Molecular Dynamics Trajectories Research Articles

A salt bridge of the C-terminal carboxyl group regulates PHPT1 substrate affinity and catalytic activity.

PHPT1 is a histidine phosphatase that modulates signaling in eukaryotes through its catalytic activity. Here, we present an analysis of the structure and dynamics of PHPT1 through a combination of solution NMR, molecular dynamics, and biochemical experiments. We identify a salt bridge formed between the R78 guanidinium moiety and the C-terminal carboxyl group on Y125 that is critical for ligand binding. Disruption of the salt bridge by appending a glycine residue at the C-terminus (G126) leads to a decrease in catalytic activity and binding affinity for the pseudo substrate, para-nitrophenylphosphate (pNPP), as well as the active site inhibitor, phenylphosphonic acid (PPA). We show through NMR chemical shift, 15N relaxation measurements, and analysis of molecular dynamics trajectories, that removal of this salt bridge results in an active site that is altered both structurally and dynamically thereby significantly impacting enzymatic function and confirming the importance of this electrostatic interaction.

Adsorption of Glycine on TiO2 in Water from On-the-fly Free-Energy Calculations and In Situ Electrochemical Impedance Spectroscopy.

We report here an experimental-computational study of hydrated TiO2 anatase nanoparticles interacting with glycine, where we obtain quantitative agreement of the measured adsorption free energies. Ab initio simulations are performed within the tight binding and density functional theory in combination with enhanced free-energy sampling techniques, which exploit the thermodynamic integration of the unbiased mean forces collected on-the-fly along the molecular dynamics trajectories. The experiments adopt a new and efficient setup for electrochemical impedance spectroscopy measurements based on portable screen-printed gold electrodes, which allows fast and in situ signal assessment. The measured adsorption free energy is -30 kJ/mol (both from experiment and calculation), with preferential interaction of the charged NH3+ group which strongly adsorbs on the TiO2 bridging oxygens. This highlights the importance of the terminal amino groups in the adsorption mechanism of amino acids on hydrated metal oxides. The excellent agreement between computation and experiment for this amino acid opens the doors to the exploration of the interaction free energies for other moderately complex bionano systems.

FROG: Exploiting all-atom molecular dynamics trajectories to calculate linear and non-linear optical responses of molecular liquids within Dalton's QM/MM polarizable embedding scheme.

Quantum mechanical/molecular mechanics (QM/MM) methods are interesting to model the impact of a complex environment on the spectroscopic properties of a molecule. In this context, a FROm molecular dynamics to second harmonic Generation (FROG) code is a tool to exploit molecular dynamics trajectories to perform QM/MM calculations of molecular optical properties. FROG stands for "FROm molecular dynamics to second harmonic Generation" since it was developed for the calculations of hyperpolarizabilities. These are relevant to model non-linear optical intensities and compare them with those obtained from second harmonic scattering or second harmonic generation experiments. FROG's specificity is that it is designed to study simple molecular liquids, including solvents or mixtures, from the bulk to the surface. For the QM/MM calculations, FROG relies on the Dalton package: its electronic-structure models, response theory, and polarizable embedding schemes. FROG helps with the global workflow needed to deal with numerous QM/MM calculations: it permits the user to separate the system into QM and MM fragments, to write Dalton's inputs, to manage the submission of QM/MM calculations, to check whether Dalton's calculation finished successfully, and finally to perform averages on relevant QM observables. All molecules within the simulation box and several time steps are tackled within the same workflow. The platform is written in Python and installed as a package. Intermediate data such as local electric fields or individual molecular properties are accessible to the users in the form of Python object arrays. The resulting data are easily extracted, analyzed, and visualized using Python scripts that are provided in tutorials.

Pharmacokinetic studies, molecular docking, and molecular dynamics simulations of phytochemicals from Morus alba: a multi receptor approach for potential therapeutic agents in colorectal cancer.

This study explores the therapeutic potential of phytochemicals derived from Morus alba for colorectal cancer (CRC) treatment. Colorectal cancer is a global health concern with increasing mortality rates, necessitating innovative strategies for prevention and therapy. Employing in silico analysis, molecular docking techniques (MDT), and molecular dynamics simulations (MDS), the study investigates the interactions between Morus alba-derived phytochemicals and key proteins (AKT1, Src, STAT3, EGFR) implicated in CRC progression. ADME/T analysis screens 78 phytochemicals for drug-like and pharmacokinetic properties. The study integrates Lipinski's Rule of Five and comprehensive bioactivity assessments, providing a nuanced understanding of Morus alba phytoconstituent's potential as CRC therapeutic agents. Notably, 14 phytochemicals out of 78 emerge as potential candidates, demonstrating oral bioavailability and favorable bioactivity scores. Autodock 1.5.7 is employed for energy minimization followed by molecular docking with the highest binding energy observed to be -11.7kcal/mol exhibited by Kuwanon A against AKT1. Molecular dynamics simulations and trajectory path analysis were conducted between Kuwanon A and AKT1 at the Pleckstrin homology (PH) domain region (TRP80), revealing minimal deviations. In comparison to the standard drug Capivasertib, the phytochemical Kuwanon A emerges as a standout candidate based on computational analysis. This suggests its potential as an alternative to mitigate the limitations associated with the standard drug. The research aims to provide insights for future experimental validations and to stimulate the development of Kuwanon A as a novel, effective therapeutic agent for managing colorectal cancer.

Exploring Biomolecular Conformational Dynamics with Polarizable Force Field AMOEBA and Enhanced Sampling Method Milestoning.

Conformational dynamics play a crucial role in determining the behavior of the biomolecules. Polarizable force fields, such as AMOEBA, can accurately capture electrostatic interactions underlying the conformational space. However, applying a polarizable force field in molecular dynamics (MD) simulations can be computationally expensive, especially in studying long-time-scale dynamics. To overcome this challenge, we incorporated the AMOEBA potential with Milestoning, an enhanced sampling method in this work. This integration allows us to efficiently sample the rare and important conformational states of a biomolecule by using many short and independent molecular dynamics trajectories with the AMOEBA force field. We applied this method to investigate the conformational dynamics of alanine dipeptide, DNA, and RNA A-B form conversion. Well-converged thermodynamic and kinetic properties were obtained, including the free energy difference, mean first passage time, and critical transitions between states. Our results demonstrate the power of integrating polarizable force fields with enhanced sampling methods in quantifying the thermodynamic and kinetic properties of biomolecules at the atomic level.

AlphaFold2 modeling and molecular dynamics simulations of an intrinsically disordered protein.

We use AlphaFold2 (AF2) to model the monomer and dimer structures of an intrinsically disordered protein (IDP), Nvjp-1, assisted by molecular dynamics (MD) simulations. We observe relatively rigid dimeric structures of Nvjp-1 when compared with the monomer structures. We suggest that protein conformations from multiple AF2 models and those from MD trajectories exhibit a coherent trend: the conformations of an IDP are deviated from each other and the conformations of a well-folded protein are consistent with each other. We use a residue-residue interaction network (RIN) derived from the contact map which show that the residue-residue interactions in Nvjp-1 are mainly transient; however, those in a well-folded protein are mainly persistent. Despite the variation in 3D shapes, we show that the AF2 models of both disordered and ordered proteins exhibit highly consistent profiles of the pLDDT (predicted local distance difference test) scores. These results indicate a potential protocol to justify the IDPs based on multiple AF2 models and MD simulations.

Unveiling molecular insights: in silico exploration of TLR4 antagonist for management of dry eye syndrome

BackgroundDry eye disease is the most commonplace multifractional ocular complication, which has already affected millions of people in the world. It is identified by the excessive buildup of reactive oxygen...

Bayesian network models identify cooperative GPCR:G protein interactions that contribute to G protein coupling

Cooperative interactions in protein-protein interfaces demonstrate the interdependency or the linked network- like behavior and their effect on the coupling of proteins. Cooperative interactions also could cause ripple or allosteric effects at a distance in protein-protein interfaces. Although they are critically important in protein-protein interfaces, it is challenging to determine which amino acid pair interactions are cooperative. In this work we have used Bayesian network modeling, an interpretable machine learning method, combined with molecular dynamics trajectories to identify the residue pairs that show high cooperativity and their allosteric effect in the interface of G protein coupled receptor (GPCR) complexes with Gα subunits. Our results reveal six GPCR:Gα contacts that are common to the different Gα subtypes and show strong cooperativity in the formation of interface. Both the C-terminus helix5 and the core of the G protein are codependent entities and play an important role in GPCR coupling. We show that a promiscuous GPCR coupling to different Gα subtypes, makes all the GPCR:Gα contacts that are specific to each Gα subtype (Gαs, Gαi and Gαq). This work underscores the potential of data-driven Bayesian network modeling in elucidating the intricate dependencies and selectivity determinants in GPCR:G protein complexes, offering valuable insights into the dynamic nature of these essential cellular signaling components.

A combined in vitro and in silico study of the inhibitory mechanism of angiotensin-converting enzyme with peanut peptides

Food-derived peptides with low molecular weight, high bioavailability, and good absorptivity have been exploited as angiotensin-converting enzyme (ACE) inhibitors. In the present study, in-vitro inhibition kinetics of peanut peptides, in silico screening, validation of ACE inhibitory activity, molecular dynamics (MD) simulations, and HUVEC cells were performed to systematically identify the inhibitory mechanism of ACE interacting with peanut peptides. The results indicate that FPHPP, FPHY, and FPHFD peptides have good thermal, pH, and digestive stability. MD trajectories elucidate the dynamic correlation between peptides and ACE and verify the specific binding interaction. Noteworthily, FPHPP is the best inhibitor with a strongest binding affinity and significantly increases NO, SOD production, and AT2R expression, and decreases ROS, MDA, ET-1 levels, ACE, and AT1R accumulation in Ang II-injury HUVEC cells.

Insights into Molecular Interactions between a GAPDH-Related Fish Antimicrobial Peptide, Analogs Thereof, and Bacterial Membranes.

Interactions between SJGAP (skipjack tuna GAPDH-related antimicrobial peptide) and four analogs thereof with model bacterial membranes were studied using Fourier-transform infrared spectroscopy (FTIR) and molecular dynamics (MD) simulations. MD trajectory analyses showed that the N-terminal segment of the peptide analogs has many contacts with the polar heads of membrane phospholipids, while the central α helix interacts strongly with the hydrophobic core of the membranes. The peptides also had a marked influence on the wave numbers associated with the phase transition of phospholipids organized as liposomes in both the interface and aliphatic chain regions of the infrared spectra, supporting the interactions observed in the MD trajectories. In addition, interesting links were found between peptide interactions with the aliphatic chains of membrane phospholipids, as determined by FTIR and from the MD trajectories, and the membrane permeabilization capacity of these peptide analogs, as previously demonstrated. To summarize, the combined experimental and computational efforts have provided insights into crucial aspects of the interactions between the investigated peptides and bacterial membranes. This work thus makes an original contribution to our understanding of the molecular interactions underlying the antimicrobial activity of these GAPDH-related antimicrobial peptides from Scombridae.

On the Mechanism of the Lysosomal Enzyme Iduronate‐2‐sulfatase. A Multiscale Approach

AbstractIduronate‐2‐sulfatase (IDS) is a Ca2+‐dependent enzyme belonging to the family of sulfatases that catalyzes the hydrolysis of sulphurylated glycosaminoglycans (GAGs), like dermatan and heparan sulphate. Its deficiency or modification leads to the accumulation of GAGs in the human body and to the occurrence of severe conditions, such as Hunter disease, or Mucopolysaccharidosis type II. Due to its involvement in this syndrome, it is of interest to understand the action mechanism of the enzyme to design new drugs for more efficient medical strategies. In the present work, we carried out a detailed multiscale modelling‐based investigation, adopting molecular dynamics simulation (MDs) and QM/MM calculations to study the enzyme‐dermatan sulfate interactions and to investigate the reaction mechanism of IDS. The analysis of molecular dynamics trajectories helped to shed light on the contribution of the individual residues of the active site in the recognition of dermatan sulfate. The role of FGly84 and His229, investigated in both neutral and protonated states in the case of the latter, is highlighted for the binding of the substrate. QM/MM calculations demonstrated that the reaction mechanism is a two‐steps process occurring via a sulphurylation‐desulphurylation mechanism where the FGly84 first attacks the sulphate group of the dermatan sulphate (DS), and later is desulphurylated.

DSSP in GROMACS: Tool for Defining Secondary Structures of Proteins in Trajectories.

This work describes a fast implementation of a software algorithm associated with determination of protein secondary structure based on the Define Secondary Structure of Proteins (DSSP) algorithm. This implementation is fully compatible with the DSSP v.4 and DSSP v.2 algorithms and implemented as a native GROMACS trajectory analysis module, which allows us to analyze molecular dynamics trajectories without any restrictions of the original DSSP implementation. This implementation works much faster than the original DSSP v.4 and DSSP v.2 algorithms.

Environmentally Driven Symmetry Breaking Quenches Dual Fluorescence in Proflavine.

Nonadiabatic couplings between several electronic excited states are ubiquitous in many organic chromophores and can significantly influence optical properties. A recent experimental study demonstrated that the proflavine molecule exhibits surprising dual fluorescence in the gas phase, which is suppressed in polar solvent environments. Here, we uncover the origin of this phenomenon by parametrizing a linear-vibronic coupling Hamiltonian from spectral densities of system-bath coupling constructed along molecular dynamics trajectories, fully accounting for interactions with the condensed-phase environment. The finite-temperature absorption, steady-state emission, and time-resolved emission spectra are then computed using powerful, numerically exact tensor network approaches. We find that the dual fluorescence in vacuum is driven by a single well-defined coupling mode but is quenched in solution due to dynamic solvent-driven symmetry breaking that mixes the two low-lying electronic states. We expect the computational framework developed here to be widely applicable to the study of non-Condon effects in complex condensed-phase environments.

Exploring the Anticancer Potential of Semisynthetic Derivatives of 7α-Acetoxy-6β-hydroxyroyleanone from Plectranthus sp.: An In Silico Approach.

The diterpene 7α-acetoxy-6β-hydroxyroyleanone isolated from Plectranthus grandidentatus demonstrates promising antibacterial, anti-inflammatory and anticancer properties. However, its bioactivity may be enhanced via strategic structural modifications of such natural products through semisynthesis. The anticancer potential of 7α-acetoxy-6β-hydroxyroyleanone and five derivatives was analyzed in silico via the prediction of chemicals absorption, distribution, metabolism, excretion, and toxicity (ADMET), quantum mechanical calculations, molecular docking and molecular dynamic simulation. The protein targets included regulators of apoptosis and cell proliferation. Additionally, network pharmacology was used to identify potential targets and signaling pathways. Derivatives 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone achieved high predicted binding affinities towards their respective protein panels, with stable molecular dynamics trajectories. Both compounds demonstrated favorable ADMET parameters and toxicity profiles. Their stability and reactivity were confirmed via geometry optimization. Network analysis revealed their involvement in cancer-related pathways. Our findings justify the inclusion of 7α-acetoxy-6β-hydroxy-12-O-(2-fluoryl)royleanone and 7α-acetoxy-6β-(4-fluoro)benzoxy-12-O-(4-fluoro)benzoylroyleanone in in vitro analyses as prospective anticancer agents. Our binding mode analysis and stability simulations indicate their potential as selective inhibitors. The data will guide studies into their structure optimization, enhancing efficacy and drug-likeness.

Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation, and molecular dynamic simulation.

SHP2 is a non-receptor protein tyrosine phosphatase to remove tyrosine phosphorylation. Functionally, SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT, and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). For the SHP2 target, the FMO method prediction has a high correlation between the binding affinity of the protein-ligand interaction and experimental values (R2 = 0.55), demonstrating a significant advantage over the MM/PBSA (R2 = 0.02) and MM/GBSA (R2 = 0.15) methods. Therefore, we employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide (7188-0011) effectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound 7188-0011 and the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors. All the molecular docking studies were employed to identify potential leads with Maestro v10.1. The protein-ligand binding affinities of potential leads were further predicted by FMO calculations at MP2/6-31G* level using GAMESS v2020 system. MD simulations were carried out with AmberTools18 by applying the FF14SB force field. MD trajectories were analyzed using VMD v1.9.3. MM/GB(PB)SA binding free energy analysis was carried out with the mmpbsa.py tool of AmberTools18. The docking and MD simulation results were visualized through PyMOL v2.5.0.

From complex data to clear insights: visualizing molecular dynamics trajectories.

Advances in simulations, combined with technological developments in high-performance computing, have made it possible to produce a physically accurate dynamic representation of complex biological systems involving millions to billions of atoms over increasingly long simulation times. The analysis of these computed simulations is crucial, involving the interpretation of structural and dynamic data to gain insights into the underlying biological processes. However, this analysis becomes increasingly challenging due to the complexity of the generated systems with a large number of individual runs, ranging from hundreds to thousands of trajectories. This massive increase in raw simulation data creates additional processing and visualization challenges. Effective visualization techniques play a vital role in facilitating the analysis and interpretation of molecular dynamics simulations. In this paper, we focus mainly on the techniques and tools that can be used for visualization of molecular dynamics simulations, among which we highlight the few approaches used specifically for this purpose, discussing their advantages and limitations, and addressing the future challenges of molecular dynamics visualization.

Insights into Lithium Sulfide Glass Electrolyte Structures and Ionic Conductivity via Machine Learning Force Field Simulations.

Sulfide-based solid electrolytes (SEs) are important for advancing all-solid-state batteries (ASSBs), primarily due to their high ionic conductivities and robust mechanical stability. Glassy SEs (GSEs) comprising mixed Si and P glass formers are particularly promising for their synthesis process and their ability to prevent lithium dendrite growth. However, to date, the complexity of their glassy structures hinders a complete understanding of the relationships between their structures and properties. This study introduces a new machine learning force field (ML-FF) tailored for lithium sulfide-based GSEs, enabling the exploration of their structural characteristics, mechanical properties, and lithium ionic conductivities. Using molecular dynamic (MD) simulations with this ML-FF, we explore the glass structures in varying compositions, including binary Li2S-SiS2 and Li2S-P2S5 as well as ternary Li2S-SiS2-P2S5. Our simulations yielded consistent results in terms of density, elastic modulus, radial distribution functions, and neutron structure factors compared to DFT and experimental work. Our findings reveal distinct local environments for Si and P within these glasses, with most Si atoms in edge-sharing configurations in Li2S-SiS2 and a mix of corner- and edge-sharing tetrahedra in the ternary Li2S-SiS2-P2S5 composition. For lithium ionic conductivity at 300 K, the 50Li2S-50SiS2 glass displayed the lowest conductivity at 2.1 mS/cm, while the 75Li2S-25P2S5 composition exhibited the highest conductivity at 3.6 mS/cm. The ternary glass showed a conductivity of 2.6 mS/cm, sitting between the two. Moreover, an in-depth analysis of lithium ion diffusion over the MD trajectory in the ternary glass demonstrated a significant correlation between diffusion pathways and the rotational dynamics of nearby SiS4 or PS4 tetrahedra. The ML-FF developed in this study provides an important tool for exploring a broad spectrum of solid-state and mixed former sulfide-based electrolytes.

Using a Drug Repurposing Strategy to Virtually Screen Potential HIV-1 Entry Inhibitors That Block the NHR Domain of the Viral Envelope Protein gp41

Using a drug repurposing strategy, virtual screening of potential inhibitors of the NHR domain of the HIV-1 gp41 protein, a conserved region critical for the virus-cell membrane fusion and viral infectivity, was carried out. The used computational approach included: (1) molecular docking of this functionally significant region of the HIV-1 envelope with compounds from a library of bioactive molecules containing clinically approved drugs, experimental drugs, and investigational drug candidates; (2) assessing the binding affinity of these compounds to the therapeutic target; (3) molecular dynamics simulations of ligand/NHR-gp41 complexes; (4) calculations of the binding free energy followed by the analysis of molecular dynamics trajectories and selection of compounds promising to test for anti-HIV-1 activity. As a result, six compounds that exhibited the high binding affinity to the NHR domain of the HIV-1 gp41 protein and showed acceptable pharmacological properties were identified. The predicted compounds are assumed to form a promising basis for the development of new, effective and safe broad-spectrum antiviral agents able to inhibit the HIV-1 entry into the host cell.

Temperature-transferable tight-binding model using a hybrid-orbital basis.

Finite-temperature calculations are relevant for rationalizing material properties, yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged as promising alternatives, but transferability of such methods to elevated temperatures in a data-efficient way remains a great challenge. In this work, we suggest a tight-binding model for efficient and accurate calculations of temperature-dependent properties of semiconductors. Our approach utilizes physics-informed modeling of the electronic structure in the form of hybrid-orbital basis functions and numerically integrating atomic orbitals for the distance dependence of matrix elements. We show that these design choices lead to a tight-binding model with a minimal amount of parameters that are straightforwardly optimized using density functional theory or alternative electronic-structure methods. The temperature transferability of our model is tested by applying it to existing molecular-dynamics trajectories without explicitly fitting temperature-dependent data and comparison with density functional theory. We utilize it together with machine-learning molecular dynamics and hybrid density functional theory for the prototypical semiconductor gallium arsenide. We find that including the effects of thermal expansion on the onsite terms of the tight-binding model is important in order to accurately describe electronic properties at elevated temperatures in comparison with experiment.

Computational Investigation of the Covalent Inhibition Mechanism of Bruton's Tyrosine Kinase by Ibrutinib.

Covalent inhibitors represent a promising class of therapeutic compounds. Nonetheless, rationally designing covalent inhibitors to achieve a right balance between selectivity and reactivity remains extremely challenging. To better understand the covalent binding mechanism, a computational study is carried out using the irreversible covalent inhibitor of Bruton tyrosine kinase (BTK) ibrutinib as an example. A multi-μs classical molecular dynamics trajectory of the unlinked inhibitor is generated to explore the fluctuations of the compound associated with the kinase binding pocket. Then, the reaction pathway leading to the formation of the covalent bond with the cysteine residue at position 481 via a Michael addition is determined using the string method in collective variables on the basis of hybrid quantum mechanical-molecular mechanical (QM/MM) simulations. The reaction pathway shows a strong correlation between the covalent bond formation and the protonation/deprotonation events taking place sequentially in the covalent inhibition reaction, consistent with a 3-step reaction with transient thiolate and enolates intermediate states. Two possible atomistic mechanisms affecting deprotonation/protonation events from the thiolate to the enolate intermediate were observed: a highly correlated direct pathway involving proton transfer to the Cα of the acrylamide warhead from the cysteine involving one or a few water molecules and a more indirect pathway involving a long-lived enolate intermediate state following the escape of the proton to the bulk solution. The results are compared with experiments by simulating the long-time kinetics of the reaction using kinetic modeling.