Accurate conformation-dependent molecular electrostatic potentials for high-throughput in silico drug discovery

Three-Dimensional Quantitative Structure-Activity Relationship Study of Transient Receptor Potential Vanilloid 1 Channel Antagonists Reveals Potential for Drug Design Purposes

Transient receptor potential vanilloid 1 (TRPV1) was reported to be a putative target for recovery from chronic pain, producing analgesic effects after its inhibition. A series of drug candidates were previously developed, without the ability to ameliorate the therapeutic outcome. Starting from previously designed compounds, derived from the hybridization of antagonist SB-705498 and partial agonist MDR-652, we performed a virtual screening on a pharmacophore model built by exploiting the Cryo-EM 3D structure of a nanomolar antagonist in complex with the human TRPV1 channel. The pharmacophore model was described by three pharmacophoric features, taking advantage of both the bioactive pose of the antagonist and the receptor exclusion spheres. The results of the screening were implemented inside a 3D-QSAR model, correlating with the negative decadic logarithm of the inhibition rate of the ligands. After the validation of the obtained 3D-QSAR model, we designed a new series of compounds by introducing key modifications on the original scaffold. Again, we determined the compounds' binding poses after alignment to the pharmacophoric model, and we predicted their inhibition rates with the validated 3D-QSAR model. The obtained values resulted in being even more promising than parent compounds, demonstrating that ongoing research still leaves much room for improvement.

AdeIJK Pump-Specific Inhibitors Effective against Multidrug Resistant Acinetobacter baumannii

Multidrug-resistant Acinetobacter baumannii is a serious threat pathogen rapidly spreading in clinics and causing a range of complicated human infections. The major contributor to A. baumannii antibiotic resistance is the overproduction of AdeIJK and AdeABC multidrug efflux pumps of the resistance-nodulation-division (RND) superfamily of proteins. The dominant role of efflux in antibiotic resistance and the relatively high permeability of the A. baumannii outer membrane to amphiphilic compounds make this pathogen a promising target for the discovery of clinically relevant efflux pump inhibitors. In this study, we identified 4,6-diaminoquoniline analogs with inhibitory activities against A. baumannii AdeIJK efflux pump and followed up on these compounds with a focused synthetic program to improve the target specificity and to reduce cytotoxicity. We identified several candidates that potentiate antibacterial activities of antibiotics erythromycin, tetracycline, and novobiocin not only in the laboratory antibiotic susceptible strain A. baumannii ATCC17978 but also in multidrug-resistant clinical isolates AB5075 and AYE. The best analogs potentiated the activities of antibiotics in low micromolar concentrations, did not have antibacterial activities on their own, inhibited AdeIJK-mediated efflux of its fluorescent substrate ethidium ion, and had low cytotoxicity in A549 human lung epithelial cells.

A derivative of trihydroxynaphthalenone and a pyrone metabolite from the endophytic fungus Talaromyces purpurpgenus

A newly discovered trihydroxynaphthalenone derivative, epoxynaphthalenone (1) involving the condensation of ortho-hydroxyl groups into an epoxy structure, and a novel pyrone metabolite characterized as pyroneaceacid (2), were extracted from Talaromyces purpurpgenus, an endophytic fungus residing in Rhododendron molle. The structures of these compounds were elucidated through a comprehensive analysis of their NMR and HRESIMS data. The determination of absolute configurations was accomplished using electronic circular dichroism (ECD) calculations and CD spectra. Notably, these recently identified metabolites exhibited a moderate inhibitory activity against xanthine oxidase (XOD).

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Exploring torsional conformer space with physical prior mean function-driven meta-Gaussian processes

We present a novel approach for systematically exploring the conformational space of small molecules with multiple internal torsions. Identifying unique conformers through a systematic conformational search is important for obtaining accurate thermodynamic functions (e.g., free energy), encompassing contributions from the ensemble of all local minima. Traditional geometry optimizers focus on one structure at a time, lacking transferability from the local potential-energy surface (PES) around a specific minimum to optimize other conformers. In this work, we introduce a physics-driven meta-Gaussian processes (meta-GPs) method that not only enables efficient exploration of target PES for locating local minima but, critically, incorporates physical surrogates that can be applied universally across the optimization of all conformers of the same molecule. Meta-GPs construct surrogate PESs based on the optimization history of prior conformers, dynamically selecting the most suitable prior mean function (representing prior knowledge in Bayesian learning) as a function of the optimization progress. We systematically benchmarked the performance of multiple GP variants for brute-force conformational search of amino acids. Our findings highlight the superior performance of meta-GPs in terms of efficiency, comprehensiveness of conformer discovery, and the distribution of conformers compared to conventional non-surrogate optimizers and other non-meta-GPs. Furthermore, we demonstrate that by concurrently optimizing, training GPs on the fly, and learning PESs, meta-GPs exhibit the capacity to generate high-quality PESs in the torsional space without extensive training data. This represents a promising avenue for physics-based transfer learning via meta-GPs with adaptive priors in exploring torsional conformer space.

Computational prediction of analog compounds of the membrane protein MCHR1 antagonists ALB-127158 and KRX-104130

Obesity, which is already pervasive throughout the world, endangers public health by raising the prevalence of metabolic disorders and making their treatment more difficult. The development of drugs to treat obesity is a focus of effort. Melanin concentrated hormone receptor 1 (MCHR1) is the target of some of these therapeutic possibilities since as increased levels of melanin concentrated hormone have been found in obesity models. Known MCHR1 antagonists include BMS-830216, GW-856464, NGD-4715, ALB-127158, and AMG 076, but many have failed phase-I clinical studies. As a potential treatment for cardiotoxicity, KRX-104130 has only recently been identified. As MCH system is potentially effective target for treatment of obesity, in silico research into interaction between MCHR1 and its antagonists at molecular level was the primary goal of this study. Analogues ALB-127158 and KRX-104130 were screened among the RealEnamine library. The complexes obtained by molecular docking were embedded in mimics brain-cell membrane and simulated for 540 ns, and then MM-GBSA were calculated with MMPBSA.py. With all these computational studies, similar or different aspects of selected analogous compounds to ALB-127158 and KRX-104130 were investigated. The specificity of this study was that it analyzed MCHR1 protein as embedded in membrane. It was concluded that KRX-104130's analogue Z1922310273 and ALB-127158's analogue PV-002757495233 did not cause a difference in terms of phospholipid membrane properties. In addition, all ligands remained stable in putative binding site. It has been suggested that PV-002757495233 and Z1922310273 compounds can be evaluated as MCHR1 antagonists when all these outputs are considered in melting pots.

Aza-Michael Addition in Explicit Solvent: A Relative Energy Gradient-Interacting Quantum Atoms Study

Aza-Michael additions are key reactions in organic synthesis. We investigate, from a theoretical and computational point of view, several examples ranging from weak to strong electrophiles in dimethylsulfoxide treated as explicit solvent. We use the REG-IQA method, which is a quantum topological energy decomposition (Interacting Quantum Atoms, IQA) coupled to a chemical-interpretation calculator (Relative Energy Gradient, REG). We focus on the rate-limiting addition step in order to unravel the different events taking place in this step, and understand the influence of solvent on the reaction, with an eye on predicting the Mayr electrophilicity. For the first time, a link is established between an REG-IQA analysis and experimental values.

Is an Inductive Effect Explicit Account Required for Atomic Charges Aimed at Use within the Force Fields?

Polarization and inductive effects are the concepts that have been widely used in qualitative and even quantitative descriptions of experimentally observed properties in chemistry. The polarization effect has proven to be important in cases of biomolecular modeling though still the vast majority of molecular simulations use the classical non-polarizable force fields. In the last few decades, a lot of effort has been put into promoting the polarization effect and incorporating it into modern force fields and charge calculation methods. In contrast, the inductive effect has not attracted such attention and is effectively absent in both classic and modern force fields. Thus, a question is whether this difference corresponds to the difference in the physical significance of the effects and their explicit account, or is an artifact that should be corrected in the next generation of force fields. The significance of the electronic effects is studied in this paper through the prism of performance of specific models for atomic charge calculation that take into explicit account a nested set of effects: the formal charge, the nearest neighbors, the inductive effect, and finally the model, which takes into account all effects, which are possible to account for using atomic charges. The specific choice for the methods is the following: formal charges, MMFF94 bond charge increments, Dynamic Electronegativity Relaxation (DENR), and RESP. We propose a special scheme for the separate estimation of each particular effect contribution. By pairwise comparing the residual molecular electrostatic potential (MEP) errors of those charge models (aimed at best reproducing the quantum chemical reference MEP), we sequentially revealed how the account of each effect contributes to the better-quality MEP reproduction. The following relative importance of effects was estimated; thus, the natural hierarchy of the effects was established. First, the account of formal charges is of primordial importance. Second, the nearest neighbors account is the next in significance. Third, the explicit account of inductive effect in empirical charge calculation schemes was shown to significantly─both qualitatively and quantitatively─improve the quality of MEP reproduction. Fourth, the contribution of polarization is indirectly assessed. Surprisingly, it is of the order of magnitude of the inductive effect even for the molecular systems, for which it is anticipated to be more significant. Finally, the relative importance of anisotropic effects in neutral molecules was additionally reviewed.

Altereporenes A-E, five epoxy octa-hydronaphthalene polyketides produced by an endophytic fungus Alternaria sp. YUD20002

Five previously undescribed epoxy octa-hydronaphthalene polyketides, altereporenes A-E (1-5) were isolated from rice culture of the endophytic fungus Alternaria sp. YUD20002 derived from the tubers of Solanum tuberosum. Their structures were determined on the basis of comprehensive spectroscopic analyses, while the absolute configurations were elucidated by the comparison of experimental and calculated specific rotations. Meanwhile, the antimicrobial, cytotoxic, anti-inflammatory and acetylcholinesterase inhibitory activities of compounds 1-5 were also investigated.

High-Level Quantum Chemistry Reference Heats of Formation for a Large Set of C, H, N, and O Species in the NIST Chemistry Webbook and the Identification and Validation of Reliable Protocols for Their Rapid Computation

A recent study has examined the accuracy of NIST heats of formation for a set of C, H, and O-containing species with a proposed low-cost quantum chemistry approach. In the present study, we have used high-level methods such as W1X-2 to obtain these data more rigorously, which we have then used to assess the NIST and the previously computed values. We find that many of these NIST data that are as suggested to be unreliable by the previous study are indeed inconsistent with our high-level reference values. However, we also find substantial deviations for the previously computed values from our benchmark. Thus, we have assessed the performance of alternative low-cost methods. In our assessment, we have additionally examined C, H, N, and O-containing species for which heats of formation are available from the NIST database. We find the ωB97M-V/ma-def2-TZVP, DSD-PBEP86/ma-def2-TZVP, and CCSD(T)-F12b/aug'-cc-pVDZ methods to be adequate for obtaining heats of formation with the atomization approach, once their atomic energies are optimized with our benchmark. Notably, the low-cost ωB97M-V method yields values that agree to be within 10 kJ mol^-1 for more than 90% of the (∼1500) species. A higher 20 kJ mol^-1 threshold captures 98% of the data. The outlier species typically contain many electron-withdrawing (nitro) groups. In these cases, the use of isodesmic-type reactions rather than the atomization approach is more reliable. Our assessment has also identified significant outliers from the NIST database, for which experimental re-determination of the heats of formation would be desirable.

Variations of SARS-CoV-2 in the Iranian population and candidate putative drug-like compounds to inhibit the mutated proteins

The first cases of the novel coronavirus, SARS-CoV-2, were detected in December 2019 in Wuhan, China. Nucleotide substitutions and mutations in the SARS-CoV-2 sequence can result in the evolution of the virus and its rapid spread across the world. Therefore, understanding genetic variants of SARS-CoV-2 and targeting the conserved elements responsible for viral replication have great benefits for detecting its infection sources and diagnosing and treating COVID-19. In this study, we used the SARS-CoV-2 sequence isolated from a 59-year-old man in Ardabil, Iran, in April 2020 and sequenced using Oxford Nanopore technology. A meta-analysis comparing the sequence under study with other sequences from Iran indicated long nucleotide insertions/deletions (indels) that code for NSP15, the NSP14-NSP10 complex, open reading frame ORF9b, and ORF1ab polyproteins. In addition, replicating the NSP8 protein in the study sequence is another topic that can affect viral replication. Then using the DNA structure of NSP8, NSP15, NSP14-NSP10 complex, and ORF1ab as a genetic target can help find drug-like compounds for COVID-19. Potential drug-like compounds reported in this study for their mechanism of action and interactions with SARS-CoV-2 genes using drug repurposing are resveratrol, erythromycin, chloramphenicol, indomethacin, ciclesonide, and PDE4 inhibitor. Ciclesonide appears to show the best results when docked with chosen viral proteins. Therefore, different proteins isolated from nucleotide mutations in the virus sequence can indicate distinct inducers for antibodies and are important in vaccine design.

GEOM, energy-annotated molecular conformations for property prediction and molecular generation

Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble of 3D conformers that are accessible to a molecule. Property prediction could be improved by using conformer ensembles as input, but there is no large-scale dataset that contains graphs annotated with accurate conformers and experimental data. Here we use advanced sampling and semi-empirical density functional theory (DFT) to generate 37 million molecular conformations for over 450,000 molecules. The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133,000 species from QM9, and 317,000 species with experimental data related to biophysics, physiology, and physical chemistry. Ensembles of 1,511 species with BACE-1 inhibition data are also labeled with high-quality DFT free energies in an implicit water solvent, and 534 ensembles are further optimized with DFT. GEOM will assist in the development of models that predict properties from conformer ensembles, and generative models that sample 3D conformations.

Measurement(s)	Conformer geometries and properties
Technology Type(s)	Computational Chemistry

Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts

Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCS_N2 and CCS_He) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.

4-Phenylcoumarin (4-PC) Glucoside from Exostema caribaeum as Corrosion Inhibitor in 3% NaCl Saturated with CO2 in AISI 1018 Steel: Experimental and Theoretical Study

The corrosion inhibition of 5-O-β-D-glucopyranosyl-7-methoxy-3′,4′-dihydroxy-4-phenylcoumarin (4-PC) in AISI 1018 steel immersed in 3% NaCl + CO₂ was studied by electrochemical impedance spectroscopy (EIS). The results showed that, at just 10 ppm, 4-PC exerted protection against corrosion with ղ = 90% and 97% at 100 rpm. At static conditions, the polarization curves indicated that, at 5 ppm, the inhibitor presented anodic behavior, while at 10 and 50 ppm, there was a cathodic-type inhibitor. The inhibitor adsorption was demonstrated to be chemisorption, according to the Langmuir isotherm for 100 and 500 rpm. By means of SEM–EDS, the corrosion inhibition was demonstrated, as well as the fact that the organic compound was effective for up to 72 h of immersion. At static conditions, dispersion-corrected density functional theory results reveal that the chemical bonds established by the phenyl group of 4-PC are responsible of the chemisorption on the steel surface. According with Fukui reactivity indices, the molecules adsorbed on the metal surface provide a protective cover against nucleophilic and electrophilic attacks, pointing to the corrosion inhibition properties of 4-PC.

Computational Structural Revision of a 4-Hydroxy-3-(1'-angeloyloxy-2',3'-epoxy-3'-methyl)butylacetophenone Compound from Ageratina grandifolia

The structure of the reported compound 4-hydroxy-3-((S)-1'-angeloyloxy-(R)-2',3'-epoxy-3'-methyl)butylacetophenone (1), isolated from Ageratina grandifolia, has been revised through the use of DFT computational predictions. Re-examination of the reported experimental and DFT computed chemical shifts has led to the proposal of a chromane skeleton rather than the original epoxide derivative. Empirical predictions of the ¹³C and ¹H NMR shifts showed a much better fit for the chromane structure than for the epoxide. The relative configuration of the molecule was established using CASE-3D methodology on the basis of new DFT chemical shielding and J-coupling predictions, allowing the proposal of a new rel-2,2-dimethyl-3R-hydroxy-4S-(1-angeloyloxy)chromane structure (2) for the isolated compound. However, DFT prediction of the optical rotation for the CASE-3D selected configuration/conformations did not provide a conclusive answer for the absolute configuration.

Efficient Amino Acid Conformer Search with Bayesian Optimization

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.

Nanoparticles Incorporating a Fluorescence Turn-on Reporter for Real-Time Drug Release Monitoring, a Chemoenhancer and a Stealth Agent: Poseidon's Trident against Cancer?

The rate and extent of drug release under physiological conditions is a key factor influencing the therapeutic activity of a formulation. Real-time detection of drug release by conventional pharmacokinetics approaches is confounded by low sensitivity, particularly in the case of tissue-targeted novel drug delivery systems, where low concentrations of the drug reach systemic circulation. We present a novel fluorescence turn-on platform for real-time monitoring of drug release from nanoparticles based on reversible fluorescence quenching in fluorescein esters. Fluorescein-conjugated carbon nanotubes (CNTs) were esterified with methotrexate in solution and solid phase, followed by supramolecular functionalization with a chemoenhancer (suramin) or/and a stealth agent (dextran sulfate). Suramin was found to increase the cytotoxicity of methotrexate in A549 cells. On the other hand, dextran sulfate exhibited no effect on cytotoxicity or cellular uptake of CNTs by A549 cells, while a decrease in cellular uptake of CNTs and cytotoxicity of methotrexate was observed in macrophages (RAW 264.7 cells). Similar results were also obtained when CNTs were replaced with graphene. Docking studies revealed that the conjugates are not internalized by folate receptors/transporters. Further, docking and molecular dynamics studies revealed the conjugates do not exhibit affinity toward the methotrexate target, dihydrofolate reductase. Molecular dynamics studies also revealed that distinct features of dextran-CNT and suramin-CNT interactions, characterized by π-π interactions between CNTs and dextran/suramin. Our study provides a simple, cost-effective, and scalable method for the synthesis of nanoparticles conferred with the ability to monitor drug release in real-time. This method could also be extended to other drugs and other types of nanoparticles.

GPU algorithms for density matrix methods on MOPAC: linear scaling electronic structure calculations for large molecular systems

Purification of the density matrix methods should be employed when dealing with complex chemical systems containing many atoms. The running times for these methods scale linearly with the number of atoms if we consider the sparsity from the density matrix. Since the efficiency expected from those methods is closely tied to the underlying parallel implementations of the linear algebra operations (e.g., P² = P × P), we proposed a central processing unit (CPU) and graphics processing unit (GPU) parallel matrix-matrix multiplication in SVBR (symmetrical variable block row) format for energy calculations through the SP2 algorithm. This algorithm was inserted in MOPAC's MOZYME method, using the original LMO Fock matrix assembly, and the atomic integral calculation implemented on it. Correctness and performance tests show that the implemented SP2 is accurate and fast, as the GPU is able to achieve speedups up to 40 times for a water cluster system with 42,312 orbitals running in one NVIDIA K40 GPU card compared to the single-threaded version. The GPU-accelerated SP2 algorithm using the MOZYME LMO framework enables the calculations of semiempirical wavefunction with stricter SCF criteria for localized charged molecular systems, as well as the single-point energies of molecules with more than 100.000 LMO orbitals in less than 1 h. Graphical abstract Parallel CPU and GPU purification algorithms for electronic structure calculations were implemented in MOPAC's MOZYME method. Some matrices in these calculations, e.g., electron density P, are compressed, and the developed linear algebra operations deal with non-zero entries only. We employed the NVIDIA/CUDA platform to develop GPU algorithms, and accelerations up to 40 times for larger systems were achieved.

Modified Fusicoccane-Type Diterpenoids from Alternaria brassicicola

Seven new modified fusicoccane-type diterpenoids (1-7), together with two known congeners (8 and 9), were obtained from Alternaria brassicicola. Their structures were elucidated from a combination of NMR and HRESIMS data and ¹³C NMR calculation as well as DP4+ probability analyses, and the absolute configurations of 1-5 were determined by ECD calculation and single-crystal X-ray diffraction (Cu Kα). Compounds 1-3 belong to a rare class of 16-nor-dicyclopenta[a,d]cyclooctane diterpenoids, and compounds 2 and 4 represent the first examples of fusicoccane-type diterpenoids featuring two previously undescribed tetracyclic 5/6/6/5 ring systems, while compound 5 features a previously undescribed tetracyclic 5/8/5/3 ring system. Compound 7 was moderately anti-inflammatory, and compounds 2, 3, 5, and 7 were weakly cytotoxic.

Cytotoxic Limonoids from the Twigs and Leaves of Toona ciliata

Eight new limonoids, toononoids A-H (1-8), eight new B-seco-29-norlimonoids, toonanoronoids A-H (9-16), and seven known analogues were obtained from the EtOAc extract of the twigs and leaves of Toona ciliata. Compounds 2, 4, 8, and 16 are rare lactam-bearing limonoids. Compounds 1, 14, and 15 possess an unusual γ-methoxybutenolide moiety at C-17, while compounds 9, 10, and 15 have a rare 3β-hydroxy group. Their 2D structure and relative configurations were identified using spectroscopic data. The absolute configurations of 1, 9, 14, and 15 were established via X-ray diffraction crystallography or comparison of experimental and calculated ECD data. The cytotoxicity of the compounds was assessed toward five human tumor cell lines, and their anti-inflammatory activity was assessed based on NO production using LPS-stimulated RAW264.7 macrophages. Compounds 11 and 12 exerted inhibition toward two tumor cell lines (MCF-7, SW-480) with IC₅₀ values between 2.1 and 3.7 μM, while 18-22 inhibited the proliferation of HL-60, MCF-7, and SW-480 cells (IC₅₀ 0.6-4.0 μM). Only compound 4 exhibited weak anti-inflammatory activity with an IC₅₀ value of 28.3 μM.

Mapping the Site of Action of Human P2X7 Receptor Antagonists AZ11645373, Brilliant Blue G, KN-62, Calmidazolium, and ZINC58368839 to the Intersubunit Allosteric Pocket

The P2X7 receptor is a trimeric ligand-gated ion channel activated by ATP. It is implicated in the cellular response to trauma/disease and considered to have significant therapeutic potential. Using chimeras and point mutants we have mapped the binding site of the P2X7R-selective antagonist AZ11645373 to the known allosteric binding pocket at the interface between two subunits, in proximity to, but separated from the ATP binding site. Our structural model of AZ11645373 binding is consistent with effects of mutations on antagonist sensitivity, and the proposed binding mode explains variation in antagonist sensitivity between the human and rat P2X7 receptors. We have also determined the site of action for the P2X7R-selective antagonists ZINC58368839, brilliant blue G, KN-62, and calmidazolium. The effect of intersubunit allosteric pocket “signature mutants” F88A, T90V, D92A, F103A, and V312A on antagonist sensitivity suggests that ZINC58368839 comprises a binding mode similar to AZ11645373 and other previously characterized antagonists. For the larger antagonists, brilliant blue G, KN-62, and calmidazolium, our data imply an overlapping but distinct binding mode involving the central upper vestibule of the receptor in addition to the intersubunit allosteric pocket. Our work explains the site of action for a series of P2X7R antagonists and establishes “signature mutants” for P2X7R binding-mode characterization.

Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening

Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library preparation have restrictive and/or expensive licenses. Freely available alternatives, though often effective, do not fully account for all possible ionization, tautomeric, and ring-conformational variants. We here present Gypsum-DL, a free, robust open-source program that addresses these challenges. As input, Gypsum-DL accepts virtual compound libraries in SMILES or flat SDF formats. For each molecule in the virtual library, it enumerates appropriate ionization, tautomeric, chiral, cis/trans isomeric, and ring-conformational forms. As output, Gypsum-DL produces an SDF file containing each molecular form, with 3D coordinates assigned. To demonstrate its utility, we processed 1558 molecules taken from the NCI Diversity Set VI and 56,608 molecules taken from a Distributed Drug Discovery (D3) combinatorial virtual library. We also used 4463 high-quality protein–ligand complexes from the PDBBind database to show that Gypsum-DL processing can improve virtual-screening pose prediction. Gypsum-DL is available free of charge under the terms of the Apache License, Version 2.0.

Phenolic C-Glycosides and Aglycones from Marine-Derived Aspergillus sp. and Their Anti-Inflammatory Activities

A chemical investigation of the secondary metabolites of a marine-derived Aspergillus sp. led to the isolation and characterization of 13 phenolic compounds, including 10 new compounds (1-10). Seven new compounds (1-7) are unusual phenolic C-glycosides, while the other new compounds (8-10) are structurally related aglycones. The chemical structures of these new compounds were elucidated by 1D and 2D NMR and HRESIMS spectroscopic analyses. The absolute configurations of these new C-glycosides were determined by comparison of experimental electronic circular dichroism spectra with those of calculated ones. In addition, the anti-inflammatory activities of these compounds were evaluated, and compound 9 significantly inhibited nitric oxide production with an IC₅₀ value of 6.0 ± 0.5 μM in lipopolysaccharide-induced RAW264.7 cells. Moreover, compound 9 also showed anti-inflammatory activity by inhibiting the NF-κB-activated pathway.

Bayesian optimization for conformer generation

Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian optimization algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation, and torsion fingerprint deviation are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{4}$$\end{document}104 (median) conformers in its search, while BOA only requires \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10^{2}$$\end{document}102 energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20–40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds.

Iterative Solvers for Empirical Partial Atomic Charges: Breaking the Curse of Cubic Numerical Complexity

Rational drug design involves a vast amount of computations to get thermodynamically reliable results and often relies on atomic charges as a means to model electrostatic interactions within the system. Computational inefficiency often hampers the development of new and wider dissemination of the known methods; thus, any source to speed up the calculations without a sacrifice in quality is warranted. At the heart of many empirical methods of calculating atomic charges is the solution of a system of linear algebraic equations (SLAE). The classical method of solving SLAE-the Gauss method-has in general case a cubic computational complexity. It is shown that the use of iterative methods for solving SLAE, characteristic to typical empirical atomic charge calculation methods, makes it possible to significantly reduce the amount of calculations and to obtain a computational complexity approaching a quadratic one. Despite the fact that this phenomenon is well-known in numerical methods, iterative solvers surprisingly do not seem to have been systematically applied to calculation of atomic charges via empirical schemes. Another finding is the relative values of the matrix elements, determined by the physical grounds of the interactions within the empirical system, generally lead to SLAE's with well-defined matrices, suited to use with iterative solvers to fasten computation compared to using the noniterative solvers. This finding broadens the applicability range of atomic charges obtained with empirical methods for such cases as, e.g., account of polarizability via "on-the-fly" recalculation of charges in changing surroundings within the force fields in molecular dynamics settings.

Towards identifying potent new hits for glioblastoma

Glioblastoma is a devastating disease of the brain and is the most common malignant primary brain tumour in adults. The prognosis for patients is very poor with median time of survival after diagnosis measured in months, due in part to the tumours being highly aggressive and often resistant to chemotherapies. Alongside the ongoing research to identify key factors involved in tumour progression in glioblastoma, medicinal chemistry approaches must also be used in order to rapidly establish new and better treatments for brain tumour patients. Using a computational similarity search of the ZINC database, alongside traditional analogue design by medicinal chemistry intuition to improve the breadth of chemical space under consideration, six new hit compounds (14, 16, 18, 19, 20 and 22) were identified possessing low micromolar activity against both established cell lines (U87MG and U251MG) and patient-derived cell cultures (IN1472, IN1528 and IN1760). Each of these scaffolds provides a new platform for future development of a new therapy in this area, with particular promise shown against glioblastoma subtypes that are resistant to conventional chemotherapeutic agents.

Novel Consensus Docking Strategy to Improve Ligand Pose Prediction

Molecular docking, which mainly includes pose prediction and binding affinity calculation, has become an important tool for assisting structure-based drug design. Correctly predicting the ligand binding pose to a protein target enables the estimation of binding free energy using various tools. Previous studies have shown that the consensus method can be used to improve the docking performance with respect to compound scoring and pose prediction. In this report, a novel consensus docking strategy was proposed, which uses a dynamic benchmark data set selection to determine the best program combinations to improve the docking success rate. Using the complexes from PDBbind as a benchmark data set, a 4.9% enhancement in success rate was achieved compared with the best program.

Shape similarity guided pose prediction: lessons from D3R Grand Challenge 3

To extend the utility of ligand 3D shape similarity into pose prediction and virtual screening, we have previously developed CDVS and PoPSS methods. Both of them utilize ligand 3D shape similarity with the crystallographic ligands to improve pose prediction. While CDVS utilizes shape similarity to select suitable receptor structures for molecular docking, PoPSS places a ligand conformation of the highest shape similarity with crystal ligands into the target protein binding pocket which is then refined by side-chain repacking and Monte Carlo energy minimization. Analyses of PoPSS revealed some drawbacks in ligand conformation generation and the scoring scheme used. Moreover, as PoPSS does not sample the ligand conformation after placing it in the binding pocket, it relies solely on conformation generation methods to produce native like conformations. To address these limitations of PoPSS method, we report here a modified approach named as PoPSS-Lite, where side-chain repacking was replaced by a simple grid-based energy minimization. This modification also allowed the sampling of terminal functional groups while keeping the core scaffold fixed. Furthermore, shape similarity calculations were improved by increasing the number of ligand conformations and using a different similarity metric. The performance of PoPSS-Lite was prospectively evaluated in D3R GC3. Comparison of PoPSS-Lite demonstrated superior performance over PoPSS and CDVS with lower mean and median RMSDs. Furthermore, comparison with other D3R GC3 pose prediction submissions revealed top performance for PoPSS-Lite. Our D3R GC3 result extends our perspective that ligand 3D shape similarity with known crystallographic information can be successfully used to predict the binding pose of ligands with unknown binding modes. Our D3R GC3 results further highlight the necessity for improvement in conformer generation methods in order to improve shape similarity guided pose prediction.

Protoilludane, Illudalane, and Botryane Sesquiterpenoids from the Endophytic Fungus Phomopsis sp. TJ507A

To explore the chemical diversity of metabolites from endophytic fungi, the strain Phomopsis sp. TJ507A, isolated from the medicinal plant Phyllanthus glaucus, was investigated. A 2,3- seco-protoilludane-type sesquiterpenoid (1), eight protoilludane-type sesquiterpenoids (2-9), four illudalane-type sesquiterpenoids (10a/10b, 11, and 12), and a botryane-type sesquiterpenoid (13) in addition to seven known sesquiterpenoids (14-20) were identified from the liquid culture of the fungus. Structures of the isolated compounds, including their absolute configurations, were elucidated based on extensive spectroscopic analyses, a modified Mosher analysis, electronic circular dichroism (ECD) calculations, and [Rh₂(OCOCF₃)₄]-induced ECD spectra as well as X-ray crystallographic analyses. Compound 1 represents the first example of a naturally occurring sesquiterpenoid containing the unusual 2,3- seco-protoilludane scaffold. Compounds 1 ( p < 0.001); 2-6, 15, and 18 ( p < 0.01); and 7, 9, and 20 ( p < 0.05) displayed β-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitory activities ranging from 19.4% to 43.8% at the concentration of 40 μM. LY2811376 was used as the positive control with an inhibitory activity of 38.6% ( p < 0.01). Furthermore, none of these compounds showed obvious hepatotoxicity at concentration of 40 μM.

Two New Terpenoids from Talaromyces purpurogenus

A new sesquiterpenoid 9,10-diolhinokiic acid (1) and a new diterpenoid roussoellol C (2), together with 4 known compounds, were isolated from the extracts of laboratory cultures of marine-derived fungus Talaromyces purpurogenus. 9,10-diolhinokiic acid is the first thujopsene-type sesquiterpenoid containing a 9,10-diol moiety, and roussoellol C possesses a novel tetracyclic fusicoccane framework with an unexpected hydroxyl at C-4. These new structures were confirmed by spectroscopic data, chemical method, NMR data calculations and electronic circular dichroism (ECD) calculations. The selected compounds were evaluated for cytotoxicities against five human cancer cell lines, including SW480, HL-60, A549, MCF-7, and SMMC-7721 and the IC₅₀ values of compound 2 against MCF-7 and 3 against HL-60 cells were 6.5 and 7.9 μM, respectively.

Aspermerodione, a novel fungal metabolite with an unusual 2,6-dioxabicyclo[2.2.1]heptane skeleton, as an inhibitor of penicillin-binding protein 2a

Rising drug resistance limits the treatment options infected by methicillin-resistant Staphylococcus aureus (MRSA). A promising solution for overcoming the resistance of MRSA is to inhibit the penicillin-binding protein 2a (PBP2a). A novel terpene-polyketide hybrid meroterpenoid, aspermerodione (1), characterized by an unusual 2,6-dioxabicyclo[2.2.1]heptane core skeleton, and a new heptacyclic analogue, andiconin C (2), were isolated and identified from the liquid cultures of endophytic fungus Aspergillus sp. TJ23. The structures and their absolute configurations of all chiral centers were elucidated via extensive spectroscopic analyses and electronic circular dichroism (ECD) calculations and determined via single-crystal X-ray diffraction analysis. Aspemerodione (1) was found to be a potential inhibitor of PBP2a, and work synergistically with the β-lactam antibiotics oxacillin and piperacillin against MRSA.

Discovery by organism based high-throughput screening of new multi-stage compounds affecting Schistosoma mansoni viability, egg formation and production

Schistosomiasis, one of the most prevalent neglected parasitic diseases affecting humans and animals, is caused by the Platyhelminthes of the genus Schistosoma. Schistosomes are the only trematodes to have evolved sexual dimorphism and the constant pairing with a male is essential for the sexual maturation of the female. Pairing is required for the full development of the two major female organs, ovary and vitellarium that are involved in the production of different cell types such as oocytes and vitellocytes, which represent the core elements of the whole egg machinery. Sexually mature females can produce a large number of eggs each day. Due to the importance of egg production for both life cycle and pathogenesis, there is significant interest in the search for new strategies and compounds not only affecting parasite viability but also egg production. Here we use a recently developed high-throughput organism-based approach, based on ATP quantitation in the schistosomula larval stage of Schistosoma mansoni for the screening of a large compound library, and describe a pharmacophore-based drug selection approach and phenotypic analyses to identify novel multi-stage schistosomicidal compounds. Interestingly, worm pairs treated with seven of the eight compounds identified show a phenotype characterized by defects in eggshell assemblage within the ootype and egg formation with degenerated oocytes and vitelline cells engulfment in the uterus and/or oviduct. We describe promising new molecules that not only impair the schistosomula larval stage but also impact juvenile and adult worm viability and egg formation and production in vitro.

Schistosomiasis is a neglected disease caused by parasitic flatworms called schistosomes. The disease affects hundreds of millions of people in developing countries in the poorest tropical and subtropical regions of the world and it represents a major public health and socio-economical problem in several countries. In humans, these blood flukes reside in the mesenteric and vesicle venules. They have a life span of many years and produce hundreds of eggs daily, which are able to pass through the gut lumen or the bladder to be finally excreted into the environment for maintaining the life cycle. Part of the eggs can be trapped in host tissues inducing immunologically mediated granulomatous inflammation and fibrosis leading eventually to severe sequelae such as hepatosplenomegaly and even death. Importantly, schistosome infections increase susceptibility to other parasitic, bacterial and viral diseases. To date, essentially a single drug, praziquantel, is available to treat this parasitic disease. Despite its high tolerability and efficacy against adult parasites it has an incomplete efficacy across all stages of the S. mansoni life cycle and it does not prevent reinfection. Moreover the potential risk of drug resistance is an increasing concern. In search of novel schistosomicidal molecules we screened a large compound collection using the schistosomula, larval stage of the parasite. We identified eight novel molecules able to impair viability of schistosomula, juvenile and adult worms and also egg formation and production, two important features required for both disease transmission and progression.

Tricyclic Polyprenylated Acylphloroglucinols from St John's Wort, Hypericum perforatum

The new polyprenylated acylphloroglucinol derivatives 1-15 and the known furohyperforin (16) were isolated from the stems and leaves of Hypericum perforatum. Their structures were determined by analyses of NMR and HRESIMS data. Their absolute configurations were elucidated by a combination of electronic circular dichroism (ECD) and Rh₂(OCOCF₃)₄-induced ECD, as well as X-ray diffraction crystallography. The new hyperforatin F (9) contains a unique acetyl functionality at C-1 of the bicyclo[3.3.1]nonane core. Hyperforatins G (10) and H (11) are similarly the first examples of naturally occurring [3.3.1]-type polycyclic prenylated acylphloroglucinols possessing a carbonyl functionality at C-32. The compounds were tested for their acetylcholinesterase (AChE) inhibitory activities and cytotoxic activities against a panel of human tumor cell lines. Compounds 3, 5, 6, 8, and 9 exerted moderate inhibitory activities (IC₅₀ 3.98-9.13 μM) against AChE.

High-quality and universal empirical atomic charges for chemoinformatics applications

BACKGROUND

Partial atomic charges describe the distribution of electron density in a molecule and therefore provide clues to the chemical behaviour of molecules. Recently, these charges have become popular in chemoinformatics, as they are informative descriptors that can be utilised in pharmacophore design, virtual screening, similarity searches etc. Especially conformationally-dependent charges perform very successfully. In particular, their fast and accurate calculation via the Electronegativity Equalization Method (EEM) seems very promising for chemoinformatics applications. Unfortunately, published EEM parameter sets include only parameters for basic atom types and they often miss parameters for halogens, phosphorus, sulphur, triple bonded carbon etc. Therefore their applicability for drug-like molecules is limited.

RESULTS

We have prepared six EEM parameter sets which enable the user to calculate EEM charges in a quality comparable to quantum mechanics (QM) charges based on the most common charge calculation schemes (i.e., MPA, NPA and AIM) and a robust QM approach (HF/6-311G, B3LYP/6-311G). The calculated EEM parameters exhibited very good quality on a training set ([Formula: see text]) and also on a test set ([Formula: see text]). They are applicable for at least 95 % of molecules in key drug databases (DrugBank, ChEMBL, Pubchem and ZINC) compared to less than 60 % of the molecules from these databases for which currently used EEM parameters are applicable.

CONCLUSIONS

We developed EEM parameters enabling the fast calculation of high-quality partial atomic charges for almost all drug-like molecules. In parallel, we provide a software solution for their easy computation (http://ncbr.muni.cz/eem_parameters). It enables the direct application of EEM in chemoinformatics.

AtomicChargeCalculator: interactive web-based calculation of atomic charges in large biomolecular complexes and drug-like molecules

Background

Partial atomic charges are a well-established concept, useful in understanding and modeling the chemical behavior of molecules, from simple compounds, to large biomolecular complexes with many reactive sites.

Results

This paper introduces AtomicChargeCalculator (ACC), a web-based application for the calculation and analysis of atomic charges which respond to changes in molecular conformation and chemical environment. ACC relies on an empirical method to rapidly compute atomic charges with accuracy comparable to quantum mechanical approaches. Due to its efficient implementation, ACC can handle any type of molecular system, regardless of size and chemical complexity, from drug-like molecules to biomacromolecular complexes with hundreds of thousands of atoms. ACC writes out atomic charges into common molecular structure files, and offers interactive facilities for statistical analysis and comparison of the results, in both tabular and graphical form.

Conclusions

Due to high customizability and speed, easy streamlining and the unified platform for calculation and analysis, ACC caters to all fields of life sciences, from drug design to nanocarriers. ACC is freely available via the Internet at http://ncbr.muni.cz/ACC.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0099-x) contains supplementary material, which is available to authorized users.

Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

TIPdb-3D: the three-dimensional structure database of phytochemicals from Taiwan indigenous plants

The rich indigenous and endemic plants in Taiwan serve as a resourceful bank for biologically active phytochemicals. Based on our TIPdb database curating bioactive phytochemicals from Taiwan indigenous plants, this study presents a three-dimensional (3D) chemical structure database named TIPdb-3D to support the discovery of novel pharmacologically active compounds. The Merck Molecular Force Field (MMFF94) was used to generate 3D structures of phytochemicals in TIPdb. The 3D structures could facilitate the analysis of 3D quantitative structure–activity relationship, the exploration of chemical space and the identification of potential pharmacologically active compounds using protein–ligand docking.

Database URL: http://cwtung.kmu.edu.tw/tipdb.