Advanced Corrections of Hydrogen Bonding and Dispersion for Semiempirical Quantum Mechanical Methods

Nature-Inspired Gallinamides Are Potent Antischistosomal Agents: Inhibition of the Cathepsin B1 Protease Target and Binding Mode Analysis

Schistosomiasis, caused by a parasitic blood fluke of the genus Schistosoma, is a global health problem for which new chemotherapeutic options are needed. We explored the scaffold of gallinamide A, a natural peptidic metabolite of marine cyanobacteria that has previously been shown to inhibit cathepsin L-type proteases. We screened a library of 19 synthetic gallinamide A analogs and identified nanomolar inhibitors of the cathepsin B-type protease SmCB1, which is a drug target for the treatment of schistosomiasis mansoni. Against cultured S. mansoni schistosomula and adult worms, many of the gallinamides generated a range of deleterious phenotypic responses. Imaging with a fluorescent-activity-based probe derived from gallinamide A demonstrated that SmCB1 is the primary target for gallinamides in the parasite. Furthermore, we solved the high-resolution crystal structures of SmCB1 in complex with gallinamide A and its two analogs and describe the acrylamide covalent warhead and binding mode in the active site. Quantum chemical calculations evaluated the contribution of individual positions in the peptidomimetic scaffold to the inhibition of the target and demonstrated the importance of the P1' and P2 positions. Our study introduces gallinamides as a powerful chemotype that can be exploited for the development of novel antischistosomal chemotherapeutics.

Stepping-Stone CBH: Benchmark and Application of a Multilayered Isodesmic-Based Correction Scheme

We present a generalization of the connectivity-based hierarchy (CBH) of isodesmic-based correction schemes to a multilayered fragmentation platform for overall cost reduction while retaining high accuracy. The newly developed multilayered CBH approach, called stepping-stone CBH (SSCBH), is benchmarked on a diverse set of 959 medium-sized organic molecules. Applying SSCBH corrections to the PBEh-D3 density functional resulted in an average error of 0.76 kcal/mol for the full test set compared to accurate CCSD(T)-quality enthalpies and an even lower error of 0.44 kcal/mol on a subset containing only acyclic molecules. These results rival the traditional CBH-3 approach at a greatly reduced cost, allowing larger fragment corrections to be made at the MP2 level of theory rather than with G4. Our SSCBH approach will enable more widespread applications of CBH methods to a broader range of organic and biomolecular systems.

Modeling Zinc Complexes Using Neural Networks

Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build potential energy surfaces and explore chemical space. However, most of this work has focused on organic molecules due to the simplicity of their electronic structures as well as the availability of data sets. In this work, we build a deep learning architecture to model the energetics of zinc organometallic complexes. To achieve this, we have compiled a configurationally and conformationally diverse data set of zinc complexes using metadynamics to overcome the limitations of traditional sampling methods. In terms of the neural network potentials, our results indicate that for zinc complexes, partial charges play an important role in modeling the long-range interactions with a neural network. Our developed model outperforms semiempirical methods in predicting the relative energy of zinc conformers, yielding a mean absolute error (MAE) of 1.32 kcal/mol with reference to the double-hybrid PWPB95 method.

Closed-loop recyclability of a biomass-derived epoxy-amine thermoset by methanolysis

Epoxy resin thermosets (ERTs) are an important class of polymeric materials. However, owing to their highly cross-linked nature, they suffer from poor recyclability, which contributes to an unacceptable level of environmental pollution. There is a clear need for the design of inherently recyclable ERTs that are based on renewable resources. We present the synthesis and closed-loop recycling of a fully lignocellulose-derivable epoxy resin (DGF/MBCA), prepared from dimethyl ester of 2,5-furandicarboxylic acid (DMFD), 4,4'-methylenebis(cyclohexylamine) (MBCA), and glycidol, which displays excellent thermomechanical properties (a glass transition temperature of 170°C, and a storage modulus at 25°C of 1.2 gigapascals). Notably, the material undergoes methanolysis in the absence of any catalyst, regenerating 90% of the original DMFD. The diamine MBCA and glycidol can subsequently be reformed by acetolysis. Application and recycling of DGF/MBCA in glass and plant fiber composites are demonstrated.

Modeling Fe(II) Complexes Using Neural Networks

We report a Fe(II) data set of more than 23000 conformers in both low-spin (LS) and high-spin (HS) states. This data set was generated to develop a neural network model that is capable of predicting the energy and the energy splitting as a function of the conformation of a Fe(II) organometallic complex. In order to achieve this, we propose a type of scaled electronic embedding to cover the long-range interactions implicitly in our neural network describing the Fe(II) organometallic complexes. For the total energy prediction, the lowest MAE is 0.037 eV, while the lowest MAE of the splitting energy is 0.030 eV. Compared to baseline models, which only incorporate short-range interactions, our scaled electronic embeddings improve the accuracy by over 70% for the prediction of the total energy and the splitting energy. With regard to semiempirical methods, our proposed models reduce the MAE, with respect to these methods, by 2 orders of magnitude.

Comprehensive Analysis of Methyl-β-D-ribofuranoside: A Multifaceted Spectroscopic and Theoretical Approach

This study presents a comprehensive analysis of the vibrational spectra of methyl-β-D-ribofuranoside. Employing a combination of inelastic neutron scattering, Raman, and infrared spectroscopy allows for the observation of all modes regardless of the selection rules. The experimental techniques were complemented by density functional theory computational methods using both gas-phase (Gaussian) and solid-state (CRYSTAL, CASTEP) approaches to provide an unambiguous assignment of the defining vibrational features. Two distinct structures of the molecule were identified in the unit cell, differentiated mainly by the orientation of the furanose ring O-H bonds. The low-energy region of the spectrum (<400 cm-1) is dominated by lattice vibrations and functional group rotation, while the midenergy region is dominated by out-of-plane bending motions of the furanose ring (400-900 cm-1) and by C-H bending in the methyl and methylene groups (1400-1600 cm-1). The high-energy region (>2800 cm-1) encompasses the C-H and O-H stretching modes and offers convincing evidence of at least one H-bonding interaction between the two structures of methyl-β-D-ribofuranoside.

CREST-A program for the exploration of low-energy molecular chemical space

Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes

Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Target-ligand binding affinity from single point enthalpy calculation and elemental composition

Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.

Benchmark Data Set of Crystalline Organic Semiconductors

This work reports a Benchmark Data set of Crystalline Organic Semiconductors to test calculations of the structural and electronic properties of these materials in the solid state. The data set contains 67 crystals consisting of mostly rigid molecules with a single dominant conformer, covering the majority of known structural types. The experimental crystal structure is available for the entire data set, whereas zero-temperature unit cell volume can be reliably estimated for a subset of 28 crystals. Using this subset, we benchmark r2SCAN-D3 and PBE-D3 density functionals. Then, for the entire data set, we benchmark approximate density functional theory (DFT) methods, including GFN1-xTB and DFTB3(3ob-3-1), with various dispersion corrections against r2SCAN-D3. Our results show that r2SCAN-D3 geometries are accurate within a few percent, which is comparable to the statistical uncertainty of experimental data at a fixed temperature, but the unit cell volume is systematically underestimated by 2% on average. The several times faster PBE-D3 provides an unbiased estimate of the volume for all systems except for molecules with highly polar bonds, for which the volume is substantially overestimated in correlation with the underestimation of atomic charges. Considered approximate DFT methods are orders of magnitude faster and provide qualitatively correct but overcompressed crystal structures unless the dispersion corrections are fitted by unit cell volume.

Photo-Activating Biomimetic Polyoxomolybdate for Boosting Oxygen Evolution in Neutral Electrolytes

Inspired by the metal-oxo cluster structural feature and charge separation behaviour of the oxygen evolving center (OEC) in photosystem II (PS-II) under photoirradiation, a new crystalline photochromic polyoxomolybdate, MV2 [β-Mo8 O26 ] (1, MV=methyl viologen cation), is designed as a biomimetic oxygen evolution reaction (OER) catalyst in neutral electrolytes. After photoinduced electron transfer (PIET) with colour change from colourless to grey, it remains in an ultra-stable charge-separated state over a year under ambient conditions. The observed overpotential at 10 mA ⋅ cm-2 and Tafel slope decrease by 49 mV and 62.8 mV ⋅ dec-1 after coloration, respectively. The outstanding OER performance of the coloured state in neutral electrolytes even outperforms the commercial RuO2 benchmark. Experimental and theoretical studies show that oxygen holes within polyanions after irradiation serve as sites for enhancing direct O-O coupling, thus effectively promoting OER. This is the first successful application of electron-transfer photochromism to realize OER activity gain.

Exploring the Influence of P3HT on PTCA Crystallization and Phase Behavior in Thin Films

The thermal properties and alignment of crystallinity of materials in thin films play crucial roles in the performance and reliability of various devices, especially in the fields of electronics, materials science, and engineering. The slight variations in the molecular packing of the active layer can make considerable differences in the optical and thermal properties. Herein, we aim to investigate the tuning of the physical properties of a blended thin film of n-type small organic molecules of perylene-3,4,9,10-tetracarboxylic acid (PTCA-SMs) with the mixing of the p-type polymer poly(3-hexylthiophene) (P3HT). The resulting thin films exhibit an enhanced surface crystallinity compared to the pristine material, leading to the formation of long crystallites, and these crystallites are thermally stable in the solid state, as confirmed by X-ray diffraction (XRD), atomic force microscopy (AFM), and thermal analysis using variable-temperature spectroscopic ellipsometry (VTSE) and differential scanning calorimetry (DSC). We believe that the crystalline structure of the obtained P3HT/PTCA-SMs blends is a combination of edge-on and face-on orientations, which enable the potential use of this material as an active layer in organic electronics.

Solvent and Substituent Size Influence on the Cyclochiral Rigidity of Aminomethylene Derivatives of Resorcin[4]arene

Resorcin[4]arenes (R[4]A) are a group of macrocyclic compounds whose peculiar feature is the presence of eight hydroxyl groups in their structure. The directional formation of intramolecular hydrogen bonds with their participation leads to the formation of a cyclochiral racemic mixture of these compounds. Their stability strongly depends on the substituent and especially the environment in which they are located. The paper discusses the cyclochiral nature of aminomethylene derivatives of R[4]A (AMD-R[4]A). Their cyclochiral rigidity in non-polar solvents has been shown. The influence of the size of the alkyl groups in the amino substituents of AMD-R[4]A on their cyclochiral nature was noted. To calculate the reaction paths for their racemization, the nudged elastic band (NEB) method was employed using the semi-empirical DFT (GFN1-xTB) approach. The calculated activation barrier energies for their racemization in chloroform, obtained through various semi-empirical quantum chemical methods (SE), Hartree-Fock (HF), and density functionals theory (DFT), show good correlation with experimental observations. Among the tested methods, the B38LYP-D4 method is highly recommended due to its fast computational speed and accuracy, which is comparable to the time-consuming double-hybrid DH-revDSD-PBEP86 approach.

Screening of the antileishmanial and antiplasmodial potential of synthetic 2-arylquinoline analogs

In this study, six analogs of 2-arylquinoline were synthesized and evaluated for their in vitro and in vivo antiplasmodial and leishmanicidal activity. At a later stage, hemolytic activity and druggability were tested in vitro and in silico, respectively, observing as a result: firstly, compounds showed half-maximal effective concentration (EC50) values between 3.6 and 19.3 µM. Likewise, a treatment using the compounds 4a-f caused improvement in most of the treated hamsters and cured some of them. Regarding the antiplasmodial activity, the compounds showed moderate to high activity, although they did not show hemolytic activity. Furthermore, 4e and 4f compounds were not able to control P. berghei infection when administered to animal models. Molecular dynamic simulations, molecular docking and ligand binding affinity indicate good characteristics of the studied compounds, which are expected to be active. And lastly, the compounds are absorbable at the hematoencephalic barrier but not in the gastrointestinal tract. In summary, ADMET properties suggest that these molecules may be used as a safe treatment against Leishmania.

High-throughput screening of spin states for transition metal complexes with spin-polarized extended tight-binding methods

The semiempirical GFNn-xTB ( n = 1 , 2 ) tight-binding methods are extended with a spin-dependent energy term (spin-polarization), enabling the fast and efficient screening of different spin states for transition metal complexes. While GFNn-xTB methods inherently can not differentiate properly between high-spin (HS) and low-spin (LS) states, this shortcoming is corrected with the presented methods termed spGFNn-xTB. The performance of spGFNn-xTB methods for spin state energy splittings is evaluated on a newly compiled benchmark set of 90 complexes (27 HS and 63 LS complexes) containing 3d, 4d, and 5d transition metals (termed TM90S) employing DFT references at the TPSSh-D4/def2-QZVPP level of theory. The challenging TM90S set contains complexes with charges between - 4 and +3, spin multiplicities between 1 and 6, and spin-splitting energies that range from - 47.8 to 146.6 kcal/mol with a mean average of 32.2 kcal/mol. On this set the (sp)GFNn-xTB methods, the PM6-D3H4 method, and the PM7 method are evaluated with spGFN1-xTB yielding the lowest MAD of 19.6 kcal/mol followed by spGFN2-xTB with 24.8 kcal/mol. While for the 4d and 5d subsets small or no improvements are observed with spin-polarization, large improvements are obtained for the 3d subset with spGFN1-xTB yielding the smallest MAD of 14.2 kcal/mol followed by spGFN2-xTB with 17.9 kcal/mol and PM6-D3H4 with 28.4 kcal/mol. The correct sign of the spin state splittings is obtained with spGFN2-xTB in 89% of all cases closely followed by spGFN1-xTB with 88%. On the full set, a pure semiempirical vertical spGFN2-xTB//GFN2-xTB-based workflow for screening purposes yields a slightly better MAD of 22.2 kcal/mol due to error compensation, while being qualitative correct for one additional case. In combination with their low computational cost (scanning spin states in seconds), the spGFNn-xTB methods represent robust tools for pre-screening steps of spin state calculations and high-throughput workflows.

Hexahelicene DNA-binding: Minor groove selectivity, semi-intercalation and chiral recognition

In this contribution, we focused on a fundamental study targeting the interaction of water-soluble [6]helicene derivative 1 (1-butyl-3-(2-methyl[6]helicenyl)-imidazolium bromide) with double-stranded (ds) DNA. A synthetic 30-base pair duplex, plasmid, chromosomal calf thymus and salmon DNA were investigated using electrochemistry, electrophoresis and spectroscopic tools supported by molecular dynamics (MD) and quantum mechanical approaches. Both experimental and theoretical work revealed the minor groove binding of 1 to the dsDNA. Both the positively charged imidazole ring and hydrophobic part of the side chain contributed to the accommodation of 1 into the dsDNA structure. Neither intercalation into the duplex DNA nor the stable binding of 1 to single-stranded DNA were found in topoisomerase relaxation experiments with structural components of 1, i.e. [6]helicene (2) and 1-butyl-3-methylimidazolium bromide (3), nor by theoretical calculations. Finally, the binding of optically pure enantiomers (P)-1 and (M)-1 was studied using circular dichroism spectroscopy, isothermal titration calorimetry and UV Resonance Raman (UVRR) methods. Using MD and quantum mechanical methods, minor groove and semi-intercalation were proposed for compound 1 as the predominant binding modes. From the UVRR findings, we also can conclude that 1 tends to preferentially interact with adenine and guanine residues in the structure of dsDNA.

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

The Back Door to the Surface Hydrated Electron

We use a Mg+ metal to extend the size regime of aqueous clusters to extrapolate to the bulk limit of the vertical detachment energy (VDE) of the solvated electron to >3,200, a value between 1 to over 2 orders of magnitude larger than the one previously measured experimentally or computed theoretically. We relate the VDE to the energy difference between the Mg+(H2O)n and Mg2+(H2O)n systems and the metal's second ionization potential. The extrapolated bulk VDEs of the localized surface electron, which moves away from the metal as n increases, are 1.89 ± 0.01 eV for semiempirical (n ∼ 3,200; PM6-D3H4) and 1.73 ± 0.03 eV (n ∼ 150; HF) and 1.83 ± 0.02 eV (n ∼ 150; MP2) for ab initio, in excellent agreement with the 1.6-1.8 eV range of experimental results. The VDEs converge from above (larger values) to the bulk limit, in a manner that is qualitatively opposite from previous studies and experiments lacking a charged metal, a fact justifying the "back door" approach to the solvated electron.

When catchers meet - a computational study on the dimerization of the Buckycatcher

We study the dimerization of the buckycatcher in gas phase and in toluene. We created an extensive library of 36 different complexes, which were characterized at semi-empirical and DFT levels. Semi-empirical geometries and dimerization energies compare well against reference data or Density Functional Theory calculations we performed. Born-Oppenheimer molecular dynamics was used to understand what happens when two molecules of the buckycatcher meet, allowing us to infer on the lack of kinetic barriers when dimers form. Thermodynamically, it is possible that room temperature solutions contain dimerized buckycatcher. Using a very simple exchange model, it is shown, however, that dimerization cannot compete thermodynamically against complexation with fullerenes, which accounts for experimental observations.

Selective binding of oxalate by a tris-ureido calix[6]tube in a protic environment

Due to their significant role in industry and biological systems, the interest in selectively recognizing and detecting small dicarboxylates has grown in recent years. In this study, we report on the binding properties of a family of tubular-shaped heterotritopic receptors based on bis-calix[6]arenes, which contain three (thio)urea bridges (C3U and C3TU) or six urea bridges (C6U), toward dicarboxylates. While poor binding properties were observed by NMR for the newly synthesized C6U, receptors C3U and C3TU exhibited a unique ability to cooperatively complex a dicarboxylate anion sandwiched between two ammonium ions. The three ions are complexed in contact and aligned within the tubular shape of the receptor, forming cascade complexes that are stable even in a competitive environment. The different binding properties between the receptors were rationalized in terms of size, flexibility, H-bond donor ability, and intramolecular H-bonding within the anion binding pocket between the calixarene cavities. With C3U, a rare selectivity for oxalate over other small dicarboxylates and various bicharged anions was observed. Molecular modeling of the cascade complex indicated that the oxalate anion is stabilized by an array of hydrogen bonds with the urea bridges of the receptor and both propylammonium cations nested within the calixarene cavities.

A semiempirical method optimized for modeling proteins

CONTEXT

In recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.

METHODS

Two alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly.

Antispasmodic Effect of Valeriana pilosa Root Essential Oil and Potential Mechanisms of Action: Ex Vivo and In Silico Studies

Infusions of Valeriana pilosa are commonly used in Peruvian folk medicine for treating gastrointestinal disorders. This study aimed to investigate the spasmolytic and antispasmodic effects of Valeriana pilosa essential oil (VPEO) on rat ileum. The basal tone of ileal sections decreased in response to accumulative concentrations of VPEO. Moreover, ileal sections precontracted with acetylcholine (ACh), potassium chloride (KCl), or barium chloride (BaCl2) were relaxed in response to VPEO by a mechanism that depended on atropine, hyoscine butylbromide, solifenacin, and verapamil, but not glibenclamide. The results showed that VPEO produced a relaxant effect by inhibiting muscarinic receptors and blocking calcium channels, with no apparent effect on the opening of potassium channels. In addition, molecular docking was employed to evaluate VPEO constituents that could inhibit intestinal contractile activity. The study showed that α-cubebene, β-patchoulene, β-bourbonene, β-caryophyllene, α-guaiene, γ-muurolene, valencene, eremophyllene, and δ-cadinene displayed the highest docking scores on muscarinic acetylcholine receptors and voltage-gated calcium channels, which may antagonize M2 and/or M3 muscarinic acetylcholine receptors and block voltage-gated calcium channels. In summary, VPEO has both spasmolytic and antispasmodic effects. It may block muscarinic receptors and calcium channels, thus providing a scientific basis for its traditional use for gastrointestinal disorders.

Discovery of New 2-Phenylamino-3-acyl-1,4-naphthoquinones as Inhibitors of Cancer Cells Proliferation: Searching for Intra-Cellular Targets Playing a Role in Cancer Cells Survival

A series of 2-phenylamino-3-acyl-1,4-naphtoquinones were evaluated regarding their in vitro antiproliferative activities using DU-145, MCF-7 and T24 cancer cells. Such activities were discussed in terms of molecular descriptors such as half-wave potentials, hydrophobicity and molar refractivity. Compounds 4 and 11 displayed the highest antiproliferative activity against the three cancer cells and were therefore further investigated. The in silico prediction of drug likeness, using pkCSM and SwissADME explorer online, shows that compound 11 is a suitable lead molecule to be developed. Moreover, the expressions of key genes were studied in DU-145 cancer cells. They include genes involved in apoptosis (Bcl-2), tumor metabolism regulation (mTOR), redox homeostasis (GSR), cell cycle regulation (CDC25A), cell cycle progression (TP53), epigenetic (HDAC4), cell-cell communication (CCN2) and inflammatory pathways (TNF). Compound 11 displays an interesting profile because among these genes, mTOR was significantly less expressed as compared to control conditions. Molecular docking shows that compound 11 has good affinity with mTOR, unraveling a potential inhibitory effect on this protein. Due to the key role of mTOR on tumor metabolism, we suggest that impaired DU-145 cells proliferation by compound 11 is caused by a reduced mTOR expression (less mTOR protein) and inhibitory activity on mTOR protein.

Characterization of Thymoquinone-Sulfobutylether-β-Cyclodextrin Inclusion Complex for Anticancer Applications

Thymoquinone (TQ) is a quinone derived from the black seed Nigella sativa and has been extensively studied in pharmaceutical and nutraceutical research due to its therapeutic potential and pharmacological properties. Although the chemopreventive and potential anticancer effects of TQ have been reported, its limited solubility and poor delivery remain the major limitations. In this study, we aimed to characterize the inclusion complexes of TQ with Sulfobutylether-β-cyclodextrin (SBE-β-CD) at four different temperatures (293-318 K). Additionally, we compared the antiproliferative activity of TQ alone to TQ complexed with SBE-β-CD on six different cancer cell lines, including colon, breast, and liver cancer cells (HCT-116, HT-29, MDA-MB-231, MCF-7, SK-BR-3, and HepG2), using an MTT assay. We calculated the thermodynamic parameters (ΔH, ΔS, and ΔG) using the van't Holf equation. The inclusion complexes were characterized by X-ray diffraction (XRD), Fourier transforms infrared (FT-IR), and molecular dynamics using the PM6 model. Our findings revealed that the solubility of TQ was improved by ≥60 folds, allowing TQ to penetrate completely into the cavity of SBE-β-CD. The IC₅₀ values of TQ/SBE-β-CD ranged from 0.1 ± 0.01 µg/mL against SK-BR-3 human breast cancer cells to 1.2 ± 0.16 µg/mL against HCT-116 human colorectal cancer cells, depending on the cell line. In comparison, the IC₅₀ values of TQ alone ranged from 0.2 ± 0.01 µg/mL to 4.7 ± 0.21 µg/mL. Overall, our results suggest that SBE-β-CD can enhance the anticancer effect of TQ by increasing its solubility and bioavailability and cellular uptake. However, further studies are necessary to fully understand the underlying mechanisms and potential side effects of using SBE-β-CD as a drug delivery system for TQ.

Delivery Systems in Ocular Retinopathies: The Promising Future of Intravitreal Hydrogels as Sustained-Release Scaffolds

Slow-release delivery systems are needed to ensure long-term sustained treatments for retinal diseases such as age-related macular degeneration and diabetic retinopathy, which are currently treated with anti-angiogenic agents that require frequent intraocular injections. These can cause serious co-morbidities for the patients and are far from providing the adequate drug/protein release rates and required pharmacokinetics to sustain prolonged efficacy. This review focuses on the use of hydrogels, particularly on temperature-responsive hydrogels as delivery vehicles for the intravitreal injection of retinal therapies, their advantages and disadvantages for intraocular administration, and the current advances in their use to treat retinal diseases.

A Buckycatcher in Solution-A Computational Perspective

In this work, we study the buckycatcher (C₆₀H₂₈) in solution using quantum chemical models. We investigate the conformational equilibria in several media and the effects that molecules of solvent might have in interconversion barriers between the different conformers. These are studied in a hypothetical gas phase, in the dielectric of a solvent, as well as with hybrid solvation. In the latter case, due to a disruption of π-stacking interactions, the transition states are destabilized. We also evaluate the complexation of the buckycatcher with solvent-like molecules. In most cases studied, there should be no adducts formed because the enthalpy driving force cannot overcome entropic penalties.

Electronic structure theory on modeling short-range noncovalent interactions between amino acids

While short-range noncovalent interactions (NCIs) are proving to be of importance in many chemical and biological systems, these atypical bindings happen within the so-called van der Waals envelope and pose an enormous challenge for current computational methods. We introduce SNCIAA, a database of 723 benchmark interaction energies of short-range noncovalent interactions between neutral/charged amino acids originated from protein x-ray crystal structures at the "gold standard" coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory with a mean absolute binding uncertainty less than 0.1 kcal/mol. Subsequently, a systematic assessment of commonly used computational methods, such as the second-order Møller-Plesset theory (MP2), density functional theory (DFT), symmetry-adapted perturbation theory (SAPT), composite electronic-structure methods, semiempirical approaches, and the physical-based potentials with machine learning (IPML) on SNCIAA is carried out. It is shown that the inclusion of dispersion corrections is essential even though these dimers are dominated by electrostatics, such as hydrogen bondings and salt bridges. Overall, MP2, ωB97M-V, and B3LYP+D4 turned out to be the most reliable methods for the description of short-range NCIs even in strongly attractive/repulsive complexes. SAPT is also recommended in describing short-range NCIs only if the δMP2 correction has been included. The good performance of IPML for dimers at close-equilibrium and long-range conditions is not transferable to the short-range. We expect that SNCIAA will assist the development/improvement/validation of computational methods, such as DFT, force-fields, and ML models, in describing NCIs across entire potential energy surfaces (short-, intermediate-, and long-range NCIs) on the same footing.

Antioxidant activity and enzymatic of lichen substances: A study based on cyclic voltammetry and theoretical

The antioxidant activity of nine lichen substances, including methylatrarate (1), methyl haematommate (2), lobaric acid (3), fumarprotocetraric acid (4), sphaerophorin (5), subsphaeric acid (6), diffractaic acid (7), barbatolic acid (8) and salazinic acid (9) has been determined through cyclic voltammetry. The compounds 1-4 presented slopes close to the Nernst constant of 0.059 V, indicating a 2H⁺/2e^- relation between protons and electrons, as long as the compounds 5, 6, 7, 8, and 9 present slopes between 0.037 V and 0.032 V, indicating a 1H⁺/2e^- relation between protons and electrons. These results show a high free radical scavenging activity by means of the release of H⁺, suggesting an important antioxidant capacity of these molecules. Theoretical calculations of hydrogen bond dissociation enthalpies (BDE), proton affinities (PA), and Proton Transfer (PT) mechanisms, at M06-2x/6-311+G(d,p) level complement the experimental results. Computations support that the best antioxidant activity is obtained for the molecules (3, 4, 5, 6, 7 and 8), that have a carboxylic acid group close to a phenolic hydroxyl group, through hydrogen atomic transfer (HAT) and sequential proton loss electron transfer (SPLET) mechanisms. Additional computations were performed for modelling binding affinity of the lichen substances with CYPs enzymes, mainly CYP1A2, CYP51, and CYP2C9*2 isoforms, showing strong affinity for all the compounds described in this study.

Reparameterization of the chemical-potential equalization model with DFTB3: A practical balance between accuracy and transferability

To improve the performance of the third-order density-functional tight-binding method (DFTB3) for non-covalent interactions involving organic and biological molecules, a chemical-potential equalization (CPE) approach was introduced [J. Phys. Chem. A, 116, 9131 (2012)] and parameterized for the H, C, N, O, and S chemical elements [J. Chem. Phys., 143, 084123 (2015)]. Based largely on equilibrium structures, the parameterized DFTB3/CPE models were shown to exhibit improvements in molecular polarizabilities and intermolecular interactions. With more extensive analyses, however, we observe here that the available DFTB3/CPE models have two critical limitations: (1) they lead to sharply varying potential energy surfaces, thus causing numerical instability in molecular dynamics (MD) simulations, and (2) they lead to spurious interactions at short distances for some dimer complexes. These shortcomings are attributed to the employed screening functions and the overfitting of CPE parameters. In this work, we introduce a new strategy to simplify the parameterization procedure and significantly reduce free parameters down to four global (i.e., independent of element type) ones. With this strategy, two new models, DFTB3/CPE(r) and DFTB3/CPE(r†) are parameterized. The new models lead to smooth potential energy surfaces, stable MD simulations, and alleviate the spurious interactions at short distances, thus representing consistent improvements for both neutral and ionic hydrogen bonds.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

In situ ligand restraints from quantum-mechanical methods

In macromolecular crystallographic structure refinement, ligands present challenges for the generation of geometric restraints due to their large chemical variability, their possible novel nature and their specific interaction with the binding pocket of the protein. Quantum-mechanical approaches are useful for providing accurate ligand geometries, but can be plagued by the number of minima in flexible molecules. In an effort to avoid these issues, the Quantum Mechanical Restraints (QMR) procedure optimizes the ligand geometry in situ, thus accounting for the influence of the macromolecule on the local energy minima of the ligand. The optimized ligand geometry is used to generate target values for geometric restraints during the crystallographic refinement. As demonstrated using a sample of >2330 ligand instances in >1700 protein-ligand models, QMR restraints generally result in lower deviations from the target stereochemistry compared with conventionally generated restraints. In particular, the QMR approach provides accurate torsion restraints for ligands and other entities.

Computational study of ground-state properties of μ2 -bridged group 14 porphyrinic sandwich complexes

The structural properties of μ₂ -bridged porphyrinic double-decker complexes are investigated and the influence of various ligands, metals, substituents, and bridging atoms on the dominant structural motif is elucidated. A variety of quantum chemical methods including semiempirical (SQM) methods and density functional theory (DFT) is assessed for the calculation of ecliptic and staggered conformational energies. Local coupled cluster (DLPNO-CCSD(T1)) data are generated for reference. The r² SCAN-3c composite scheme as well as the B2PLYP-D4/def2-QZVPP approach are identified as reliable methods. Energy decomposition analyses (EDA) and localized molecular orbital analyses (LMO) are used to investigate the bonding situation and the nature of the inter-ligand interaction energy underlining the crucial role of attractive London dispersion interactions. Targeted modification of the bridging atom, e.g., by replacing O^2- by S^2- is shown to drastically change the major structural features of the investigated complexes. Further, the influence of different substituents of varying size at the phthalocyanine ligand regarding the dominant conformation is described.

How well do semiempirical QM methods describe the structure of proteins?

Semiempirical quantum-mechanical (QM) computational methods are an increasingly popular tool for the study of biomolecular systems. They were, however, developed and tested mostly on small model molecules. In this work, we explore one topic fundamental to these applications: the ability of the methods to describe the structure of proteins. In a set of 19 proteins for which a crystal structure with very high resolution is available, we analyze the properties of the protein geometries optimized using several semiempirical QM methods including PM6-D3H4, PM7, and GFN2-xTB. Some of the methods provide a very good description of the general structural features of the protein, yielding results better than or comparable to the AMBER ff03 force field. However, PM7 and PM6-D3H4 optimizations introduce artificial close contacts in the structure, which is partially remediated by reparameterization.

A specific MNDO parameterization for water

A modified neglect of differential overlap has been parameterized specifically for water and its oligomers with the addition of polarization functions on both hydrogen and oxygen, Feynman dispersion, and a slight modification of the treatment of the hydrogen nucleus. The results show that it is possible to easily obtain good geometries and energies for hydrogen-bonded water aggregates. Data from the Benchmark Energy and Geometry Database water-cluster database were used to parameterize the new Hamiltonian for water clusters from the dimer to the decamer using MP2/aug-cc-pVDZ optimized geometries and CCSD(T)/CBS oligomerization energies. Seventy five oligomerization and rearrangement energies derived from the parameterization data are reproduced with a root mean-square error (RMSE) of 0.79 kcal mol^-1 and the geometries of 38 oligomers with an RMSE of 0.17 Å. Interestingly, the Feynman dispersion term adopts a role different from that intended and tunes the atomic polarizability. The implications of these results in terms of future dedicated neglect of diatomic differential overlap Hamiltonians and those that use force-field-like atom types are discussed.

Synthesis of conjugates of (aR,7S)-colchicine with monoterpenoids and investigation of their biological activity

Conjugates of the natural alkaloid (aR,7S)-colchicine with bicyclic monoterpenoids and their derivatives were synthesized for the first time. Molecular docking of the synthesized agents in the active site of the main viral protease of the SARS-CoV-2 virus was carried out. The cytotoxic properties of the agents against different cell lines and the ability to inhibit the main viral protease 3CLPro were studied.

Efficient Computation of the Interaction Energies of Very Large Non-covalently Bound Complexes

We present a new benchmark set consisting of 16 large non-covalently bound systems (LNCI16) ranging from 380 up to 1988 atoms and featuring diverse interaction motives. Gas-phase interaction energies are calculated with various composite DFT, semi-empirical quantum mechanical (SQM), and force field (FF) methods and are evaluated using accurate DFT reference values. Of the employed QM methods, PBEh-3c proves to be the most robust for large systems with a relative mean absolute deviation (relMAD) of 8.5% with respect to the reference interaction energies. r2SCAN-3c yields an even smaller relMAD, at least for the subset of complexes for which the calculation could be converged, but is less robust for systems with smaller HOMO–LUMO gaps. The inclusion of Fock-exchange is therefore important for the description of very large non-covalent interaction (NCI) complexes in the gas phase. GFN2-xTB was found to be the best performer of the SQM methods with an excellent result of only 11.1% deviation. From the assessed force fields, GFN-FF and GAFF achieve the best accuracy. Considering their low computational costs, both can be recommended for routine calculations of very large NCI complexes, with GFN-FF being clearly superior in terms of general applicability. Hence, GFN-FF may be routinely applied in supramolecular synthesis planning.

1 Introduction

2 The LNCI16 Benchmark Set

3 Computational Details

4 Generation of Reference Values

5 Results and Discussion

6 Conclusions

Self-Assembled Monolayers of Molecular Conductors with Terpyridine-Metal Redox Switching Elements: A Combined AFM, STM and Electrochemical Study

Self-assembled monolayers (SAMs) of terpyridine-based transition metal (ruthenium and osmium) complexes, anchored to gold substrate via tripodal anchoring groups, have been investigated as possible redox switching elements for molecular electronics. An electrochemical study was complemented by atomic force microscopy (AFM) and scanning tunneling microscopy (STM) methods. STM was used for determination of the SAM conductance values, and computation of the attenuation factor β from tunneling current-distance curves. We have shown that SAMs of Os-tripod molecules contain larger adlayer structures compared with SAMs of Ru-tripod molecules, which are characterized by a large number of almost evenly distributed small islands. Furthermore, upon cyclic voltammetric experimentation, Os-tripod films rearrange to form a smaller number of even larger islands, reminiscent of the Ostwald ripening process. Os-tripod SAMs displayed a higher surface concentration of molecules and lower conductance compared with Ru-tripod SAMs. The attenuation factor of Os-tripod films changed dramatically, upon electrochemical cycling, to a higher value. These observations are in accordance with previously reported electron transfer kinetics studies.

Best-Practice DFT Protocols for Basic Molecular Computational Chemistry

Nowadays, many chemical investigations are supported by routine calculations of molecular structures, reaction energies, barrier heights, and spectroscopic properties. The lion's share of these quantum-chemical calculations applies density functional theory (DFT) evaluated in atomic-orbital basis sets. This work provides best-practice guidance on the numerous methodological and technical aspects of DFT calculations in three parts: Firstly, we set the stage and introduce a step-by-step decision tree to choose a computational protocol that models the experiment as closely as possible. Secondly, we present a recommendation matrix to guide the choice of functional and basis set depending on the task at hand. A particular focus is on achieving an optimal balance between accuracy, robustness, and efficiency through multi-level approaches. Finally, we discuss selected representative examples to illustrate the recommended protocols and the effect of methodological choices.

A computational study of the inclusion of β-cyclodextrin and nicotinic acid: DFT, DFT-D, NPA, NBO, QTAIM, and NCI-RDG studies

Forming complexes with cyclodextrins can protect nicotinic acid (vitamin B3) from premature metabolism and enhance the solubility and stability of this drug. In this work, the formation of the inclusion complex of the neutral form of nicotinic acid and β-cyclodextrin was achieved. The complex is modeled using PM3, PM6-D4H3, and PM7, by considering two orientations of the guest: A and B, one is from wide to narrow rim, and the second is from narrow to the wide rim, respectively. The global minima positions were re-optimized using three density function methods: MN-15, B3LYP, and PW6B95-D3 with polarized Pople basis set 6-31G(d) in gas and aqueous phase. Orientation A showed the minimum complexation energy where the carboxylic functional group of nicotinic acid is located on the primary hydroxyl rim of β-cyclodextrin and the pyridine ring is totally embedded in the cavity. To further our study on the nature of complexation and the interactions of this host-guest system, different calculations were done. The reactivity indices showed that orientation A is harder than B and more electrophilic; the charge transfer occurred from the host to the guest and was confirmed by the natural population analysis (NPA). The natural bond orbitals (NBO) reveal the delocalization of orbitals between the host and the guest, quantum theory of atoms in molecules (QTAIM) analysis, and non-covalent interaction (NCI) analysis based on a reduced density gradient (RDG) give a detailed description of the nature of interactions between the host and the guest such as the hydrogen bonding and van der Waals interaction, and confirmed the stability of the complex given by the orientation A.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

Efficient and Accurate Description of Diels-Alder Reactions Using Density Functional Theory

Modeling chemical reactions using Quantum Chemistry is a widely used predictive strategy capable to complement experiments in order to understand the intrinsic mechanisms guiding the chemicals towards the most favorable reaction products. However, at this purpose, it is mandatory to use reliable and computationally tractable theoretical methods. In this work, we focus on six Diels-Alder reactions of increasing complexity and perform an extensive benchmark of middle- to low-cost computational approaches to predict the characteristic reactions energy barriers. We found that Density Functional Theory, using the ωB97XD, LC-ωPBE, CAM-B3LYP, M11 and MN12SX functionals, with empirical dispersion corrections coupled to an affordable 6-31G basis set, provides quality results for this class of reactions, at a small computational effort. Such efficient and reliable simulation protocol opens perspectives for hybrid QM/MM molecular dynamics simulations of Diels-Alder reactions including explicit solvation.

A computational study of potential therapeutics for COVID-19 invoking conceptual density functional theory

The pandemic, COVID-19, has caused social and economic disruption at a larger pace all over the world. Identification of an effective drug for the deadliest disease is still an exigency. One of the most promising approaches to combat the lethal disease is use of repurposed drugs. This study provides insights into some of the potential repurposed drugs viz. camostat mesylate, hydroxychloroquine, nitazoxanide, and oseltamivir in terms of the computational quantum chemical method. Properties of these compounds have been elucidated in terms of Conceptual Density Functional Theory (CDFT)-based descriptors, IR spectra, and thermochemical properties. Computed results specify that hydroxychloroquine is the most reactive drug among them. Thermochemical data reveals that camostat mesylate has the utmost heat capacity, entropy, and thermal energy. Our findings indicate that camostat mesylate and hydroxychloroquine may be investigated further as potential COVID-19 therapeutics. We anticipate that the current study will aid the scientific community to design and develop viable therapeutics against COVID-19.

Carbon Nanodots from an In Silico Perspective

Carbon nanodots (CNDs) are the latest and most shining rising stars among photoluminescent (PL) nanomaterials. These carbon-based surface-passivated nanostructures compete with other related PL materials, including traditional semiconductor quantum dots and organic dyes, with a long list of benefits and emerging applications. Advantages of CNDs include tunable inherent optical properties and high photostability, rich possibilities for surface functionalization and doping, dispersibility, low toxicity, and viable synthesis (top-down and bottom-up) from organic materials. CNDs can be applied to biomedicine including imaging and sensing, drug-delivery, photodynamic therapy, photocatalysis but also to energy harvesting in solar cells and as LEDs. More applications are reported continuously, making this already a research field of its own. Understanding of the properties of CNDs requires one to go to the levels of electrons, atoms, molecules, and nanostructures at different scales using modern molecular modeling and to correlate it tightly with experiments. This review highlights different in silico techniques and studies, from quantum chemistry to the mesoscale, with particular reference to carbon nanodots, carbonaceous nanoparticles whose structural and photophysical properties are not fully elucidated. The role of experimental investigation is also presented. Hereby, we hope to encourage the reader to investigate CNDs and to apply virtual chemistry to obtain further insights needed to customize these amazing systems for novel prospective applications.