria-stabilized zirconia (YSZ) under an ethanol gas environment, motivated by the previous experiment of ultralow friction phenomenon (friction fade-out). We observe (i) dissociation of ethanol into ethoxy and hydrogen, both of which simultaneously adsorb on the YSZ surface, and (ii) dissociation of ethanol into ethyl and hydroxy, the former of which forms a bond with another ethanol molecule and the latter of which adsorbs on the DLC surface. Reaction (i) is enhanced by the sliding motion, but occurs even without it, while reaction (ii) only occurs during sliding with a sufficiently high load pressure. The potentials of mean force for the two reactions are also calculated combining the steered MD and Jarzynski equality. It is shown that the activation energies of reactions (i) and (ii) are significantly lowered by the YSZ and DLC surfaces, respectively, as compared to those in a vacuum. The resultant activation energy is higher for reaction (ii) than for reaction (i).

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Investigating Shear Stress of Ice Accumulated on Surfaces with Various Roughnesses: Effects of a Quasi-Water Layer

The investigation of the anti-icing/deicing is essential because the icing phenomenon deteriorates the natural environment and various projects. By conducting molecular dynamics simulation, this work analyzes the effect of the quasi-water layer on the ice shear stress over smooth and rough surfaces, along with the underlying physics of the quasi-water layer. The results indicate that the thickness of the quasi-water layer monotonically increases with temperature, resulting in a monotonic decrease in the ice shear stress on the smooth surface. Due to the joint effects of the smooth surface wettability and the quasi-water layer, the ice shear stress increases and then decreases to almost a constant value when the surface changes from a hydrophobic to a hydrophilic one. For rough surfaces with stripe nanostructures, when the width of the bump for one case equals the depression for the other case, the variations of shear stress with height for these two cases are almost the same. The rough surface is effective in reducing the ice shear stress compared to the smooth surface due to the thickening of the quasi-water layer. Each molecule in the quasi-water layer and its four nearest neighboring molecules gradually form a tetrahedral ice-like structure along the direction away from the surface. The radial distribution function also shows that the quasi-water layer resembles the liquid water rather than the ice structure. These findings shed light on developing anti-icing and deicing techniques.

Investigation of the Driving Force of Replacing Adsorbed Hydrocarbons by CO2 in Organic Matter from an Energy Perspective

Carbon dioxide (CO2) has been widely used to enhance the recovery of adsorbed hydrocarbons from the organic matter (OM) in shale formations. To reveal the driving force of replacing adsorbed hydrocarbons from OM by CO2, we performed molecular dynamics (MD) simulations of the replacement process and calculated the interaction forces between CO2 and hydrocarbons. In addition, based on the umbrella sampling method, steered MD simulations were performed, and the free energy profiles of hydrocarbons were obtained using the weighted histogram analysis method. Results show that the condition of the hydrocarbon replacement by CO2 requires the hydrocarbon to have sufficient kinetic energy or to have a sufficiently large attractive force exerted to ensure that the hydrocarbon escapes the potential well of the OM. The attractive forces exerted on hydrocarbon molecules by CO2 can significantly decrease the energy barrier associated with hydrocarbon movement away from the OM surface. Furthermore, both CO2 and supercritical CO2 can effectively displace adsorbed hydrocarbon gas (methane) on the OM, while supercritical CO2 is required to enhance the recovery of adsorbed hydrocarbon oil (n-dodecane). The results obtained in this study provide guidance for enhancing the recovery of adsorbed hydrocarbons by CO2 in shale formations.

Rare Events Sampling Methods for Quantum and Classical Ab Initio Molecular Dynamics

We provide an approach to sample rare events during classical ab initio molecular dynamics and quantum wavepacket dynamics. For classical AIMD, a set of fictitious degrees of freedom are introduced that may harmonically interact with the electronic and nuclear degrees of freedom to steer the dynamics in a conservative fashion toward energetically forbidden regions. A similar approach when introduced for quantum wavepacket dynamics has the effect of biasing the trajectory of the wavepacket centroid toward the regions of the potential surface that are difficult to sample. The approach is demonstrated for a phenol-amine system, which is a prototypical problem for condensed phase-proton transfer, and for model potentials undergoing wavepacket dynamics. In all cases, the approach yields trajectories that conserve energy while sampling rare events.

Learning Protein-Ligand Unbinding Pathways via Single-Parameter Community Detection

Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the comprehension of paths such systems take between metastable states. In MD simulations, paths are usually not observable per se, but they need to be inferred from simulation trajectories. Here, we present a novel approach to cluster trajectories based on a community detection algorithm that necessitates only the definition of a single parameter. The unbinding of the streptavidin-biotin complex is used as a benchmark system and the A2a adenosine receptor in complex with the inhibitor ZM241385 as an elaborate application. We demonstrate how such clusters of trajectories correspond to pathways and how the approach helps in the identification of reaction coordinates for a considered (un)binding process.

Molecular behavior of silicone adhesive at buried polymer interface studied by molecular dynamics simulation and sum frequency generation vibrational spectroscopy

Silicones have excellent material properties and are used extensively in many applications, ranging from adhesives and lubricants to electrical insulation. To ensure strong adhesion of silicone adhesives to a wide variety of substrates, silane-based adhesion promotors are typically blended into the silicone adhesive formulation. However, little is known at the molecular level about the true silane adhesion promotion mechanism, which limits the ability to develop even more effective adhesion promoters. To understand the adhesion promotion mechanism of silane molecules at the molecular level, this study has used sum frequency generation vibrational spectroscopy (SFG) to determine the behavior of (3-glycidoxypropyl)trimethoxy silane (γ-GPS) at the buried interface between poly(ethylene terephthalate) (PET) and a bulk silicone adhesive. To complement and extend the SFG results, atomistic molecular dynamics (MD) simulations were applied to investigate molecular behavior and interfacial interaction of γ-GPS at the silicone/PET interface. Free energy computations were used to study the γ-GPS interaction in the sample system and determine the γ-GPS interfacial segregation mechanism. Both experiments and simulations consistently show that γ-GPS molecules prefer to segregate at the interface between PET and PDMS. The methoxy groups on γ-GPS molecules orient toward the PDMS polymer phase. The consistent picture of interfacial structure emerging from both simulation and experiment provides enhanced insight on how γ-GPS behaves in the silicone - PET system and illustrates why γ-GPS could improve the adhesion of silicone adhesive, leading to further understanding of silicone adhesion mechanisms useful in the design of silicone adhesives with improved performance.

Estimating Position-Dependent and Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories: Existing Methods and Future Outlook

Confinement can substantially alter the physicochemical properties of materials by breaking translational isotropy and rendering all physical properties position-dependent. Molecular dynamics (MD) simulations have proven instrumental in characterizing such spatial heterogeneities and probing the impact of confinement on materials' properties. For static properties, this is a straightforward task and can be achieved via simple spatial binning. Such an approach, however, cannot be readily applied to transport coefficients due to lack of natural extensions of autocorrelations used for their calculation in the bulk. The prime example of this challenge is diffusivity, which, in the bulk, can be readily estimated from the particles' mobility statistics, which satisfy the Fokker-Planck equation. Under confinement, however, such statistics will follow the Smoluchowski equation, which lacks a closed-form analytical solution. This brief review explores the rich history of estimating profiles of the diffusivity tensor from MD simulations and discusses various approximate methods and algorithms developed for this purpose. Besides discussing heuristic extensions of bulk methods, we overview more rigorous algorithms, including kernel-based methods, Bayesian approaches, and operator discretization techniques. Additionally, we outline methods based on applying biasing potentials or imposing constraints on tracer particles. Finally, we discuss approaches that estimate diffusivity from mean first passage time or committor probability profiles, a conceptual framework originally developed in the context of collective variable spaces describing rare events in computational chemistry and biology. In summary, this paper offers a concise survey of diverse approaches for estimating diffusivity from MD trajectories, highlighting challenges and opportunities in this area.

Lambda-ABF: Simplified, Portable, Accurate, and Cost-Effective Alchemical Free-Energy Computation

We introduce the lambda-Adaptive Biasing Force (lambda-ABF) method for the computation of alchemical free-energy differences. We propose a software implementation and showcase it on biomolecular systems. The method arises from coupling multiple-walker adaptive biasing force with λ-dynamics. The sampling of the alchemical variable is continuous and converges toward a uniform distribution, making manual optimization of the λ schedule unnecessary. Contrary to most other approaches, alchemical free-energy estimates are obtained immediately without any postprocessing. Free diffusion of λ improves orthogonal relaxation compared to fixed-λ thermodynamic integration or free-energy perturbation. Furthermore, multiple walkers provide generic orthogonal space coverage with minimal user input and negligible computational overhead. We show that our high-performance implementations coupling the Colvars library with NAMD and Tinker-HP can address real-world cases including ligand-receptor binding with both fixed-charge and polarizable models, with a demonstrably richer sampling than fixed-λ methods. The implementation is fully open-source, publicly available, and readily usable by practitioners of current alchemical methods. Thanks to the portable Colvars library, lambda-ABF presents a unified user interface regardless of the back-end (NAMD, Tinker-HP, or any software to be interfaced in the future), sparing users the effort of learning multiple interfaces. Finally, the Colvars Dashboard extension of the visual molecular dynamics (VMD) software provides an interactive monitoring and diagnostic tool for lambda-ABF simulations.

Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction

Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may not be the optimal choice for elucidating transition mechanisms within low-dimensional representations, especially for intricate biosystems. Unraveling the association mechanism in such complex systems always necessitates permutations of several essential independent components or collective variables, a process that is inherently obscure and may require empirical knowledge for selection. To address these challenges, we have implemented an integrated unsupervised dimension reduction model: uniform manifold approximation and projection (UMAP) with hierarchy density-based spatial clustering of applications with noise (HDBSCAN). This approach effectively generates low-dimensional configurational embeddings. The hierarchical application of this architecture, in conjunction with MSM, reveals global kinetic connectivity while identifying local conformational states. Consequently, our methodology establishes a multiscale mechanistic elucidation framework. Leveraging the benefits of the uniform sample distribution and a denoising approach, our model demonstrates robustness in preserving global and local data structures compared to traditional dimension reduction methods in the field of MD analysis area. The interpretability of hyperparameter selection and compatibility with downstream tasks are cross-validated across various simulation data sets, utilizing both computational evaluation metrics and experimental kinetic observables. Furthermore, the predicted Mcl1-BH3 association kinetics (0.76 s-1) is in close agreement with surface plasmon resonance experiments (0.12 s-1), affirming the plausibility of the identified pathway composed of representative conformations. We anticipate that the devised workflow will serve as a foundational framework for studying recognition patterns in complex biological systems. Its contributions extend to the exploration of protein functional dynamics and rational drug design, offering a potent avenue for advancing research in these domains.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Molecular choreography: Unveiling the dynamic landscape of type IIA DNA topoisomerases before T-segment passage through all-atom simulations

Type IIA DNA topoisomerases are molecular nanomachines responsible for controlling topological states of DNA molecules. Here, we explore the dynamic landscape of yeast topoisomerase IIA during key stages of its catalytic cycle, focusing in particular on the events preceding the passage of the T-segment. To this end, we generated six configurations of fully catalytic yeast topo IIA, strategically inserted a T-segment into the N-gate in relevant configurations, and performed all-atom simulations. The essential motion of topo IIA protein dimer was characterized by rotational gyrating-like movement together with sliding motion within the DNA-gate. Both appear to be inherent properties of the enzyme and an inbuilt feature that allows passage of the T-segment through the cleaved G-segment. Coupled dynamics of the N-gate and DNA-gate residues may be particularly important for controlled and smooth passage of the T-segment and consequently the prevention of DNA double-strand breaks. QTK loop residue Lys367, which interacts with ATP and ADP molecules, is involved in regulating the size and stability of the N-gate. The unveiled features of the simulated configurations provide insights into the catalytic cycle of type IIA topoisomerases and elucidate the molecular choreography governing their ability to modulate the topological states of DNA topology.

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.

SARS-CoV-2 omicron RBD forms a weaker binding affinity to hACE2 compared to Delta RBD in in-silico studies

The COVID-19 pandemic sparked an unprecedented race in biotechnology in a search for effective therapies and a preventive vaccine. The continued appearance of SARS-CoV-2 variants of concern (VoCs) further swept the world. The entry of SARS-CoV-2 into cells is mediated by binding the receptor-binding domain (RBD) of the S protein to the cell-surface receptor, human angiotensin-converting enzyme 2 (hACE2). In this study, using a coarse-grained force field to parameterize the system, we employed steered-molecular dynamics (SMD) simulations to reveal the binding of SARS-CoV-2 Delta/Omicron RBD to hACE2. Our benchmarked results demonstrate a good correlation between computed rupture force and experimental binding free energy for known protein-protein systems. Moreover, our findings show that the Omicron RBD has a weaker binding affinity to hACE2, consistent with the respective experimental results. This indicates that our method can effectively be applied to other emerging SARS-CoV-2 strains.Communicated by Ramaswamy H. Sarma.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Transient crosslinking controls the condensate formation pathway within chromatin networks

The network structure of densely packed chromatin within the nucleus of eukaryotic cells acts in concert with nonequilibrium processes. Using statistical physics simulations, we explore the control provided by transient crosslinking of the chromatin network by structural-maintenance-of-chromosome (SMC) proteins over (i) the physical properties of the chromatin network and (ii) condensate formation of embedded molecular species. We find that the density and lifetime of transient SMC crosslinks regulate structural relaxation modes and tune the sol-vs-gel state of the chromatin network, which imparts control over the kinetic pathway to condensate formation. Specifically, lower density, shorter-lived crosslinks induce sollike networks and a droplet-fusion pathway, whereas higher density, longer-lived crosslinks induce gellike networks and an Ostwald-ripening pathway.

Design of AsLOV2 domain as a carrier of light-induced dissociable FMN photosensitizer

Flavin mononucleotide (FMN) is a highly efficient photosensitizer (PS) yielding singlet oxygen (1 O2 ). However, its 1 O2 production efficiency significantly decreases upon isoalloxazine ring encapsulation into the protein matrix in genetically encoded photosensitizers (GEPS). Reducing isoalloxazine ring interactions with surrounding amino acids by protein engineering may increase 1 O2 production efficiency GEPS, but at the same time weakened native FMN-protein interactions may cause undesirable FMN dissociation. Here, in contrast, we intentionally induce the FMN release by light-triggered sulfur oxidation of strategically placed cysteines (oxidation-prone amino acids) in the isoalloxazine-binding site due to significantly increased volume of the cysteinyl side residue(s). As a proof of concept, in three variants of the LOV2 domain of Avena sativa (AsLOV2), namely V416C, T418C, and V416C/T418C, the effective 1 O2 production strongly correlated with the efficiency of irradiation-induced FMN dissociation (wild type (WT) < V416C < T418C < V416C/T418C). This alternative approach enables us: (i) to overcome the low 1 O2 production efficiency of flavin-based GEPSs without affecting native isoalloxazine ring-protein interactions and (ii) to utilize AsLOV2, due to its inherent binding propensity to FMN, as a PS vehicle, which is released at a target by light irradiation.

Interactions and Transport of a Bioconjugated Peptide Targeting the Mitomembrane

The Szeto-Schiller (SS) peptides are a subclass of cell-penetrating peptides that can specifically target mitochondria and mediate conditions caused by mitochondrial dysfunction. In this work, we constructed an iron-chelating SS peptide and studied its interaction with a mitochondrial-mimicking membrane using atomistic molecular dynamics (MD) simulations. We report that the peptide/membrane interaction is thermodynamically favorable, and the localization of the peptide to the membrane is driven by electrostatic interactions between the cationic residues and the anionic phospholipid headgroups. The insertion of the peptide into the membrane is driven by hydrophobic interactions between the aromatic side chains in the peptide and the lipid acyl tails. We also probed the translocation of the peptide across the membrane by applying nonequilibrium steered MD simulations and resolved the translocation pathway, free energy profile, and metastable states. We explored four distinct orientations of the peptide along the translocation pathway and found that one orientation was energetically more favorable than the other orientations. We tested a significantly slower pulling velocity on the most thermodynamically favorable system and compared metastable states during peptide translocation. We found that the peptide can optimize hydrophobic interactions with the membrane by having aromatic side chains interacting with the lipid acyl tails instead of forming π-π interactions with each other. The mechanistic insights emerging from our work will potentially facilitate improved peptide design with enhanced activity.

Crystallographic and Computational Insights into Isoform-Selective Dynamics in Nitric Oxide Synthase

In our efforts to develop inhibitors selective for neuronal nitric oxide synthase (nNOS) over endothelial nitric oxide synthase (eNOS), we found that nNOS can undergo conformational changes in response to inhibitor binding that does not readily occur in eNOS. One change involves movement of a conserved tyrosine, which hydrogen bonds to one of the heme propionates, but in the presence of an inhibitor, changes conformation, enabling part of the inhibitor to hydrogen bond with the heme propionate. This movement does not occur as readily in eNOS and may account for the reason why these inhibitors bind more tightly to nNOS. A second structural change occurs upon the binding of a second inhibitor molecule to nNOS, displacing the pterin cofactor. Binding of this second site inhibitor requires structural changes at the dimer interface, which also occurs more readily in nNOS than in eNOS. Here, we used a combination of crystallography, mutagenesis, and computational methods to better understand the structural basis for these differences in NOS inhibitor binding. Computational results show that a conserved tyrosine near the primary inhibitor binding site is anchored more tightly in eNOS than in nNOS, allowing for less flexibility of this residue. We also find that the inefficiency of eNOS to bind a second inhibitor molecule is likely due to the tighter dimer interface in eNOS compared with nNOS. This study provides a better understanding of how subtle structural differences in NOS isoforms can result in substantial dynamic differences that can be exploited in the development of isoform-selective inhibitors.

Tertiary Plasticity Drives the Efficiency of Enterocin 7B Interactions with Lipid Membranes

The ability of antimicrobial peptides to efficiently kill their bacterial targets depends on the efficiency of their binding to the microbial membrane. In the case of enterocins, there is a three-part interaction: initial binding, unpacking of helices on the membrane surface, and permeation of the lipid bilayer. Helical unpacking is driven by disruption of the peptide hydrophobic core when in contact with membranes. Enterocin 7B is a leaderless enterocin antimicrobial peptide produced from Enterococcus faecalis that functions alone, or with its cognate partner enterocin 7A, to efficiently kill a wide variety of Gram-stain positive bacteria. To better characterize the role that tertiary structural plasticity plays in the ability of enterocin 7B to interact with the membranes, a series of arginine single-site mutants were constructed that destabilize the hydrophobic core to varying degrees. A series of experimental measures of structure, stability, and function, including CD spectra, far UV CD melting profiles, minimal inhibitory concentrations analysis, and release kinetics of calcein, show that decreased stabilization of the hydrophobic core is correlated with increased efficiency of a peptide to permeate membranes and in killing bacteria. Finally, using the computational technique of adaptive steered molecular dynamics, we found that the atomistic/energetic landscape of peptide mechanical unfolding leads to free energy differences between the wild type and its mutants, whose trends correlate well with our experiment.

Computational Simulations of Adsorption Behavior of Anionic Surfactants at the Portlandite-Water Interface under Sulfate and Calcium Ions

The adsorption behaviors of two kinds of anionic surfactants (called HSO4 and HPO4, respectively) with different negatively charged hydrophilic head groups (sulfate and phosphate groups) under different concentrations of sulfate and calcium ions at the portlandite-water interface are investigated by molecular dynamics simulations. Although the adsorption strength of HPO4 is much greater than that of HSO4, the desorption energy of HSO4 is slightly greater at an early stage of desorption due to a more perpendicular orientation and denser packing of hydrophobic tail chains. After adding ions, the sulfate ion has a significant weakening effect due to competitive adsorption, and the negative influence of the calcium ion is weaker, and it even slightly promotes the adsorption at low concentration. Due to the stronger electrostatic interaction of phosphate head groups with the portlandite surface, adsorption strength and adsorption stability for HPO4 are always greater than that of HSO4 under the interference of sulfate ions. The competitive adsorption of the sulfate ion significantly weakens the interaction of hydrophilic head groups with portlandite and the dense packing of two surfactants. The calcium ion with low concentration approaches the portlandite surface and acts as an ion bridge to slightly enhance the adsorption of the surfactant. The ion bridging effect is stronger in the HPO4 system than in the HSO4 system.

Approaching the catalytic mechanism of protein lysine methyltransferases by biochemical and simulation techniques

Protein lysine methyltransferases (PKMTs) transfer up to three methyl groups to the side chains of lysine residues in proteins and fulfill important regulatory functions by controlling protein stability, localization and protein/protein interactions. The methylation reactions are highly regulated, and aberrant methylation of proteins is associated with several types of diseases including neurologic disorders, cardiovascular diseases, and various types of cancer. This review describes novel insights into the catalytic machinery of various PKMTs achieved by the combined application of biochemical experiments and simulation approaches during the last years, focusing on clinically relevant and well-studied enzymes of this group like DOT1L, SMYD1-3, SET7/9, G9a/GLP, SETD2, SUV420H2, NSD1/2, different MLLs and EZH2. Biochemical experiments have unraveled many mechanistic features of PKMTs concerning their substrate and product specificity, processivity and the effects of somatic mutations observed in PKMTs in cancer cells. Structural data additionally provided information about the substrate recognition, enzyme-substrate complex formation, and allowed for simulations of the substrate peptide interaction and mechanism of PKMTs with atomistic resolution by molecular dynamics and hybrid quantum mechanics/molecular mechanics methods. These simulation technologies uncovered important mechanistic details of the PKMT reaction mechanism including the processes responsible for the deprotonation of the target lysine residue, essential conformational changes of the PKMT upon substrate binding, but also rationalized regulatory principles like PKMT autoinhibition. Further developments are discussed that could bring us closer to a mechanistic understanding of catalysis of this important class of enzymes in the near future. The results described here illustrate the power of the investigation of enzyme mechanisms by the combined application of biochemical experiments and simulation technologies.

Designing collagens to shed light on the multi-scale structure-function mapping of matrix disorders

Collagens are the most abundant structural proteins in the extracellular matrix of animals and play crucial roles in maintaining the structural integrity and mechanical properties of tissues and organs while mediating important biological processes. Fibrillar collagens have a unique triple helix structure with a characteristic repeating sequence of (Gly-X-Y)n. Variations within the repetitive sequence can cause misfolding of the triple helix, resulting in heritable connective tissue disorders. The most common variations are single-point missense mutations that lead to the substitution of a glycine residue with a bulkier amino acid (Gly → X). In this review, we will first discuss the importance of collagen's triple helix structure and how single Gly substitutions can impact its folding, structure, secretion, assembly into higher-order structures, and biological functions. We will review the role of "designer collagens," i.e., synthetic collagen-mimetic peptides and recombinant bacterial collagen as model systems to include Gly → X substitutions observed in collagen disorders and investigate their impact on structure and function utilizing in vitro studies. Lastly, we will explore how computational modeling of collagen peptides, especially molecular and steered molecular dynamics, has been instrumental in probing the effects of Gly substitutions on structure, receptor binding, and mechanical stability across multiple length scales.

Osteocalcin: Promoter or Inhibitor of Hydroxyapatite Growth?

Osteocalcin is the most abundant noncollagenous bone protein and the functions in bone remineralization as well as in inhibition of bone growth have remained unclear. In this contribution, we explain the dual role of osteocalcin in the nucleation of new calcium phosphate during bone remodeling and in the inhibition of hydroxyapatite crystal growth at the molecular scale. The mechanism was derived using pH-resolved all-atom models for the protein, phosphate species, and hydroxyapatite, along with molecular dynamics simulations and experimental and clinical observations. Osteocalcin binds to (hkl) hydroxyapatite surfaces through multiple residues, identified in this work, and the fingerprint of binding residues varies as a function of the (hkl) crystal facet and pH value. On balance, the affinity of osteocalcin to hydroxyapatite slows down crystal growth. The unique tricalcium γ-carboxylglutamic acid (Gla) domain hereby rarely adsorbs to hydroxyapatite surfaces and faces instead toward the solution. The Gla domain enables prenucleation of calcium phosphate for new bone formation at a slightly acidic pH of 5. The growth of prenucleation clusters of calcium phosphate continues upon increase in pH value from 5 to 7 and is much less favorable, or not observed, on the native osteocalcin structure at and above neutral pH values of 7. The results provide mechanistic insight into the early stages of bone remodeling from the molecular scale, help inform mutations of osteocalcin to modify binding to apatites, support drug design, and guide toward potential cures for osteoporosis and hyperosteogeny.

pSPICA Force Field Extended for Proteins and Peptides

Many coarse-grained (CG) molecular dynamics (MD) studies have been performed to investigate biological processes involving proteins and lipids. CG force fields (FFs) in these MD studies often use implicit or nonpolar water models to reduce computational costs. CG-MD using water models cannot properly describe electrostatic screening effects owing to the hydration of ionic segments and thus cannot appropriately describe molecular events involving water channels and pores through lipid membranes. To overcome this issue, we developed a protein model in the pSPICA FF, in which a polar CG water model showing the proper dielectric response was adopted. The developed CG model greatly improved the transfer free energy profiles of charged side chain analogues across the lipid membrane. Application studies on melittin-induced membrane pores and mechanosensitive channels in lipid membranes demonstrated that CG-MDs using the pSPICA FF correctly reproduced the structure and stability of the pores and channels. Furthermore, the adsorption behavior of the highly charged nona-arginine peptides on lipid membranes changed with salt concentration, indicating the pSPICA FF is also useful for simulating protein adsorption on membrane surfaces.

Deciphering Sequence-Specific DNA Binding by H-NS Using Molecular Simulation

H-NS is a DNA organizing protein that occurs in Gram-negative bacteria. It can form long filaments between two DNA duplexes by first binding to a high-affinity AT-rich nucleotide sequence and extending from there. Using molecular dynamics simulations and steered molecular dynamics, we are able to determine the free energy of formation and dissociation of a protein-DNA complex comprising an H-NS DNA-binding domain and a specific nucleotide sequence. The molecular dynamics simulations allow detailed characterization of the interactions between the protein and a specific nucleotide sequence. To quantify the strength of the interaction, we employ an additional potential based on protein-DNA contacts to speed up dissociation of the protein-DNA complex. The work required for the dissociation results in an estimate of the free energy of dissociation/complex formation. Our protocol can provide quantitative prediction of protein-DNA complex stability, while also providing high-resolution insights into recognition mechanisms. In this chapter, we have used this approach to quantify the sequence specificity of H-NS DNA-binding domains to various nucleotide sequences, thus elucidating the mechanism with which H-NS can specifically bind to AT-rich DNA.

Decoding dissociation of sequence-specific protein-DNA complexes with non-equilibrium simulations

Sequence-specific protein-DNA interactions are crucial in processes such as DNA organization, gene regulation and DNA replication. Obtaining detailed insights into the recognition mechanisms of protein-DNA complexes through experiments is hampered by a lack of resolution in both space and time. Here, we present a molecular simulation approach to quantify the sequence specificity of protein-DNA complexes, that yields results fast, and is generally applicable to any protein-DNA complex. The approach is based on molecular dynamics simulations in combination with a sophisticated steering potential and results in an estimate of the free energy difference of dissociation. We provide predictions of the nucleotide specific binding affinity of the minor groove binding Histone-like Nucleoid Structuring (H-NS) protein, that are in agreement with experimental data. Furthermore, our approach offers mechanistic insight into the process of dissociation. Applying our approach to the major groove binding ETS domain in complex with three different nucleotide sequences identified the high affinity consensus sequence, quantitatively in agreement with experiments. Our protocol facilitates quantitative prediction of protein-DNA complex stability, while also providing high resolution insights into recognition mechanisms. As such, our simulation approach has the potential to yield detailed and quantitative insights into biological processes involving sequence-specific protein-DNA interactions.

Sequence- and Structure-Based Mining of Thermostable D-Allulose 3-Epimerase and Computer-Guided Protein Engineering To Improve Enzyme Activity

D-Allulose, a functional sweetener, can be synthesized from fructose using D-allulose 3-epimerase (DAEase). Nevertheless, a majority of the reported DAEases have inadequate stability under harsh industrial reaction conditions, which greatly limits their practical applications. In this study, big data mining combined with a computer-guided free energy calculation strategy was employed to discover a novel DAEase with excellent thermostability. Consensus sequence analysis of flexible regions and comparison of binding energies after substrate docking were performed using phylogeny-guided big data analyses. TtDAE from Thermogutta terrifontis was the most thermostable among 358 candidate enzymes, with a half-life of 32 h at 70 °C. Subsequently, structure-guided virtual screening and a customized strategy based on a combinatorial active-site saturation test/iterative saturation mutagenesis were utilized to engineer TtDAE. Finally, the catalytic activity of the M4 variant (P105A/L14C/T63G/I65A) was increased by 5.12-fold. Steered molecular dynamics simulations indicated that M4 had an enlarged substrate-binding pocket, which enhanced the fit between the enzyme and the substrate. The approach presented here, combining DAEases mining with further rational modification, provides guidance for obtaining promising catalysts for industrial-scale production.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro. The structural modification of nirmatrelvir significantly enhances the electrostatic interaction free energy between the protein and ligand and slightly decreases the vdW term. However, the vdW term is the most important factor in controlling the ligand-binding affinity. In addition, the modified nirmatrelvir might be less toxic to the human body than the original inhibitor.

Simulation of the ligand-leaving process of the human heat shock protein

The human heat shock protein plays a critical role in various diseases and is an important target for pharmacological modulation. Simulation of conformational changes and free energy profiles of the human heat shock protein derived by the ligand-leaving process is a challenging issue. In this work, steered molecular dynamics simulation was adopted to simulate the ligand-leaving process. Two composite systems of heat shock protein NHSP90 and small molecules 6FJ and 6G7 are selected as research objects. The free energy during the leaving of ligand small molecules is calculated using conventional molecular dynamics simulation, steered molecular dynamics simulation (SMD), and the umbrella sampling method. We found that the a slower pulling velocity (0.001 nm ns-1) will result in 2.19 kcal mol-1, and the umbrella sampling method gives a value of 3.26 kcal mol-1 for the free energy difference for the two systems, which reasonably agrees with experimental results. A faster-pulling velocity (0.01 nm ns-1) leads to a large overestimation of free energy. At the same time, the conformational analysis indicated that the faster pulling velocity may lead to the conformational change of NHSP90, which was proved to be false by the slower pulling velocity and the umbrella sampling method.