Structural Molecular Dynamics Research Articles

Influence of Pnictogen and Ligand Framework on the Lewis Acidity and Steric Environment in Pnictogen Pincer Complexes.

Pnictogen pincer complexes are a fascinating class of compounds due to their dynamic molecular and electronic structures, and valuable stoichiometric or catalytic reactivity. As recognition of their unique chemistry has grown, so too has the library of pincer ligands employed and pnictogen centres engaged to prepare them. Here we computationally study how the choice of pincer ligand framework and pnictogen influence the electronic and steric outcomes of the complexes obtained. The most relevant electronic parameter is the pnictogen-centred electrophilicity, which has been quantified by fluoride ion affinities and LUMO energies, while the most relevant steric parameter is the crowding around the central pnictogen, which has been quantified by the %Vbur values and visualized using steric maps. The resulting trends are analyzed with reference to binding pocket size, acceptor orbital type, electronic delocalization, π-donor strengths, and heteroatom incorporation. Thus, considering 16 ligand frameworks and 4 heavy pnictogen centres, this study provides a broad-spectrum view of stereo-electronic variation in pnictogen pincer complexes, which, together with a recent study on geometric variation in the same family, provides a substantial dataset to guide future molecular design and reactivity studies.

Characterizing the Orderliness of Interfacial Water through Stretching Vibrations.

Spatial orderliness, which is the orderly structure of molecules, differs significantly between interfacial water and bulk water. Understanding this property is essential for various applications in both natural and engineered environments. However, the subnanometer thickness of interfacial water presents challenges for direct and rapid characterization of its structural orderliness. Herein, through molecular dynamics simulations and infrared spectral analysis of interfacial water in a graphene slit pore, we reveal a hyperbolic tangent relationship between the water ordering and its O-H stretching information in the infrared spectrum. Specifically, O-H symmetric stretching dominated in the highly ordered water structure, while a transition to the asymmetric stretching corresponded to an increase in the degree of disorder. Thus, the O-H stretching behavior could serve as a useful and quick assessment of the orderliness of interfacial water. This work provided insights into interfacial water's unique molecular network and structural dynamics and identified the stretching vibrations' key role in its degree of order, providing insight for fields such as nanotechnology, biology, and material science.

Carbonyl stretching band in amides as Lorentz oscillator. Insights into anharmonicity and local environment in the liquid phase from NIR and MIR spectra of N-methylformamide and di-N,N-methylformamide

We investigated the anharmonicity and intermolecular interactions of N-methylformamide (NMF) and di-N,N-methylformamide (DMF) in the neat liquid phase with particular interest in the amide bands. The vibrational spectra, complex refractive index, and complex electric permittivity were determined in in the mid- (MIR) and near-infrared (NIR) regions (11,500–560 cm−1; 870–17857 nm). Dispersion analysis was based on the Classical Damped Harmonic Oscillator (CDHO) and simultaneous modelling of the real and imaginary components of the spectra. This data delivered insights into the vibrational energy dissipation and self-association in liquid amides. Identification of the MIR and NIR bands was based on anharmonic GVPT2//B3LYP/6-311++G(d,p) calculations. DMF and NMF follow distinct self-association, evidenced in the MIR fingerprint by the two components of the νCO, the analog of the Amide I band. These conclusions are supported by the structural information derived from the NIR spectra. Furthermore, the contribution of overtones and combination bands in the MIR spectra of amides was examined. The conclusions on molecular interactions and structural dynamics of NMF and DMF contribute to a deeper understanding of the effects of changes in the local environment of the amide group.

Molecular Structure and Rotational Dynamics in the Acetonitrile:Acetylene (1:2) Plastic Co-crystal at Titan Conditions

Molecular Structure and Rotational Dynamics in the Acetonitrile:Acetylene (1:2) Plastic Co-crystal at Titan Conditions

Hydration effects on molecular structure, vibrational, electronic properties, drug-likeness analysis, molecular docking, and molecular dynamics studies of (Z)-3-(2-oxo-2-phenylethylidene)indolin-2-one

This study examines how solvation models, including COSMO, CPCM, PCM, and SMD, affect the conformations, molecular structure, vibrational behaviors, and electronic properties of the (Z)-3-(2-oxo-2-phenylethylidene)indolin-2-one (Z3-2O2PEI) molecule within the framework of the B3LYP/cc-pVTZ model chemistry, along with its potential effectiveness as a drug for treating COVID-19. The most stable molecular conformation of the studied coùpound was identified in the presence of the COSMO solvent. The theoretical vibrational frequencies matched the experimentally observed FT-IR and Raman data. Solvent influences on the UV–vis spectra of Z3-2O2PEI revealed electronic transitions from n to π* and π to π* orbitals, along with evidence of intermolecular charge transfer (ICT) supported by analyses of Frontier Molecular Orbitals (FMO) and Natural Bond Orbitals (NBO). The examinations of Mulliken atomic charge distributions and molecular electrostatic potential surfaces reveal the electrophilic and nucleophilic sites of the molecule under study. Non-covalent Interaction (NCI) analysis confirmed the presence of steric effects and Van der Waals interactions within the compound. According to molecular docking studies, the Z3-2O2PEI molecule exhibits significant binding affinity for PLpro, a protein targeted in COVID-19 treatment. This finding is supported by subsequent molecular dynamics analysis, validating the docking results.

Molecular structure, spectroscopic (FT-IR, NMR and UV–Vis), electronic properties, molecular docking, and molecular dynamics studies on novel thiazolidinone derivative: A potent breast cancer drug

This study details the synthesis, characterization, and comprehensive computational analysis of a novel (R)-camphor-based thiazolidinone derivative. The structural integrity of the synthesized compound was confirmed using 1H- and 13CNMR spectroscopy as well as HRMS. Theoretical vibrational modes were successfully assigned and matched well with the observed FT-IR spectrum. Simulated NMR values were in excellent agreement with experimental chemical shifts, showing maximum deviations of less than 1.5 ppm for 1H and 4 ppm for 13C. UV–vis spectroscopy revealed n→π* and π→π* transitions in thiazolidinone, with intermolecular charge transfer (ICT) corroborated by Frontier Molecular Orbital (FMO), density of states (DOS), and Natural Bond Orbital (NBO) analyses. Molecular docking studies indicated that thiazolidinone exhibited strong inhibitory activity against the breast cancer target protein 6HQO, which was further validated by molecular dynamics simulations, confirming the stability and accuracy of the docking results.

Targeting MDM2-p53 interaction in Glioblastoma: Transcriptomic analysis and Peptide-Based inhibition strategy

MDM2 is a gene that encodes a protein involved in cell survival, growth, and DNA repair. It has been implicated in the development and progression of glioblastoma (GBM). Inhibition of the MDM2-p53 interaction has emerged as a promising strategy for treating GBM. In this study, we performed comprehensive transcriptomic expression analysis from diverse datasets and observed MDM2 overexpression in a subset of GBM cases. MDM2 negatively regulates the major onco-suppressor p53. The interaction between MDM2 and p53 is a promising target for cancer therapy, as it can trigger p53-mediated cell death in response to different stress conditions, such as oncogene activation or DNA damage. In this study, we have identified a peptide-based inhibition of MDM2 as a therapeutic strategy for GBM. We have further validated the stability of the MDM2-peptide interaction using a molecular structural dynamics approach. The major trajectories, including root mean square of deviation (RMSD), root mean square of fluctuation (RMSF), and radius of gyration (RoG), indicate that the candidate peptides have a more stable binding compared to the native ligand and control drug. The stability of the binding interaction was further estimated by MMGBSA analysis, which also suggests that MDM2 has a stable binding with both peptide molecules. Based on these results, peptides P-1843 and P-3837 could be tested further for experimental validation to confirm their targeted inhibition of MDM-2. This approach could provide a highly selective and efficient inhibitor with potentially fewer side effects and less toxicity compared to small drug-based molecules.

EMMAs: Implementation and Assessment of a Suite of Cross-Disciplinary, Case-Based High School Activities to Explore Three-Dimensional Molecular Structure, Noncovalent Interactions, and Molecular Dynamics.

Students frequently develop misconceptions about noncovalent interactions that make it challenging for them to appropriately interpret aspects of molecular structure and interactions critical to myriad applications. We hypothesized that computational molecular modeling and visualization could provide a valuable approach to help address these core misconceptions when students are first exposed to these concepts in secondary school chemistry courses. Here, we present a series of activities exploring biomolecular drug-target interactions using molecular visualization software and an introduction to molecular dynamics methods that were implemented in secondary school classrooms. A pre- and postsurvey approach that incorporated Likert response type, written free response, and drawing-based items demonstrated that students gained an enhanced conceptualization of intermolecular interactions, particularly related to aspects of shape complementarity, after completing the activities. Students also expressed increased comfort with and facility in utilizing different three-dimensional representations of molecules in their postsurvey responses. The activities led to an increased appreciation of interdisciplinary connections of chemistry with mathematics and physics. Overall, the modular activities presented provide a relatively time-efficient and accessible manner to help promote an understanding of a traditionally challenging topic for beginning chemistry students while introducing them to contemporary research tools.

M-Polynomial and NM-Polynomial Methods for Topological Indices of Polymers

Topological indices (TIs) are numerical tools widely applied in chemometrics, biomedicine, and bioinformatics for predicting diverse physicochemical attributes and biological activities within molecular structures. Despite their significance, the challenges in deriving TIs necessitate novel approaches. This study addresses the limitations of conventional methods in dealing with dynamic molecular structures, focusing on the neighborhood M-polynomial (NM-polynomial), a pivotal polynomial for calculating degree-based TIs. Current literature acknowledges these polynomials but overlooks their limited adaptability to intricate biopolymer relationships. Our research advances by computing degree-based and neighborhood degree-based indices for prominent biopolymers, including polysaccharides, poly-γ-glutamic acid, and poly-L-lysine. Through innovative utilization of the NM-polynomial and the M-polynomial, we establish a fresh perspective on molecular structure and topological indices. Moreover, we present diverse graph representations highlighting the nuanced correlations between indices and structural parameters. By systematically investigating these indices and their underlying polynomials, our work contributes to predictive modelling in various fields. This exploration sheds light on intricate biochemical systems, offering insights into applications encompassing medicine, the food industry, and wastewater treatment. This research deepens our understanding of complex molecular interactions and paves the way for enhanced applications in diverse industries.

An investigation of water status in gelatin methacrylate hydrogels by means of water relaxometry and differential scanning calorimetry.

The relationship between molecular structure and water dynamics is a fundamental yet often neglected subject in the field of hydrogels for drug delivery, bioprinting, as well as biomaterial science and tissue engineering & regenerative medicine (TE&RM). Water is a fundamental constituent of hydrogel systems and engages via hydrogen bonding with the macromolecular network. The methods and techniques to measure and reveal the phenomena and dynamics of water within hydrogels are still limited. In this work, differential scanning calorimetry (DSC) was used as a quantitative method to analyze freezable (including free and freezable bound) and non-freezable bound water within gelatin methacrylate (GelMA) hydrogels. Nuclear magnetic resonance (NMR) is a complementary method for the study of water behavior and can be used to measure the spin-relaxation of water hydrogen nuclei, which is related to water dynamics. In this research, nuclear magnetic resonance relaxometry was employed to investigate the molecular state of water in GelMA hydrogels using spin-lattice (T1) and spin-spin (T2) spin-relaxation time constants. The data displays a trend of increasing bound water content with increasing GelMA concentration. In addition, T2 values were further applied to calculate microviscosity and translational diffusion coefficients. Water relaxation under various chemical environments, including different media, temperatures, gelatin sources, as well as crosslinking effects, were also examined. These comprehensive physical data sets offer fundamental insight into biomolecule transport within the GelMA hydrogel system, which ultimately are important for drug delivery, bioprinting, as well as biomaterial science and TE&RM communities.

Molecular structural dynamics in water–ethanol mixtures: Spectroscopy with polarized neutrons simultaneously accessing collective and self-diffusion

Binary mixtures of water with lower alcohols display non-linear phase behaviors upon mixing, which are attributed to potential cluster formation at the molecular level. Unravelling such elusive structures requires investigation of hydrogen-bonding sub-nanosecond dynamics. We employ high-resolution neutron time-of-flight spectroscopy with polarization analysis in combination with selective deuteration to study the concentration-dependent structural dynamics in the water rich part of the phase diagram of water–ethanol mixtures. This method enables simultaneous access to atomic correlations in space and time and allows us to separate spatially incoherent scattering probing self-diffusion of the ethanol fraction from the coherent scattering probing collective diffusion of the water network as a whole. Our observations indicate an enhanced rigidity of the hydrogen bond network at the mesoscopic length scale compared to the molecular scale as the ethanol fraction increases, which is consistent with the hypothesis of clusters.

Structural Changes to the Gd‐DTPA Complex at Varying Ligand Protonation State

AbstractDiethylenetriaminepentaacetic acid (DTPA) is a chelating agent whose complex with the Gd3+ ion is used in medical imaging. DTPA is also used in lanthanide‐actinide separation processes. As protonation of the DTPA ligand can facilitate dissociation of the Gd3+ ion from the Gd‐DTPA complex, this work investigates the coordination structures of the aqueous Gd3+ ion and its environment when chelated by DTPA in eight different DTPA protonation states. Both classical and ab initio molecular dynamics (MD) simulations are conducted to model the solvated complexes. Extended X‐ray absorption fine structure (EXAFS) measurements of the Gd3+aqua ion, and the Gd‐DTPA complex at pH 1 and 11, are compared to EXAFS spectra predicted from the MD simulations to verify the accuracy of the MD structures. The findings of this work provide atomic‐level details into the fluctuating Gd‐DTPA complex environment as the DTPA ligand gradually detaches from the Gd3+ ion with increased protonation.

Application of molecular dynamic simulations in modeling the excited state behavior of confined molecules.

Relative to isotropic organic solvent medium, the structure and conformation of a reactant molecule in an organized and confining medium are often different. In addition, because of the rigidity of the immediate environment, the reacting molecule have a little freedom to undergo large changes even upon gaining energy or modifications in the electronic structure. These alterations give rise to differences in the photochemistry of a molecular and supramolecular species. In this study, one such example is presented. α-Alkyl dibenzylketones upon excitation in isotropic solvents give products via Norrish type I and type II reactions that are independent of the chain length of the alkyl substituent. On the other hand, when these molecules are enclosed within an organic capsule of volume ~ 550Å3, they give products that are strikingly dependent on the length of the α-alkyl substitution. These previously reported experimental observations are rationalized based on the structures generated by molecular modeling (docking and molecular dynamics (MD) simulations). It is shown that MD simulations that are utilized extensively in biologically important macromolecules can also be useful to understand the excited state behavior of reactive molecules that are part of supramolecular assemblies. These simulations can provide structural information of the reactant molecule and the surroundings complementing that with the one obtained from 1 and 2D NMR experiments. MD simulated structures of seven α-alkyl dibenzylketones encapsulated within the octa acid capsule provide a clear understanding of their unique behavior in this restricted medium. Because of the rigidity of the medium, these structures although generated in the ground state can rationalize the photochemical behavior of the molecules in the excited state.

Structural characterization of an intrinsically disordered protein complex using integrated small-angle neutron scattering and computing.

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure-function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small-angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue-specific deuterium labeling SANS experiments, microsecond all-atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder-based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB-disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs.

Shedding light into the biological activity of aminopterin, via molecular structural, docking, and molecular dynamics analyses

In this study, the structural and anticancer properties of aminopterin, as well as its antiviral characteristics, were elucidated. The preferred conformations of the title molecule were investigated with semiempirical AM1 method, and the obtained the lowest energy conformer was then optimized by using density functional (DFT/B3LYP) method with 6-311++G(d,p) as basis set. The vibrational frequencies of the optimized structure were calculated by the same level of theory and were compared with the experimental values. The vibrational assignments were performed based on the computed potential energy distribution (PED) of the vibrational modes. The molecular electrostatic potential (MEP) and frontier molecular orbitals (HOMO, LUMO) analyses were carried out for the optimized structure and the chemical reactivity has been scrutinized. To enlighten the biological activity of aminopterin as anticancer and anti-COVID-19 agents, aminopterin was docked into DNA, αIIBβ3 and α5β1integrins, human dihydrofolate reductase, main protease (Mpro) of SARS-CoV-2 and SARS-CoV-2/ACE2 complex receptor. The binding mechanisms of aminopterin with the receptors were clarified. The molecular docking results revealed the strong interaction of the aminopterin with DNA (−8.2 kcal/mol), αIIBβ3 and α5β1 integrins (−9.0 and −10.8 kcal/mol, respectively), human dihydrofolate reductase (−9.7 kcal/mol), Mpro of SARS-CoV-2 (−6.7 kcal/mol), and SARS-CoV-2/ACE2 complex receptor (−8.1 kcal/mol). Moreover, after molecular docking calculations, top-scoring ligand-receptor complexes of the aminopterin with SARS-CoV-2 enzymes (6M03 and 6M0J) were subjected to 50 ns all-atom MD simulations to investigate the ligand-receptor interactions in more detail, and to determine the binding free energies accurately. The predicted results indicate that the aminopterin may significantly inhibit SARS-CoV-2 infection. Thus, in this study, as both anticancer and anti-COVID-19 agents, the versatility of the biological activity of aminopterin was shown. Communicated by Ramaswamy H. Sarma

Synthesis, crystal structure, static and dynamic properties, molecular structure, reactive sites, wavefunction and molecular docking of 1-(3-((4-(diethylamino)-2-hydroxybenzylidene)amino)phenyl)ethan-1-one

The Schiff base compound1-(3-((4-(diethylamino)-2-hydroxybenzylidene)amino)phenyl)ethan-1-one (34D2HA) has been synthesized and characterized by FT-IR, XRD techniques. The title compound was optimized. From the calculated perspective, the title compound's Natural bond orbital, Molecular Electrostatic Potential, Electron Localization Function, Localized-Orbital Locator, Frontier Molecular Orbital, Mulliken charges and Fukui function properties were also examined. The vibrational analysis of 34D2HA were investigated by means of computational calculations. The intensity of vibrational bands and the fundamental frequencies were evaluated by the standard B3LYP method on the basis set of 6–311G(d,p) combination with diffusion function + + are done by density functional theory (DFT). In addition to PED, VEDA was utilized to predict vibrational assignment. The absorptions were analyzed theoretically using UV–Vis spectra with different solvents. The experimental and computed bond parameters exhibit a strong agreement. The infrared frequency calculations lead overall agreement with observed spectral patterns. Beside, ADME studies have been carried out and this compound satisfies with Lipinski's rule of five. The results of protein- ligand docking using the Auto Dock method with various proteins suggest that the molecule has pharmaceutical properties against receptors.

Novel applications of generative adversarial networks (GANs) in the analysis of ultrafast electron diffraction (UED) images.

Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper, we present an attempt to tackle this problem using machine learning. Although most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network trained on synthetic data only. We validate this approach on ultrafast electron diffraction data of bismuth samples undergoing thermalization upon excitation via 800nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe that employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples.

Single-molecule imaging of genome maintenance proteins encountering specific DNA sequences and structures

DNA repair pathways are tightly regulated processes that recognize specific hallmarks of DNA damage and coordinate lesion repair through discrete mechanisms, all within the context of a three-dimensional chromatin landscape. Dysregulation or malfunction of any one of the protein constituents in these pathways can contribute to aging and a variety of diseases. While the collective action of these many proteins is what drives DNA repair on the organismal scale, it is the interactions between individual proteins and DNA that facilitate each step of these pathways. In much the same way that ensemble biochemical techniques have characterized the various steps of DNA repair pathways, single-molecule imaging (SMI) approaches zoom in further, characterizing the individual protein-DNA interactions that compose each pathway step. SMI techniques offer the high resolving power needed to characterize the molecular structure and functional dynamics of individual biological interactions on the nanoscale. In this review, we highlight how our lab has used SMI techniques – traditional atomic force microscopy (AFM) imaging in air, high-speed AFM (HS-AFM) in liquids, and the DNA tightrope assay – over the past decade to study protein-nucleic acid interactions involved in DNA repair, mitochondrial DNA replication, and telomere maintenance. We discuss how DNA substrates containing specific DNA sequences or structures that emulate DNA repair intermediates or telomeres were generated and validated. For each highlighted project, we discuss novel findings made possible by the spatial and temporal resolution offered by these SMI techniques and unique DNA substrates.

Generation of Large-Bandwidth High-Power X-Ray Free-Electron-Laser Pulses Using a Hollow-Channel Plasma

Large-bandwidth x-ray free-electron-laser (XFEL) facilities are desirable scientific tools in various fields, such as molecular structural dynamics and spectroscopy diagnosis. Various methods are proposed to broaden the FEL spectra. Here, a method is proposed to generate high-power XFEL radiation of tunable spectral bandwidth using plasma wakefield acceleration. An ultrabroad bandwidth is achieved by chirping the electron beam in a hollow-channel plasma without noticeable slice-beam-quality degradation. A dedicated beamline can match the beams with a large energy chirp in the undulators almost without beam loss. Numerical simulations demonstrate that a relative spectral bandwidth (full width) of up to 24% can be obtained with optimized beam and plasma parameters.

Relationships between Molecular Structural Order Parameters and Equilibrium Water Dynamics in Aqueous Mixtures

Water's unique thermophysical properties and how it mediates aqueous interactions between solutes have long been interpreted in terms of its collective molecular structure. The seminal work of Errington and Debenedetti [Nature 2001, 409, 318-321] revealed a striking hierarchy of relationships among the thermodynamic, dynamic, and structural properties of water, motivating many efforts to understand (1) what measures of water structure are connected to different experimentally accessible macroscopic responses and (2) how many such structural metrics are adequate to describe the collective structural behavior of water. Diffusivity constitutes a particularly interesting experimentally accessible equilibrium property to investigate such relationships because advanced NMR techniques allow the measurement of bulk and local water dynamics in nanometer proximity to molecules and interfaces, suggesting the enticing possibility of measuring local diffusivities that report on water structure. Here, we apply statistical learning methods to discover persistent structure-dynamic correlations across a variety of simulated aqueous mixtures, from alcohol-water to polypeptoid-water systems. We investigate a variety of molecular water structure metrics and find that an unsupervised statistical learning algorithm (namely, sequential feature selection) identifies only two or three independent structural metrics that are sufficient to predict water self-diffusivity accurately. Surprisingly, the translational diffusivity of water across all mixed systems studied here is strongly correlated with a measure of tetrahedral order given by water's triplet angle distribution. We also identify a separate small number of structural metrics that well predict an important thermodynamic property, the excess chemical potential of an idealized methane-sized hydrophobe in water. Ultimately, we offer a Bayesian method of inferring water structure by using only structure-dynamics linear regression models with experimental Overhauser dynamic nuclear polarization (ODNP) measurements of water self-diffusivity. This study thus quantifies the relationships among several distinct structural order parameters in water and, through statistical learning, reveals the potential to leverage molecular structure to predict fundamental thermophysical properties. In turn, these findings suggest a framework for solving the inverse problem of inferring water's molecular structure using experimental measurements such as ODNP studies that probe local water properties.