Merck molecular force field. V. Extension of MMFF94 using experimental data, additional computational data, and empirical rules

Drosophila melanogaster as a model organism for screening acetylcholinesterase reactivators

The widely used insecticide chlorpyrifos (CP) is known to inhibit acetylcholinesterase (AChE) activity attributed to result in various neurological disorders and acetylcholine-dependent organ functions including heart, skeletal muscle, lung, gastrointestinal tract, and central nervous systems. Enzyme reactivators, such as oximes, are known to restore AChE activity and mitigate adverse effects. The identification of compounds that reactivate AChE constitute agents with important therapeutic beneficial effects in cases of pesticide poisoning. However, the screening of novel drugs using traditional models may raise ethical concerns. This study aimed to investigate the potential of Drosophila melanogaster as a model organism for screening AChE reactivators, with a focus on organophosphate poisoning. The efficacy of several oximes, including pralidoxime, trimedoxime, obidoxime, methoxime, HI-6, K027, and K048, against CP-induced AChE activity inhibition in D. melanogaster was determined in silico, in vitro, and in vivo experiments. Molecular docking studies indicated a strong interaction between studied oximes and the active-site gorge of AChE. Data showed that selected oximes (100 μM) are effective in the reactivation of AChE inhibited by CP (10 μM) in vitro. Finally, in vivo investigations demonstrated that selected oximes, pralidoxime and K048 (1.5 ppm), reversed the locomotor deficits, inhibition of AChE activity as well as lowered the mortality rates induced by CP (0.75 ppm). Our findings contribute to utilization of D. melanogaster as a robust model for determination of actions of identified new AChE inhibitory agents with more effective therapeutic properties that those currently in use in the clinical practice in treatment of AChE associated disorders.

Comprehensive Computational Investigation of the Porphyrin-Based COF as a Nanocarrier for Delivering Anti-Cancer Drugs: A Combined MD Simulation and DFT Calculation

As nanomaterials have gained prominence in drug delivery technology, exploring their feasibility through computational methods is beneficial before practical tests. In this study, we aim to evaluate the capability of the porphyrin-based covalent organic framework COF-366 as a nanocarrier for two anticancer drugs, irinotecan (IRI) and doxorubicin (DOX). The optimal binding conformation of the drug molecules on the COF surface was predicted by using molecular docking. Subsequently, molecular dynamic simulation (MD) was performed to assess the adsorption mechanism of drug molecules on the COF in the aqueous environment. The free energy of adsorption for DOX and IRI was estimated to be -20.07 and -23.89 kcal/mol, respectively. The adsorption of both drugs on the COF surface is mainly influenced by the π-π interaction. Furthermore, density functional theory (DFT) calculation, natural bond orbital (NBO), and quantum theory of atoms in molecules (QTAIM) analyses were employed to investigate the structural stability of Drug@COF complexes and gain a detailed understanding of the interaction between them at the molecular level. Based on DFT results, it was found that in addition to π-π interaction, the bis-piperidine-phenylene interaction affects the adsorption of IRI on the COF surface. Moreover, the diffusion behavior of the drug molecule inside the COF pore was simulated using a ten-layer COF. Based on the mean square displacement analysis, the diffusion coefficients of DOX and IRI within the COF pore were calculated to be 108 and 97 um2/s, respectively. This computational study sheds light on how different types of interactions between the drug molecule and COF affect the adsorption and diffusion process. Our findings validated that the porphyrin-based COF-366 can serve as a nanobased platform for delivering DOX and IRI.

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning

Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometries remain elusive. Recent deep learning approaches have significantly accelerated protein structure prediction and the generation of small-molecule conformational ensembles, yet similar progress has not been made for macrocyclic peptides due to their unique properties. Here, we introduce CREMP, a resource generated for the rapid development and evaluation of machine learning models for macrocyclic peptides. CREMP contains 36,198 unique macrocyclic peptides and their high-quality structural ensembles generated using the Conformer-Rotamer Ensemble Sampling Tool (CREST). Altogether, this new dataset contains nearly 31.3 million unique macrocycle geometries, each annotated with energies derived from semi-empirical extended tight-binding (xTB) DFT calculations. Additionally, we include 3,258 macrocycles with reported passive permeability data to couple conformational ensembles to experiment. We anticipate that this dataset will enable the development of machine learning models that can improve peptide design and optimization for novel therapeutics.

Sensitization of Europium Oxide Nanoparticles Enhances Signal-to-Noise over Autofluorescence with Time-Gated Luminescence Detection

Clinical translation of nanoparticle-based therapeutics has been limited, and a lack of preclinical delivery characterization is partly to blame, limiting our understanding of the mechanisms of failure. The improvement of the preclinical delivery assessment requires nanoparticles with higher detectability. This work focused on the exploration of several aromatic carboxylic ligands (terephthalic acid, quinaldic acid, and kynurenic acid) for the sensitization of europium oxide nanoparticles with a long emission lifetime to overcome cellular autofluorescence, a key confounder of detection in luminescence-based bioimaging. A facile one-pot synthesis and ligand exchange process generated and sensitized ultrasmall Eu2O3 cores. As reflected in the emission spectra and lifetimes, ligand binding yielded unique coordination environments around Eu3+. Then, the efficacy of sensitization was tested against the autofluorescence provided by tissue lysate. Normal (simultaneous excite-read) measurements showed integrated signal improvements over autofluorescence of 2.2-, 3.9-, and 14.0-fold for EuTA, EuQA, and EuKA, respectively. In time-gated mode, the improvements over autofluorescence were more dramatic with fold differences of 75-, 89-, and 108-fold for EuTA, EuQA, and EuKA, respectively. The investigation of novel sensitizers expands the breadth of the field of sensitized lanthanide oxide nanoparticles, and the signal enhancement observed with sensitization and time-gating supports the utility of the generated samples for future bioimaging applications.

Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Determination of 3- and 4-chloromethcathinone interactions with plasma proteins: study involving analytical and theoretical methods

PURPOSE

The purpose of this paper was to determine 3- and 4-chloromethcathinone (3- and 4-CMC) binding degree and possible binding interaction modes with human serum albumin (HSA) using analytical and theoretical methods.

METHODS

Experimental determination of 3- and 4-CMC binding degree with HSA was performed using gas chromatography-tandem mass spectrometry preceded by the equilibrium dialysis (ED) and ultrafiltration (UF). Nuclear magnetic resonance (NMR) spectroscopy was used to determine 3- and 4-CMC epitope-binding maps and possible binding sites in HSA. The molecular docking and molecular dynamics were employed to obtain detailed information about binding modes of 3- and 4-CMC enantiomers in HSA.

RESULTS

As follows from the presented data, the degree of binding of 3- and 4-CMC is at a similar level of approx. 80%. This indicates a relatively strong binding of CMC to plasma proteins. The model studies employing the NMR spectroscopy and molecular simulations indicate that both CMCs bind to HSA. The whole 3- and 4-CMC molecules are embedded in the binding sites, with aromatic moieties being in the closest contact with the HSA residues. Moreover, conducted experiments show that  Sudlow site II is the main binding center for 3- and 4-CMC and  Sudlow site I acts as the secondary binding site.

CONCLUSIONS

Although many studies describe pharmacological and toxicological properties of synthetic cathinones (SC), the data taking SCs binding in plasma into consideration are scarce. To our knowledge, this is the first report presenting comprehensive experimental and theoretical characterization of 3- and 4-CMC binding with plasma proteins.

Toward high-resolution modeling of small molecule-ion channel interactions

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2 Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

Conformational energies of biomolecules in solution: Extending the MPCONF196 benchmark with explicit water molecules

A prerequisite for the computational prediction of molecular properties like conformational energies of biomolecules is a reliable, robust, and computationally affordable method usually selected according to its performance for relevant benchmark sets. However, most of these sets comprise molecules in the gas phase and do not cover interactions with a solvent, even though biomolecules typically occur in aqueous solution. To address this issue, we introduce a with explicit water molecules solvated version of a gas-phase benchmark set containing 196 conformers of 13 peptides and other relevant macrocycles, namely MPCONF196 [J. Řezáč et al., JCTC 2018, 14, 1254-1266], and provide very accurate PNO-LCCSD(T)-F12b/AVQZ' reference values. The novel solvMPCONF196 benchmark set features two additional challenges beyond the description of conformers in the gas phase: conformer-water and water-water interactions. The overall best performing method for this set is the double hybrid revDSDPBEP86-D4/def2-QZVPP yielding conformational energies of almost coupled cluster quality. Furthermore, some (meta-)GGAs and hybrid functionals like B97M-V and ω B97M-D with a large basis set reproduce the coupled cluster reference with an MAD below 1 kcal mol- 1  . If more efficient methods are required, the composite DFT-method r2 SCAN-3c (MAD of 1.2 kcal mol- 1 ) is a good alternative, and when conformational energies of polypeptides or macrocycles with more than 500-1000 atoms are in the focus, the semi-empirical GFN2-xTB or the MMFF94 force field (for very large systems) are recommended.

Molecular Study of Pneumocystis jirovecii in Respiratory Samples of HIV Patients in Chile

Pneumocystis is an opportunistic fungus that causes potentially fatal pneumonia (PCP) in immunocompromised patients. The objective of this study was to determine the prevalence of P. jirovecii in HIV patients through phenotypic and molecular study, to investigate the genetic polymorphisms of P. jirovecii at the mitochondrial gene mtLSU and at the nuclear dihydropteroate synthase gene (DHPS), and by analysis of molecular docking to study the effect of DHPS mutations on the enzymatic affinity for sulfamethoxazole. A PCP prevalence of 28.3% was detected, with mtLSU rRNA genotypes 3 (33.3%) and 2 (26.6%) being the most common. A prevalence of 6.7% (1/15) mutations in the DHPS gene was detected, specifically at codon 55 of the amino acid sequence of dihydropteroate synthase. Molecular docking analysis showed that the combination of mutations at 55 and 98 codons is required to significantly reduce the affinity of the enzyme for sulfamethoxazole. We observed a low rate of mutations in the DHPS gene, and molecular docking analysis showed that at least two mutations in the DHPS gene are required to significantly reduce the affinity of dihydropteroate synthase for sulfamethoxazole.

Marine Toxins as Pharmaceutical Treasure Troves: A Focus on Saxitoxin Derivatives from a Computational Point of View

This work highlights the significant potential of marine toxins, particularly saxitoxin (STX) and its derivatives, in the exploration of novel pharmaceuticals. These toxins, produced by aquatic microorganisms and collected by bivalve mollusks and other filter-feeding organisms, offer a vast reservoir of chemical and biological diversity. They interact with sodium channels in physiological processes, affecting various functions in organisms. Exposure to these toxins can lead to symptoms ranging from tingling sensations to respiratory failure and cardiovascular shock, with STX being one of the most potent. The structural diversity of STX derivatives, categorized into carbamate, N-sulfocarbamoyl, decarbamoyl, and deoxydecarbamoyl toxins, offers potential for drug development. The research described in this work aimed to computationally characterize 18 STX derivatives, exploring their reactivity properties within marine sponges using conceptual density functional theory (CDFT) techniques. Additionally, their pharmacokinetic properties, bioavailability, and drug-likeness scores were assessed. The outcomes of this research were the chemical reactivity parameters calculated via CDFT as well as the estimated pharmacokinetic and ADME properties derived using computational tools. While they may not align directly, the integration of these distinct datasets enriches our comprehensive understanding of the compound's properties and potential applications. Thus, this study holds promise for uncovering new pharmaceutical candidates from the considered marine toxins.

How Modulator Binding at the Amyloidβ-γ-Secretase Interface Enhances Substrate Binding and Attenuates Membrane Distortion

Inhibition of γ-secretase, an intramembrane protease, to reduce secretion of Amyloid-β (Aβ) peptides has been considered for treating Alzheimer's disease. However, γ-secretase inhibitors suffer from severe side effects. As an alternative, γ-secretase modulators (GSM) reduce the generation of toxic peptides by enhancing the cleavage processivity without diminishing the enzyme activity. Starting from a known γ-secretase structure without substrate but in complex with an E2012 GSM, we generated a structural model that included a bound Aβ43 peptide and studied interactions among enzyme, substrate, GSM, and lipids. Our result suggests that E2012 binding at the enzyme-substrate-membrane interface attenuates the membrane distortion by shielding the substrate-membrane interaction. The model predicts that the E2012 modulation is charge-dependent and explains the preserved hydrogen acceptor and the aromatic ring observed in many imidazole-based GSM. Predicted effects of γ-secretase mutations on E2012 modulation were confirmed experimentally. We anticipate that the study will facilitate the future development of effective GSMs.

In silico identification and characterization of small molecule binding to the CD1d immunoreceptor

CD1 immunoreceptors are a non-classical major histocompatibility complex (MHC) that present antigens to T cells to elucidate immune responses against disease. The antigen repertoire of CD1 has been composed primarily of lipids until recently when CD1d-restricted T cells were shown to be activated by non-lipidic small molecules, such as phenyl pentamethyl dihydrobenzofuran sulfonate (PPBF) and related benzofuran sulfonates. To date structural insights into PPBF/CD1d interactions are lacking, so it is unknown whether small molecule and lipid antigens are presented and recognized through similar mechanisms. Furthermore, it is unknown whether CD1d can bind to and present a broader range of small molecule metabolites to T cells, acting out functions analogous to the MHC class I related protein MR1. Here, we perform in silico docking and molecular dynamics simulations to structurally characterize small molecule interactions with CD1d. PPBF was supported to be presented to T cell receptors through the CD1d F' pocket. Virtual screening of CD1d against more than 17,000 small molecules with diverse geometry and chemistry identified several novel scaffolds, including phytosterols, cholesterols, triterpenes, and carbazole alkaloids, that serve as candidate CD1d antigens. Protein-ligand interaction profiling revealed conserved residues in the CD1d F' pocket that similarly anchor small molecules and lipids. Our results suggest that CD1d could have the intrinsic ability to bind and present a broad range of small molecule metabolites to T cells to carry out its function beyond lipid antigen presentation.Communicated by Ramaswamy H. Sarma.

Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science but also a very difficult one due to the vast number of possible molecular systems. We propose an evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favorable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely, minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was carried out over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) data sets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to 106 QM calculations and were thus feasible only thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches.

Ensemble-based virtual screening of African natural products to target human thymidylate synthase

Human thymidylate synthase (hTS) is a validated drug target for chemotherapy. A virtual screening experiment was used to prioritize a list of compounds from African Natural Products Databases docked against the orthosteric binding pocket of hTS. Consensus scores of binding affinities from ensemble-based virtual screening, hydrated docking and MM-PBSA calculations ranked compounds NEA4433 and NEA4434 as the best candidates owing to binding affinity scores in the picomolar order, their excellent ADMET profiles and the good stability of the protein-ligand complexes formed. The current study demonstrates the role of water in small molecule binding to hTS in mediating protein-ligand interactions. Similarly, the robust ensemble docking (relaxed scheme complex) ranked NEA4433 and NEA4434 as the best candidates. Furthermore, the best candidates prioritized were shown to strongly interact with the same residues that interacted with hTS substrate and cofactor.

Discovery of ICOS-Targeted Small Molecules Using Pharmacophore-Based Screening

There are currently no small molecules clinically approved as immune checkpoint modulators. Besides possessing oral bioavailability, cell-penetrating capabilities and enhanced tumor penetration compared to monoclonal antibodies (mAbs), small molecules are amenable to pharmacokinetic optimization, which allows adopting flexible dosage regimens that may avoid immune-related adverse events associated with mAbs. The interaction of inducible co-stimulator (ICOS) with its ligand (ICOS-L) plays key roles in T-cell differentiation and activation of T-cell to B-cell functions. This study represents the development and validation of a virtual screening strategy to identify small molecules that bind a novel druggable binding pocket in human ICOS. We used a lipophilic canyon in the apo-structure of ICOS and the ICOS/ICOS-L interface individually as templates for molecular dynamics simulation to generate 3D pharmacophores subsequently used for virtual screening campaigns. Our strategy was successful finding a first-in-class small molecule ICOS binder (5P, KD value=108.08±26.76 μM) and validating biophysical screening platforms for ICOS-targeted small molecules. We anticipate that future structural optimization of 5P will result in the discovery of high affinity chemical ligands for ICOS.

Two-body dissociation of isoxazole following double photoionization - an experimental PEPIPICO and theoretical DFT and MP2 study

The dissociative double photoionization of isoxazole molecules has been investigated experimentally and theoretically. The experiment has been carried out in the 27.5-36 eV photon energy range using vacuum ultraviolet (VUV) synchrotron radiation excitation combined with ion time-of-flight (TOF) spectrometry and photoelectron-photoion-photoion coincidence (PEPIPICO) technique. Five well-resolved two-body dissociation channels have been identified in the isoxazole's coincidence maps, and their appearance energies have been determined. The coincidence yield curves of these dissociation channels have been obtained in the photon energy ranges from their appearance energies up to 36 eV. The double photoionization of isoxazole produces a C3H3NO2+ transient dication, which decomposes into fragments differing from previously reported photofragmentation products of isoxazole. We have found no evidence of pathways leading to the C3H2NO+, HCN+, C2H2O+, C3HN+, or C2H2+ fragments or their neutral counterparts that have been observed in previous neutral photodissociation and single photoionization studies. Instead, the dissociation of isoxazole after the ejection of two electrons is bond-selective and is governed by two reactions, HCO+ + H2CCN+ and H2CO+ + HCCN+, whose appearance energies are 28.6 (±0.3) and 29.4 (±0.3) eV, respectively. A third dissociation channel turns out to be a variant of the most intense channel (HCO+ + H2CCN+), where one of the fragment ions contains a heavy isotope. Two minor dissociation channels occurring at higher energies, CO+ + CH3CN+ and CN+ + H3CCO+, are also identified. The density functional and ab initio quantum chemical calculations have been performed to elucidate the dissociative charge-separating mechanisms and determine the energies of the observed photoproducts. The present work unravels hitherto unexplored photodissociation mechanisms of isoxazole and thus provides deeper insight into the photophysics of five-membered heterocyclic molecules containing two heteroatoms.

Particle-phase accretion forms dimer esters in pinene secondary organic aerosol

Secondary organic aerosol (SOA) is ubiquitous in the atmosphere and plays a pivotal role in climate, air quality, and health. The production of low-volatility dimeric compounds through accretion reactions is a key aspect of SOA formation. However, despite extensive study, the structures and thus the formation mechanisms of dimers in SOA remain largely uncharacterized. In this work, we elucidate the structures of several major dimer esters in SOA from ozonolysis of α-pinene and β-pinene-substantial global SOA sources-through independent synthesis of authentic standards. We show that these dimer esters are formed in the particle phase and propose a mechanism of nucleophilic addition of alcohols to a cyclic acylperoxyhemiacetal. This chemistry likely represents a general pathway to dimeric compounds in ambient SOA.

5-Substituted Uridines with Activity against Gram-Positive Bacteria

The emergence of drug-resistant strains of pathogenic microorganisms necessitates the creation of new drugs. A series of uridine derivatives containing an extended substituent at the C-5 position as well as C-5 alkyloxymethyl, alkylthiomethyl, alkyltriazolylmethyl, alkylsulfinylmethyl and alkylsulfonylmethyl uridines were obtained in order to explore their antimicrobial properties and solubility. It has been shown that new ribonucleoside derivatives have an order of magnitude better solubility in water compared to their 2'-deoxy analogues and effectively inhibit the growth of a number of Gram-positive bacteria, including resistant strains of Mycobacterium smegmatis (MIC=15-200 μg/mL) and Staphylococcus aureus (MIC=25-100 μg/mL). Their activity is comparable to that of some antibiotics used in medicine.

Truxillic acid monoamides as fatty acid binding protein 5 inhibitors

Fatty acid binding proteins (FABPs) are intracellular chaperones that deliver bioactive lipids to cytosolic enzymes and nuclear receptors, thereby regulating diverse biological functions. FABP5 is a member of the FABP family that mediates endocannabinoid transport and inactivation, with pharmacological or genetic FABP5 inhibition conferring antinociceptive effects. Consequently, FABP5 inhibitors have emerged as promising analgesics and demonstrate antinociceptive activity in models of pain. Recently developed FABP5 inhibitors based upon the α-truxillic acid monoester (TAME) scaffold demonstrate high affinities for FABP5 but are commonly accompanied by reduced selectivity against related FABPs, notably FABP3 that is expressed in the heart, highlighting the need to identify additional scaffolds that afford enhanced selectivity while maintaining FABP5 potency. Here, we describe the synthesis and biological evaluation of truxillic acid monoamides (TAMADs) as potent, selective, and efficacious FABP5 inhibitors. Combining in silico molecular docking and in vitro binding assay approaches, our findings demonstrate that TAMADs exhibit exceptional selectivity against FABP3 and several compounds attain high FABP5 affinities. Examination of antinociceptive activity revealed that TAMADs and their corresponding TAMEs demonstrate comparable efficacy and temporal activity profiles in vivo. These results position TAMAD as a suitable scaffold for the development of FABP5 inhibitors with diminished FABP3 cross-reactivity.

Dense Neural Network for Calculating Solvation Free Energies from Electronegativity-Equalization Atomic Charges

I propose a dense Neural Network for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents, Easy Solvation Energy with Electronegativity Equalization charges and Dense Neural Network (ESE-EE-DNN). As input features, it uses the Conductor-like Screening Model (COSMO) electrostatic energy, atomic cavity surface areas, total cavity volume, and induced surface charges. For the COSMO calculation, electronegativity-equalization atomic charges are employed. ESE-EE-DNN exhibits fairly high accuracy, similar or even superior to that of mainstream density functional theory-based methods. For neutral solutes in water, polar protic, polar aprotic, and nonpolar solvents, ESE-EE-DNN yields a root-mean-square error (RMSE) of 1.25, 1.36, 0.70, and 0.71 kcal/mol, respectively. ESE-EE-DNN is particularly advantageous for ionic solutes, with an RMSE of 2.82 and 1.42 kcal/mol for aqueous and nonaqueous ion solutions, correspondingly. ESE-EE-DNN is very efficient due to a fast evaluation of the electronegativity-equalization charges.

Synthesis, Cytotoxic Activity and In Silico Study of Novel Dihydropyridine Carboxylic Acids Derivatives

To aid the possible prevention of multidrug resistance in tumors and cause lower toxicity, a set of sixteen novel dihydropyridine carboxylic acids derivatives 3a-p were produced; thus, the activation of various ynones with triflic anhydride was performed, involving a nucleophilic addition of several bis(trimethylsilyl) ketene acetals, achieving good yields requiring easy workup. The target molecules were unequivocally characterized by common spectroscopic methods. In addition, two of the tested compounds (3a, and 3b) were selected to perform in silico studies due to the highest cytotoxic activity towards the HCT-15 cell line (7.94 ± 1.6 μM and 9.24 ± 0.9 μM, respectively). Employing theoretical calculations with density functional theory (DFT) using the B3LYP/6-311++G(d,p) showed that the molecular parameters correlate adequately with the experimental results. In contrast, predictions employing Osiris Property Explorer showed that compounds 3a and 3b present physicochemical characteristics that would likely make it an orally active drug. Moreover, the performance of Docking studies with proteins related to the apoptosis pathway allowed a proposal of which compounds could interact with PARP-1 protein. Pondering the obtained results (synthesis, in silico, and cytotoxic activity) of the target compounds, they can be judged as suitable antineoplastic agent candidates.

Computational Discovery of Marine Molecules of the Cyclopeptide Family with Therapeutic Potential

Stellatolides are natural compounds that have shown promising biological activities, including antitumor, antimicrobial, and anti-inflammatory properties, making them potential candidates for drug development. Chemical Reactivity Theory (CRT) is a branch of chemistry that explains and predicts the behavior of chemical reactions based on the electronic structure of molecules. Conceptual Density Functional Theory (CDFT) and Computational Peptidology (CP) are computational approaches used to study the behavior of atoms, molecules, and peptides. In this study, we present the results of our investigation of the chemical reactivity and ADMET properties of Stellatolides A-H using a novel computational approach called Conceptual DFT-based Computational Peptidology (CDFT-CP). Our study uses CDFT and CP to predict the reactivity and stability of molecules and to understand the behavior of peptides at the molecular level. We also predict the ADMET properties of the Stellatolides A-H to provide insight into their effectiveness, potential side effects, and optimal dosage and route of administration, as well as their biological targets. This study sheds light on the potential of Stellatolides A-H as promising candidates for drug development and highlights the potential of CDFT-CP for the study of other natural compounds and peptides.

Talarolide A and Talaropeptides A-D: Potential Marine-Derived Therapeutic Peptides with Interesting Chemistry and Biological Activity Studied through Density Functional Theory (DFT) and Conceptual DFT

Molecules sourced from marine environments hold immense promise for the development of novel therapeutic drugs, owing to their distinctive chemical compositions and valuable medicinal attributes. Notably, Talarolide A and Talaropeptides A-D have gained recent attention as potential candidates for pharmaceutical applications. This study aims to explore the chemical reactivity of Talarolide A and Talaropeptides A-D through the application of molecular modeling and computational chemistry techniques, specifically employing Conceptual Density Functional Theory (CDFT). By investigating their chemical behaviors, the study seeks to contribute to the understanding of the potential pharmacological uses of these marine-derived compounds. The molecular geometry optimizations and frequency calculations were conducted using the Density Functional Tight Binding (DFTBA) method. This was followed by a subsequent round of geometry optimization, frequency analysis, and computation of electronic properties and chemical reactivity descriptors. We employed the MN12SX/Def2TZVP/H2O model chemistry, utilizing the Gaussian 16 program and the SMD solvation model. The analysis of the global reactivity descriptors arising from CDFT was achieved as well as the graphical comparison of the dual descriptor DD revealing the areas of the molecules with more propensity to suffer a nucleophilic or electrophilic attack. Additionally, Molinspiration and SwissTargetPrediction were considered for the calculation of molecular characteristics and predicted biological targets. These include enzymes, nuclear receptors, kinase inhibitors, GPCR ligands, and ion channel modulators. The graphical results show that Talarolide A and the Talaropeptides A-D are likely to behave as protease inhibitors.

Protein Modelling and Molecular Docking Analysis of Fasciola hepatica β-Tubulin's Interaction Sites, with Triclabendazole, Triclabendazole Sulphoxide and Triclabendazole Sulphone

PURPOSE

Fasciola hepatica is a globally distributed trematode that causes significant economic losses. Triclabendazole is the primary pharmacological treatment for this parasite. However, the increasing resistance to triclabendazole limits its efficacy. Previous pharmacodynamics studies suggested that triclabendazole acts by interacting mainly with the β monomer of tubulin.

METHODS

We used a high-quality method to model the six isotypes of F. hepatica β-tubulin in the absence of three-dimensional structures. Molecular dockings were conducted to evaluate the destabilization regions in the molecule against the ligands triclabendazole, triclabendazole sulphoxide and triclabendazole sulphone.

RESULTS

The nucleotide binding site demonstrates higher affinity than the binding sites of colchicine, albendazole, the T7 loop and pβVII (p < 0.05). We suggest that the binding of the ligands to the polymerization site of β-tubulin can lead a microtubule disruption. Furthermore, we found that triclabendazole sulphone exhibited significantly higher binding affinity than other ligands (p < 0.05) across all isotypes of β-tubulin.

CONCLUSIONS

Our investigation has yielded new insight on the mechanism of action of triclabendazole and its sulphometabolites on F. hepatica β-tubulin through computational tools. These findings have significant implications for ongoing scientific research ongoing towards the discovery of novel therapeutics to treat F. hepatica infections.

4'-(8-(4-Methylimidazole)-octyloxy)-arctigenin: The first inhibitor of fish rhabdovirus glycoprotein

Spring viraemia of carp virus (SVCV), a highly pathogenic rhabdovirus, could cause spring viraemia of carp (SVC) with up to 90% lethality. Like other rhabdoviruses, the entry of SVCV into susceptible cells was mediated by a single envelope glycoprotein G. Specific inhibitors targeting the glycoprotein were the most effective means to alleviate the epidemic. The programs including SWISS-MODEL, I-TASSER, Phyre2 and AlphaFold2 were used to build a three-dimensional structural model of glycoprotein. The structural comparison between SVCV-G and homology protein VSV-G revealed that the SVCV glycoprotein ectodomain (residues 19 to 466) folded into four distinct domains. Based on the potential small molecule binding sites on glycoprotein surfaces, virtual screening of the anti-SVCV drug libraries was performed using Autodock software and 4'-(8-(4-Methylimidazole)-octyloxy)-arctigenin (MOA) with a high binding affinity was identified. The solubility enhancer tags including trigger factor and maltose binding protein were fused with the ectodomain of glycoprotein, and the target protein with a purity of about 90% was successfully obtained. The interaction confirmation tests revealed that the fluorescence intensity of a characteristic peak induced by the endogenous chromophores in glycoprotein was decreased with the addition of MOA, indicating changes in the microenvironment of glycoprotein. Moreover, the interaction could cause a slight conformational change in glycoprotein, as shown by the content of β-turn, β-folding, and random coil of protein all increased with the decrease of α-helix content after the addition of MOA compound. These results demonstrated that MOA could act as a novel drug against fish rhabdovirus via direct targeting of glycoprotein.

Preliminary Evaluation of Lablab purpureus Phytochemicals for Anti-BoHV-1 Activity Using In Vitro and In Silico Approaches

Lablab purpureus from the Fabaceae family has been reported to have antiviral properties and used in traditional medical systems like ayurveda and Chinese medicine and has been employed to treat a variety of illnesses including cholera, food poisoning, diarrhea, and phlegmatic diseases. The bovine alphaherpesvirus-1 (BoHV-1) is notorious for causing significant harm to the veterinary and agriculture industries. The removal of the contagious BoHV-1 from host organs, particularly in those reservoir creatures, has required the use of antiviral drugs that target infected cells. This study developed LP-CuO NPs from methanolic crude extracts, and FTIR, SEM, and EDX analyses were used to confirm their formation. SEM analysis revealed that the LP-CuO NPs had a spherical shape with particle sizes between 22 and 30 nm. Energy-dispersive X-ray pattern analysis revealed the presence of only copper and oxide ions. By preventing viral cytopathic effects in the Madin-Darby bovine kidney cell line, the methanolic extract of Lablab purpureus and LP-CuO NPs demonstrated a remarkable dose-dependent anti-BoHV-1 action in vitro. Furthermore, molecular docking and molecular dynamics simulation studies of bio-actives from Lablab purpureus against the BoHV-1 viral envelope glycoprotein disclosed effective interactions between all phytochemicals and the protein, although kievitone was found to have the highest binding affinity, with the greatest number of interactions, which was also validated with molecular dynamics simulation studies. Understanding the chemical reactivity qualities of the four ligands was taken into consideration facilitated by the global and local descriptors, which aimed to predict the chemical reactivity descriptors of the studied molecules through the conceptual DFT methodology, which, along with ADMET finding, support the in vitro and in silico results.

Roles of Virtual Screening and Molecular Dynamics Simulations in Discovering and Understanding Antimalarial Drugs

Malaria continues to be a global health threat, with approximately 247 million cases worldwide. Despite therapeutic interventions being available, patient compliance is a problem due to the length of treatment. Moreover, drug-resistant strains have emerged over the years, necessitating urgent identification of novel and more potent treatments. Given that traditional drug discovery often requires a great deal of time and resources, most drug discovery efforts now use computational methods. In silico techniques such as quantitative structure-activity relationship (QSAR), docking, and molecular dynamics (MD) can be used to study protein-ligand interactions and determine the potency and safety profile of a set of candidate compounds to help prioritize those tested using assays and animal models. This paper provides an overview of antimalarial drug discovery and the application of computational methods in identifying candidate inhibitors and elucidating their potential mechanisms of action. We conclude with the continued challenges and future perspectives in the field of antimalarial drug discovery.

Accurate Host-Guest Binding Free Energies Using the AMOEBA Polarizable Force Field

A grand challenge of computational biophysics is accurate prediction of interactions between molecules. Molecular dynamics (MD) simulations have recently gained much interest as a tool to directly compute rigorous intermolecular binding affinities. The choice of a fixed point-charge or polarizable multipole force field used in MD is a topic of ongoing discussion. To compare alternative methods, we participated in the SAMPL7 and SAMPL8 Gibb octaacid host-guest challenges to assess the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) polarizable multipole force field. Advantages of AMOEBA over fixed charge models include improved representation of molecular electrostatic potentials and better description of water occupying the unligated host cavity. Prospective predictions for 26 host-guest systems exhibit a mean unsigned error vs experiment of 0.848 kcal/mol across all absolute binding free energies, demonstrating excellent agreement between computational and experimental results. In addition, we explore two topics related to the inclusion of ions in MD simulations: use of a neutral co-alchemical protocol and the effect of salt concentration on binding affinity. Use of the co-alchemical method minimally affects computed energies, but salt concentration significantly perturbs our binding results. Higher salt concentration strengthens binding through classical charge screening. In particular, added Na+ ions screen negatively charged carboxylate groups near the binding cavity, thereby diminishing repulsive coulomb interactions with negatively charged guests. Overall, the AMOEBA results demonstrate the accuracy available through a force field providing a detailed energetic description of the four octaacid hosts and 13 charged organic guests. Use of the AMOEBA polarizable atomic multipole force field in conjunction with an alchemical free energy protocol can achieve chemical accuracy in application to realistic molecular systems.

Targeting Sterylglucosidase A to Treat Aspergillus fumigatus Infections

Invasive fungal infections are a leading cause of death in immunocompromised patients. Current therapies have several limitations, and innovative antifungal agents are critically needed. Previously, we identified the fungus-specific enzyme sterylglucosidase as essential for pathogenesis and virulence of Cryptococcus neoformans and Aspergillus fumigatus (Af) in murine models of mycoses. Here, we developed Af sterylglucosidase A (SglA) as a therapeutic target. We identified two selective inhibitors of SglA with distinct chemical scaffolds that bind in the active site of SglA. Both inhibitors induce sterylglucoside accumulation and delay filamentation in Af and increase survival in a murine model of pulmonary aspergillosis. Structure-activity relationship (SAR) studies identified a more potent derivative that enhances both in vitro phenotypes and in vivo survival. These findings support sterylglucosidase inhibition as a promising antifungal approach with broad-spectrum potential. IMPORTANCE Invasive fungal infections are a leading cause of death in immunocompromised patients. Aspergillus fumigatus is a fungus ubiquitously found in the environment that, upon inhalation, causes both acute and chronic illnesses in at-risk individuals. A. fumigatus is recognized as one of the critical fungal pathogens for which a substantive treatment breakthrough is urgently needed. Here, we studied a fungus-specific enzyme, sterylglucosidase A (SglA), as a therapeutic target. We identified selective inhibitors of SglA that induce accumulation of sterylglucosides and delay filamentation in A. fumigatus and increase survival in a murine model of pulmonary aspergillosis. We determined the structure of SglA, predicted the binding poses of these inhibitors through docking analysis, and identified a more efficacious derivative with a limited SAR study. These results open several exciting avenues for the research and development of a new class of antifungal agents targeting sterylglucosidases.

Machine Learning Prediction of Hydration Free Energy with Physically Inspired Descriptors

We present machine learning models for predicting experimental hydration free energies of molecules without any atom-, bond-, or geometry-specific input feature. Four types of physically inspired descriptors are adopted for predictions. The first type is composed of the total dipole moment, anisotropic polarizability, and vibrational analysis results of the solute molecule. The second and third types are derived from the electrostatic potential distribution of the solute. The last type includes the solvent accessible surface area and shape similarities. Several machine learning regression models are built on the basis of the FreeSolv database with ∼600 samples, showing a better performance in comparison with that of most traditional approaches and other prediction methods based on molecular fingerprints. In particular, the present descriptors are capable of predicting hydration free energies of new compounds with elements or fragments that are never seen in the training set. The importance of these descriptors, the impact of dissociation energies of specific covalent bonds, and the outliers with relatively large prediction errors are also discussed.

Synthesis, In Silico Study, Antibacterial and Antifungal Activities of N-phenylbenzamides

Antibiotic and antifungal resistance problems have been prevalent in recent decades. One of the efforts to solve the problems is to develop new medicines with more potent antibacterial and antifungal activity. N-phenylbenzamides have the potential to be developed as antibacterial and antifungal medicine. This study aimed to synthesize N-phenylbenzamides and evaluate their in silico and in vitro antibacterial and antifungal activities. The in silico studies conducted absorption, distribution, metabolism, excretion and toxicity (ADMET) predictions along with molecular docking studies. ADMET predictions used pkCSM software online, while the docking studies used MVD software (Molegro ^® Virtual Docker version 5.5) on Aminoglycosid-2 ″-phosphotransferase-IIa (APH2 ″-IIa) enzyme with protein data bank (PDB) ID code 3HAV as antibacterial and aspartic proteinases enzyme (Saps) with PDB ID code 2QZX as an antifungal. In vitro, antibacterial and antifungal tests were carried out using the zone of inhibition (ZOI) method. The five N-phenylbenzamides (3a-e) were successfully synthesized with a high yield. Based on in silico and in vitro studies, compounds 3a-e have antibacterial and antifungal activities, where they can inhibit the growth of Gram-positive bacteria (Staphylococcus aureus), Gram-negative (Escherichia coli), and Candida albicans. Therefore, compounds 3a-e can be developed as a topical antibacterial and antifungal agent.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Calix[6]arene-Based [3]Rotaxanes as Prototypes for the Template Synthesis of Molecular Capsules

In this work, the ability of several bis-viologen axles to thread a series of heteroditopic tris(N-phenylureido)calix[6]arene wheels to give interwoven supramolecular complexes to the [3]pseudorotaxane type was studied. The unidirectionality of the threading process inside these nonsymmetric wheels allows the formation of highly preorganised [3]pseudorotaxane and [3]rotaxane species in which the macrocycles phenylureido moieties, functionalised with either ester, carboxylic, or hydroxymethyl groups, are facing each other. As verified by NMR and semiempirical computational studies, these latter compounds possess the correct spatial arrangement of their subcomponents, which could lead, in principle, upon proper bridging reaction, to the realisation of upper-to-upper molecular capsules that are based on calix[6]arene derivatives.

Systems and in vitro pharmacology profiling of diosgenin against breast cancer

Aim: The purpose of this study was to establish a mode of action for diosgenin against breast cancer employing a range of system biology tools and to corroborate its results with experimental facts. Methodology: The diosgenin-regulated domains implicated in breast cancer were enriched in the Kyoto Encyclopedia of Genes and Genomes database to establish diosgenin-protein(s)-pathway(s) associations. Later, molecular docking and the lead complexes were considered for molecular dynamics simulations, MMPBSA, principal component, and dynamics cross-correlation matrix analysis using GROMACS v2021. Furthermore, survival analysis was carried out for the diosgenin-regulated proteins that were anticipated to be involved in breast cancer. For gene expression analyses, the top three targets with the highest binding affinity for diosgenin and tumor expression were examined. Furthermore, the effect of diosgenin on cell proliferation, cytotoxicity, and the partial Warburg effect was tested to validate the computational findings using functional outputs of the lead targets. Results: The protein-protein interaction had 57 edges, an average node degree of 5.43, and a p-value of 3.83e-14. Furthermore, enrichment analysis showed 36 KEGG pathways, 12 cellular components, 27 molecular functions, and 307 biological processes. In network analysis, three hub proteins were notably modulated: IGF1R, MDM2, and SRC, diosgenin with the highest binding affinity with IGF1R (binding energy -8.6 kcal/mol). Furthermore, during the 150 ns molecular dynamics (MD) projection run, diosgenin exhibited robust intermolecular interactions and had the least free binding energy with IGF1R (-35.143 kcal/mol) compared to MDM2 (-34.619 kcal/mol), and SRC (-17.944 kcal/mol). Diosgenin exhibited the highest cytotoxicity against MCF7 cell lines (IC₅₀ 12.05 ± 1.33) µg/ml. Furthermore, in H₂O₂-induced oxidative stress, the inhibitory constant (IC₅₀ 7.68 ± 0.51) µg/ml of diosgenin was lowest in MCF7 cell lines. However, the reversal of the Warburg effect by diosgenin seemed to be maximum in non-cancer Vero cell lines (EC₅₀ 15.27 ± 0.95) µg/ml compared to the rest. Furthermore, diosgenin inhibited cell proliferation in SKBR3 cell lines more though. Conclusion: The current study demonstrated that diosgenin impacts a series of signaling pathways, involved in the advancement of breast cancer, including FoxO, PI3K-Akt, p53, Ras, and MAPK signaling. Additionally, diosgenin established a persistent diosgenin-protein complex and had a significant binding affinity towards IGF1R, MDM2, and SRC. It is possible that this slowed down cell growth, countered the Warburg phenomenon, and showed the cytotoxicity towards breast cancer cells.

An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus

BACKGROUND

The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere.

METHODS

In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result.

RESULT

We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (-7.7 and -7.3 kcal mol-1) when compared with the control drug Telaprevir (-5.9 and -6.0 kcal mol-1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition.

CONCLUSION

To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

On the Origin of the Blue Color in The Iodine/Iodide/Starch Supramolecular Complex

The nature of the blue color in the iodine-starch reaction is still a matter of debate. Some textbooks still invoke charge-transfer bands within a chain of neutral I₂ molecules inside the hydrophobic channel defined by the interior of the amylose helical structure. However, the consensus is that the interior of the helix is not altogether hydrophobic-and that a mixture of I₂ molecules and iodide anions reside there and are responsible for the intense charge-transfer bands that yield the blue color of the "iodine-starch complex". Indeed, iodide is a prerequisite of the reaction. However, some debate still exists regarding the nature of the iodine-iodine units inside the amylose helix. Species such as I₃^-, I₅^-, I₇^- etc. have been invoked. Here, we report UV-vis titration data and computational simulations using density functional theory (DFT) for the iodine/iodide chains as well as semiempirical (AM1, PM3) calculations of the amylose-iodine/iodide complexes, that (1) confirm that iodide is a pre-requisite for blue color formation in the iodine-starch system, (2) propose the nature of the complex to involve alternating sets of I₂ and I_x^- units, and (3) identify the nature of the charge-transfer bands as involving transfer from the I_x^- σ* orbitals (HOMO) to I₂ σ* LUMO orbitals. The best candidate for the "blue complex", based on DFT geometry optimizations and TD-DFT spectral simulations, is an I₂-I₅-I₂ unit, which is expected to occur in a repetitive manner inside the amylose helix.

Molecular Mechanics Simulations and Experimental Investigation of the Effect of Tadalafil on Various Inflammatory Pain Mediators

PURPOSE

Tadalafil's exact analgesic mechanism is still unclear. The current study aimed to elucidate this mechanism in an inflammatory pain model.

METHODS

Computer-assisted simulation docking experiments were carried out to assess the binding of tadalafil to different ligands. The anti-inflammatory and analgesic effects of tadalafil were evaluated using formalin-induced paw edema and a von Frey filament test, respectively. The plantar paw of the mice was then dissected to quantify iNOS, nNOS, COX-2, TNFα, IL1, and IL10 gene expression levels using a real-time polymerase chain reaction. iNOS, TNFα, and COX-2 inhibition was reassessed in vitro using the ELISA technique. One-way analysis of variance followed by post hoc Tukey test or t-test was used to compare the means.

RESULTS

Docking analysis showed a superior binding score of tadalafil to COX-2, iNOS, IL-1, and TNF-α compared to that of indomethacin and morphine and a similar binding score to nNOS and IL-10 relative to that of indomethacin. In the in vivo study, tadalafil, after an hour of formalin administration, inhibited significantly paw edema, similar to indomethacin. Furthermore, it significantly increased the withdrawal force in the von Frey filament test as compared to the negative control, which was similar to the effect observed with indomethacin and morphine. The RT-PCR revealed that tadalafil reduced significantly the iNOS, COX-2, and TNF-α gene expressions but had no effect on nNOS, IL 1, and IL10. In vitro ELISA tests confirmed the inhibition of iNOS, COX-2, and TNF-α.

CONCLUSION

Tadalafil probably exerts its analgesic effect through the simultaneous inhibition of iNOS, COX-2, and TNF-α, which is not the case with other nonsteroidal anti-inflammatory drugs. Nevertheless, further studies are required to confirm its mechanism.

SAR study on Novel truxillic acid monoester-Based inhibitors of fatty acid binding proteins as Next-Generation antinociceptive agents

Fatty acid binding protein 5 (FABP5) is a highly promising target for the development of analgesics as its inhibition is devoid of CB1R-dependent side-effects. The design and discovery of highly potent and FABP5-selective truxillic acid (TA) monoesters (TAMEs) is the primary aim of the present study. On the basis of molecular docking analysis, ca. 2,000 TAMEs were designed and screened in silico, to funnel down to 55 new TAMEs, which were synthesized and assayed for their affinity (Ki) to FABP5, 3 and 7. The SAR study revealed that the introduction of H-bond acceptors to the far end of the 1,1'-biphenyl-3-yl and 1,1'-biphenyl-2-yl ester moieties improved the affinity of α-TAMEs to FABP5. Compound γ-3 is the first γ-TAME, demonstrating a high affinity to FABP5 and competing with α-TAMEs. We identified the best 20 TAMEs based on the FABP5/3 selectivity index. The clear front runner is α-16, bearing a 2‑indanyl ester moiety. In sharp contrast, no ε-TAMEs made the top 20 in this list. However, α-19 and ε-202, have been identified as potent FABP3-selective inhibitors for applications related to their possible use in the protection of cardiac myocytes and the reduction of α-synuclein accumulation in Parkinson's disease. Among the best 20 TAMEs selected based on the affinity to FABP7, 13 out of 20 TAMEs were found to be FABP7-selective, with α-21 as the most selective. This study identified several TAMEs as FABP7-selective inhibitors, which would have potentially beneficial therapeutic effects in diseases such as Down's syndrome, schizophrenia, breast cancer, and astrocytoma. We successfully introduced the α-TA monosilyl ester (TAMSE)-mediated protocol to dramatically improve the overall yields of α-TAMEs. α-TAMSEs with TBDPS as the silyl group is isolated in good yields and unreacted α-TA/ α-MeO-TA, as well as disilyl esters (α-TADSEs) are fully recycled. Molecular docking analysis provided rational explanations for the observed binding affinity and selectivity of the FABP3, 5 and 7 inhibitors, including their α, γ and ε isomers, in this study.

Theoretical Study on the Vapochromic Ni(II)-Quinonoid Complex: One-Dimensional Stacking Structure-Based Color Switching

Vapochromic crystals of Ni(II)-quinonoid complexes were theoretically investigated using density functional theory (DFT) calculations. Kato et al. previously reported that the purple crystals of a four-coordinate Ni(II)-quinonoid complex (1P) exhibited vapochromic characteristics upon exposure to methanol gas, resulting in orange crystals of the six-coordinate methanol-bound complex (1O) [Angew. Chem., Int. Ed.2017, 56, 2345-2349]. However, the authors did not characterize the crystal structure of 1P. In the present study, we computationally predicted the crystal structure of 1P by performing a crystal structure search with classical force-field computations followed by optimization using DFT calculations. The simulated powder X-ray diffraction pattern of the DFT-optimized structure agreed with experimental observations, indicating that our predicted crystal structure is reliable. Investigation of the optimized crystal structure of 1P revealed that its color change arose from changes in its 1D-band structure, which consists of Ni 3d orbitals and quinonoid π-orbitals. Intermolecular interactions were weakened upon the binding of methanol to the Ni(II) center in 1O. Consequently, the intermolecular 3d-π interaction in 1P lowered the band gap and induced the red-shifting of the monomeric four-coordinate Ni(II)-quinonoid complex. Meanwhile, the obtained absorption spectrum of 1O closely corresponded to that of the monomeric six-coordinate Ni(II)-quinonoid complex. Our study provides a new strategy for accurately predicting molecular crystal structures and reveals a new insight into vapochromism based on band structure color switching.

Phytoconstituents of Withania somnifera unveiled Ashwagandhanolide as a potential drug targeting breast cancer: Investigations through computational, molecular docking and conceptual DFT studies

Breast cancer is the second most common malignancy in females worldwide and poses a great challenge that necessitates the identification of novel therapeutic agents from several sources. This research aimed to study the molecular docking and molecular dynamics simulations of four proteins (such as PDB: 6CBZ, 1FDW, 5GWK and 2WTT) with the selected phytochemicals from Withania somnifera to identify the potential inhibitors for breast cancer. The molecular docking result showed that among 44 compounds, two of them, Ashwagandhanolide and Withanolide sulfoxide have the potential to inhibit estrogen receptor alpha (ERα), 17-beta-hydroxysteroid -dehydrogenase type 1 (17β-HSD1), topoisomerase II alpha (TOP2A) and p73 tetramerization domain that are expressed during breast cancer. The molecular dynamics (MD) simulations results suggested that Ashwagandhanolide remained inside the binding cavity of four targeted proteins and contributed favorably towards forming a stable protein-ligand complex throughout the simulation. Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties confirmed that Ashwagandhanolide is hydrophobic and has moderate intestinal permeability, good intestinal absorption, and poor skin permeability. The compound has a relatively low VDss value (-1.652) and can be transported across ABC transporter and good central nervous system (CNS) permeability but did not easily cross the blood-brain barrier (BBB). This compound does not possess any mutagenicity, hepatotoxicity and skin sensitization. Based on the results obtained, the present study highlights the anticancer potential of Ashwagandhanolide, a compound from W. somnifera. Furthermore, in vitro and in vivo studies are necessary to perform before clinical trials to prove the potentiality of Ashwagandhanolide.

In Vitro Anticancer Screening, Molecular Docking and Antimicrobial Studies of Triazole-Based Nickel(II) Metal Complexes

Herein we describe the synthesis of a series of nickel(II) complexes (C1–C3) with Schiff bases (HL1–HL3) derived from 4-amino-5-mercapto-3-methyl-1,2,4-triazole and ortho/meta/para-nitrobenzaldehyde having composition [Ni(L)₂(H₂O)₂]. The obtained ligands and their complexes were characterized using physico-chemical techniques viz., elemental analysis, magnetic moment study, spectral (electronic, FT-IR, 1H-NMR) and thermal analysis. The elemental analysis and spectral analysis revealed that Schiff bases behave as monoanionic bidentate ligands towards the Ni(II) ion. Whereas, the magnetic moment study suggested the octahedral geometry of all the Ni(II) complexes. The thermal behavior of the complexes has been studied by thermogravimetric analysis and agrees well with the composition of complexes. Further, the biological activities such as antimicrobial and antifungal studies of the Schiff bases and Ni(II) complexes have been screened against bacterial species (Staphylococcus aureus and Pseudomonas aeruginosa) and fungal species (Aspergillus niger and Candida albicans) activity by MIC method, the results of which revealed that metal complexes exhibited significant antimicrobial activities than their respective ligands against the tested microbial species. Furthermore, the molecular docking technique was employed to investigate the active sites of the selected protein, which indeed helped us to screen the potential anticancer agents among the synthesized ligand and complexes. Further, these compounds have been screened for their in vitro anticancer activity using OVCAR-3 cell line. The results revealed that the complexes are more active than the ligands.

Evaluating fast methods for static polarizabilities on extended conjugated oligomers

Given the importance of accurate polarizability calculations to many chemical applications, coupled with the need for efficiency when calculating the properties of sets of molecules or large oligomers, we present a benchmark study examining possible calculation methods for polarizable materials. We first investigate the accuracy of the additive model used in GFN2, a highly-efficient semi-empirical tight-binding method, and the D4 dispersion model, comparing its predicted additive polarizabilities to ωB97XD results for a subset of PubChemQC and a compiled benchmark set of molecules spanning polarizabilities from approximately 3 ų to 600 ų, with some compounds in the range of approximately 1200-1400 ų. Although we find additive GFN2 polarizabilities, and thus D4, to have large errors with polarizability calculations on large conjugated oligomers, it would appear an empirical quadratic correction can largely remedy this. We also compare the accuracy of DFT polarizability calculations run using basis sets of varying size and level of augmentation, determining that a non-augmented basis set may be used for large, highly polarizable species in conjunction with a linear correction factor to achieve accuracy extremely close to that of aug-cc-pVTZ.