Evaluation of the activity of cardiac glycosides on RORγ and RORγT nuclear receptors.

Cardiac glycosides, derived from plants and animals, have been recognized since ancient times. These substances hinder the function of the sodium-potassium pump within eukaryotic cells. Many reports have shown that these compounds influence the activity of nuclear receptors. Thus, we assessed the effects of various cardiac glycosides at nontoxic concentrations on RORγ and RORγT. RORγT is a crucial protein involved in the differentiation of Th17 lymphocytes. Sixteen analyzed cardiac glycosides exhibited varying toxicities in HepG2 cells, all of which demonstrated agonistic effects on RORγ, as confirmed in the RORγ-HepG2 reporter cell line. The overexpression of both the RORγ and RORγT isoforms intensified the effects of these compounds. Additionally, these glycosides induced the expression of G6PC, a gene regulated by RORγ, in HepG2 cells. Subsequently, the effects of two endogenous cardiac glycosides (marinobufagenin and ouabain) and the three most potent glycosides (bufalin, oleandrin, and telecinobufagenin) were evaluated in Th17 primary lymphocytes. All of these compounds increased the expression of the IL17A, IL17F, IFNG, and CXCL10 genes, but they exhibited varying effects on GZMB and CCL20 expression. Molecular docking analysis revealed the robust binding affinity of cardiac glycosides for the ligand binding domain of the RORγ/RORγT receptors. Thus, we demonstrated that at nontoxic concentrations, cardiac glycosides have agonistic effects on RORγ/RORγT nuclear receptors, augmenting their activity. This potential can be harnessed to modulate the phenotype of IL17-expressing cells (e.g., Th17 or Tc17 lymphocytes) in adoptive therapy for combating various types of cancer.

Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains.

Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 1023 and 1060. The use of molecular docking algorithms can help in new drug development by sieving out the worst drug-receptor complexes. New chemical spaces can be efficiently searched with the application of artificial intelligence. From that, new structures can be proposed. The research proposed aims to create new chemical structures supported by a deep neural network that will possess an affinity to the selected protein domains. Transferring chemical structures into SELFIES codes helped us pass chemical information to a neural network. On the basis of vectorized SELFIES, new chemical structures can be created. With the use of the created neural network, novel compounds that are chemically sensible can be generated. Newly created chemical structures are sieved by the quantitative estimation of the drug-likeness descriptor, Lipinski's rule of 5, and the synthetic Bayesian accessibility classifier score. The affinity to selected protein domains was verified with the use of the AutoDock tool. As per the results, we obtained the structures that possess an affinity to the selected protein domains, namely PDB IDs 7NPC, 7NP5, and 7KXD.

Artificial Intelligence in Decrypting Cytoprotective Activity under Oxidative Stress from Molecular Structure.

Artificial intelligence (AI) is widely explored nowadays, and it gives opportunities to enhance classical approaches in QSAR studies. The aim of this study was to investigate the cytoprotective activity parameter under oxidative stress conditions for indole-based structures, with the ultimate goal of developing AI models capable of predicting cytoprotective activity and generating novel indole-based compounds. We propose a new AI system capable of suggesting new chemical structures based on some known cytoprotective activity. Cytoprotective activity prediction models, employing algorithms such as random forest, decision tree, support vector machines, K-nearest neighbors, and multiple linear regression, were built, and the best (based on quality measurements) was used to make predictions. Finally, the experimental evaluation of the computational results was undertaken in vitro. The proposed methodology resulted in the creation of a library of new indole-based compounds with assigned cytoprotective activity. The other outcome of this study was the development of a validated predictive model capable of estimating cytoprotective activity to a certain extent using molecular structure as input, supported by experimental confirmation.

Identification of Corosolic and Oleanolic Acids as Molecules Antagonizing the Human RORγT Nuclear Receptor Using the Calculated Fingerprints of the Molecular Similarity.

RORγT is a protein product of the RORC gene belonging to the nuclear receptor subfamily of retinoic-acid-receptor-related orphan receptors (RORs). RORγT is preferentially expressed in Th17 lymphocytes and drives their differentiation from naive CD4+ cells and is involved in the regulation of the expression of numerous Th17-specific cytokines, such as IL-17. Because Th17 cells are implicated in the pathology of autoimmune diseases (e.g., psoriasis, inflammatory bowel disease, multiple sclerosis), RORγT, whose activity is regulated by ligands, has been recognized as a drug target in potential therapies against these diseases. The identification of such ligands is time-consuming and usually requires the screening of chemical libraries. Herein, using a Tanimoto similarity search, we found corosolic acid and other pentacyclic tritepenes in the library we previously screened as compounds highly similar to the RORγT inverse agonist ursolic acid. Furthermore, using gene reporter assays and Th17 lymphocytes, we distinguished compounds that exert stronger biological effects (ursolic, corosolic, and oleanolic acid) from those that are ineffective (asiatic and maslinic acids), providing evidence that such combinatorial methodology (in silico and experimental) might help wet screenings to achieve more accurate results, eliminating false negatives.

Synthesis of naphthalimide-carborane and metallacarborane conjugates: Anticancer activity, DNA binding ability.

The development of 1,8-naphthalimide derivatives as DNA-targeting anticancer agents is a rapidly growing area and has resulted in several derivatives entering into clinical trials. One of original recent developments is the use of boron clusters: carboranes and metallacarboranes in the design of pharmacologically active molecules. In this direction several naphthalimide-carborane and metallacarborane conjugates were synthesized in the present study. Their effect on a cancer cell line - cytotoxicity, type of cell death, cell cycle, and ROS production were investigated. The tested conjugates revealed different activities than the leading members of the naphthalimides family, namely mitonafide and pinafide. These derivatives could induce G0/G1 arrest and promote mainly apoptosis in HepG2 cell line. Our investigations demonstrated that the most promising molecule is N-{[2-(3,3'-commo-bis(1,2-dicarba-3-cobalta(III)-closo-dodecaborate-1-yl)ethyl]-1'-aminoethyl)}-1,8-naphthalimide] (17). It was shown that 17 exhibited cytotoxicity against HepG2 cells, activated cell apoptosis, and caused cell cycle arrest in HepG2 cells. Further investigations in HepG2 cells revealed that compound 17 can also induce ROS generation, particularly mitochondrial ROS (mtROS), which was also proved by increased 8-oxo-dG level in DNA. Additionally to biological assays the interaction of the new compounds with ct-DNA was studied by CD spectra and melting temperature, thus demonstrating that these compounds were rather weak classical DNA intercalators.

The Dichotomous Nature of AZ5104 (an EGFR Inhibitor) Towards RORγ and RORγT.

The RORC (RAR related orphan receptor C) gene produces two isoforms by alternative promoter usage: RORγ (nuclear receptor ROR-gamma isoform 1) and RORγT (nuclear receptor ROR-gamma isoform 1). Both proteins have distinct tissue distributions and are involved in several physiological processes, including glucose/lipid metabolism and the development of Th17 lymphocytes. Previously, we developed a stably transfected reporter cell line and used it to screen a library of kinase inhibitors. We found that AZ5104 acts as an RORγ agonist at low micromolar concentrations. Molecular docking analysis showed that this compound occupies the ligand binding domain of the receptor with a significant docking score. However, analysis of the biological activity of this compound in Th17 cells revealed that it downregulates RORγT expression and Th17-related cytokine production via inhibition of SRC-ERK-STAT3 (SRC proto-oncogene - extracellular regulated MAP kinase - signal transducer and activator of transcription 3). We thus identified a compound acting as an agonist of RORγ that, due to the inhibition of downstream elements of EGFR (epidermal growth factor receptor) signaling, exerts different biological activity towards a Th17-specific isoform. Additionally, our results may be relevant in the future for the design of treatments targeting signaling pathways that inhibit Th17-related inflammation in certain autoimmune disorders.

Machine Learning Application for Medicinal Chemistry: Colchicine Case, New Structures, and Anticancer Activity Prediction.

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure-activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the IC50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an IC50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating IC50 values based on molecular structure input.

QSPRmodeler - An open source application for molecular predictive analytics.

The drug design process can be successfully supported using a variety of in silico methods. Some of these are oriented toward molecular property prediction, which is a key step in the early drug discovery stage. Before experimental validation, drug candidates are usually compared with known experimental data. Technically, this can be achieved using machine learning approaches, in which selected experimental data are used to train the predictive models. The proposed Python software is designed for this purpose. It supports the entire workflow of molecular data processing, starting from raw data preparation followed by molecular descriptor creation and machine learning model training. The predictive capabilities of the resulting models were carefully validated internally and externally. These models can be easily applied to new compounds, including within more complex workflows involving generative approaches.

RORγT agonists as immune modulators in anticancer therapy.

RORγT is a transcription factor that directs the development of Th17 lymphocytes and other IL-17-expressing cells (e.g., Tc17 and ILC3 cells). These cells are involved in the body's defense against pathogenic bacteria and fungi, but they also participate in maintaining the proinflammatory environment in some autoimmune diseases and play a role in the immune system's response to cancer. Similar to other members of the nuclear receptor superfamily, the activity of RORγT is regulated by low-molecular-weight ligands. Therefore, extensive efforts have been dedicated to identifying inverse agonists that diminish the activity of this receptor and subsequently inhibit the development of autoimmune diseases. Unfortunately, in the pursuit of an ideal inverse agonist, the development of agonists has been overlooked. It is important to remember that these types of compounds, by stimulating lymphocytes expressing RORγT (Th17 and Tc17), can enhance the immune system's response to tumors. In this review, we present recent advancements in the biology of RORγT agonists and their potential application in anticancer therapy.

The application of machine learning methods to the prediction of novel ligands for RORγ/RORγT receptors.

In this work, we developed and applied a computational procedure for creating and validating predictive models capable of estimating the biological activity of ligands. The combination of modern machine learning methods, experimental data, and the appropriate setup of molecular descriptors led to a set of well-performing models. We thoroughly inspected both the methodological space and various possibilities for creating a chemical feature space. The resulting models were applied to the virtual screening of the ZINC20 database to identify new, biologically active ligands of RORγ receptors, which are a subfamily of nuclear receptors. Based on the known ligands of RORγ, we selected candidates and calculate their predicted activities with the best-performing models. We chose two candidates that were experimentally verified. One of these candidates was confirmed to induce the biological activity of the RORγ receptors, which we consider proof of the efficacy of the proposed methodology.

Anti-hepatocellular carcinoma activity of the cyclin-dependent kinase inhibitor AT7519.

Hepatocellular carcinoma (HCC) is one of the most common cancerous tumors and one of the leading causes of death among cancer-related disorders. Chemotherapy is ineffective in HCC patients, and the number of drugs that are in use is limited. Thus, new molecules are needed that could increase the effectiveness of anti-HCC regimens. Here, we show that AT7519, a CDK inhibitor, exerts positive effects on HCC cells: it inhibits proliferation, migration and clonogenicity. Detailed analysis of the transcriptomes of cells treated with this compound indicated that AT7519 affects a substantial portion of genes that are associated with HCC development and progression. Moreover, we showed that the concomitant use of AT7519 with gefitinib or cabozantinib sensitized HCC cells to these drugs. Thus, our research indicates that AT7519 is worth considering in monotherapy for hepatocellular carcinoma patients or in combination with other drugs, e.g., gefitinib or cabozantinib.

Predicting a Time-Dependent Quantity Using Recursive Generative Query Network.

We propose here a novel neural architecture dedicated to the prediction of time series. It can be considered as an adaptation of the idea of (GQN) to the data which is of a sequence nature. The new approach, dubbed here as the (RGQN), allows for efficient prediction of time series. The predictor information (i.e. the independent variable) is one or more of the other time series which are in some relationship with the predicted sequence. Each time series is accompanied by additional meta-information reflecting its selected properties. This meta-information, together with the standard dynamic component, is provided simultaneously in (RNN). During the inference phase, meta-information becomes a query reflecting the expected properties of the predicted time series. The proposed idea is illustrated with use cases of strong practical relevance. In particular, we discuss the example of an industrial pipeline that transports liquid media. The trained RGQN model is applied to predict pressure signals, assuming that the training was carried out during routine operational conditions. The subsequent comparison of the prediction with the actual data gathered under extraordinary circumstances, e.g. during the leakage, leads to a specific residual distribution of the prediction. This information can be applied directly within the data-driven Leak Detection and Location framework. The RGQN approach can be applied not only to pressure time series but also in many other use cases where the quantity of sequence nature is accompanied by a meta-descriptor.

The MP2-F12 method in the Turbomole program package.

A detailed description of the explicitly correlated second-order Møller-Plesset perturbation theory (MP2-F12) method, as implemented in the TURBOMOLE program package, is presented. The TURBOMOLE implementation makes use of density fitting, which greatly reduces the prefactor for integral evaluation. Methods are available for the treatment of ground states of open- and closed-shell species, using unrestricted as well as restricted (open-shell) Hartree-Fock reference determinants. Various methodological choices and approximations are discussed. The performance of the TURBOMOLE implementation is illustrated by example calculations of the molecules leflunomide, prednisone, methotrexate, ethylenedioxytetrafulvalene, and a cluster model for the adsorption of methanol on the zeolite H-ZSM-5. Various basis sets are used, including the correlation-consistent basis sets specially optimized for explicitly correlated calculations (cc-pVXZ-F12).

Large-amplitude vibrations of an N-H...pi hydrogen bonded cis-amide-benzene complex.

The ground-state N-H...pi interaction of 2-pyridone.benzene (2PY.Bz) has been studied by infrared-UV depletion spectroscopy of the supersonic-jet cooled complex [P. Ottiger et al., J. Phys. Chem. B (2009) 113, 2937]. Here, we investigate the large-amplitude vibrations of 2PY.Bz and its d(1)-2PY and benzene-d(6) isotopologues in the S(1) state, using two-color resonant two-photon ionization and UV-holeburning spectroscopies, complemented by RI-CC2 and SCS-RI-CC2 calculations of the S(1) state. The latter predict a tilted T-shaped structure with an N-H...pi hydrogen bond to the benzene ring, similar to the S(0) state. The binding energy is predicted to increase by 1.5 kJ mol(-1) upon S(1)<--S(0) excitation, in close agreement with the experimental value of 1.2 kJ mol(-1). The vibronic band structure up to 60 cm(-1) above the 0 band is dominated by large-amplitude delta tilting excitations, reflecting a change in the tilt angle of the T-shaped complex. The S(0) and S(1) state delta potentials were fitted to experiment, yielding a single minimum in the S(0) state and a double-minimum S(1) potential with delta(min) = +/-13 degrees. The second large-amplitude vibration is the theta twisting or benzene internal-rotation mode. Due to the C(6) symmetry of the benzene moiety the S(0) and S(1) state theta potentials are sixfold symmetric. Analysis of the theta band structure reveals that the S(0) and S(1)theta potentials are mutually aligned and that the internal rotation barriers are V(6)(S(0)) < 0.2 kJ mol(-1) and V(6)(S(1)) = 0.10(1) kJ mol(-1), in close agreement with the calculations. Weaker excitations of the totally symmetric intermolecular vibrations chi (shear), omega (bend) and sigma (stretch) vibrations are also observed. The 2PY intramolecular nu(1) overtone, corresponding to an 2PY amide out-of-plane twist distortion, lies approximately 30% higher than in bare 2PY, reflecting the hindrance of this motion by the strong N-H...pi interaction.

Cardiac glycosides with target at direct and indirect interactions with nuclear receptors.

Cardiac glycosides are compounds isolated from plants and animals and have been known since ancient times. These compounds inhibit the activity of the sodium potassium pump in eukaryotic cells. Cardiac glycosides were used as drugs in heart ailments to increase myocardial contraction force and, at the same time, to lower frequency of this contraction. An increasing number of studies have indicated that the biological effects of these compounds are not limited to inhibition of sodium-potassium pump activity. Furthermore, an increasing number of data have shown that they are synthesized in tissues of mammals, where they may act as a new class of steroid hormones or other hormones by mimicry to modulate various signaling pathways and influence whole organisms. Thus, we discuss the interactions of cardiac glycosides with the nuclear receptor superfamily of transcription factors activated by low-weight molecular ligands (including hormones) that regulate many functions of cells and organisms. Cardiac glycosides of endogenous and exogenous origin by interacting with nuclear receptors can affect the processes regulated by these transcription factors, including hormonal management, immune system, body defense, and carcinogenesis. They can also be treated as initial structures for combinatorial chemistry to produce new compounds (including drugs) with the desired properties.

Strong N-H...pi hydrogen bonding in amide-benzene interactions.

Among the weak intermolecular interactions found in proteins, the amide N--H...pi interaction has been widely observed but remains poorly characterized as an individual interaction. We have investigated the isolated supersonic-jet-cooled dimer of the cis-amide and nucleobase analogue 2-pyridone (2PY) with benzene and benzene-d6. Both MP2 and SCS-MP2 geometry optimizations yield a T-shaped structure with a N--H...pi hydrogen bond to the benzene ring and the C=O group above, but far from the C--H bonds of benzene. The CCSD(T) calculated binding energy at the optimum geometry is De = 25.2 kJ/mol (dissociation energy D0 = 21.6 kJ/mol), corresponding to the H-bond strength of the water dimer or of N--H...O hydrogen bonds. The T-shaped geometry is supported by the infrared-ultraviolet depletion spectra of 2PY x benzene: The N--H stretch vibrational frequency is lowered by 56 cm(-1), and the C=O stretch vibration is lowered by 10 cm(-1), relative to those of bare 2PY, indicating a strong N--H...pi interaction and a weak interaction of the C=O group. The benzene C--H infrared stretches exhibit very small shifts (approximately 2 cm(-1)) relative to benzene, signaling the absence of interactions with the benzene C--H groups. The infrared spectral shifts are consistent with a strong nonconventional pi hydrogen bond and a T-shaped structure for 2PY x benzene. Symmetry-adapted perturbation theory calculations show that the N--H...pi interaction is by far the dominant stabilization factor.

Accurate coupled cluster calculations of the reaction barrier heights of two CH3* + CH4 reactions.

We have computed barrier heights of 71.8 +/- 2.0 and 216.4 +/- 2.0 kJ mol(-1) for the reactions CH4 + CH3* --> CH3* + CH4 and CH4 + CH3* --> H* + C2H6, respectively, using explicitly correlated coupled cluster theory with singles and doubles combined with standard coupled cluster theory with up to connected quadruple excitations. Transition-state theory has been used to compute the respective reaction rate constants in the temperature interval of 250-1500 K. The computed rates for the reaction to ethane are orders of magnitude slower than those used in the mechanism of Norinaga and Deutschmann (Ind. Eng. Chem. Res. 2007, 46, 3547.) for the modeling of the chemical vapor deposition of pyrolytic carbon.

Rovibrational energy levels of H3(+) with energies above the barrier to linearity.

The H(3)(+) potential energy surface is sampled at 5900 geometries with the emphasis on the nonequilibrium and asymptotic points. Apart from the Born-Oppenheimer energy converged to the accuracy better than 0.02 cm(-1), the adiabatic and the leading relativistic corrections are computed at each geometry. To represent analytically the potential energy surface, the parameters of a power series are determined by fitting to the computed energy points. Possible choice of nuclear masses simulating the nonadiabatic effects in solving the nuclear Schrodinger equation is analyzed. A set of theoretically predicted rovibrational transitions is confronted with the experimental data in the 10,700-13,700 cm(-1) window of the spectra.

Photoelectron spectrum of valence anions of uracil and first-principles calculations of excess electron binding energies.

The photoelectron spectrum (PES) of the uracil anion is reported and discussed from the perspective of quantum chemical calculations of the vertical detachment energies (VDEs) of the anions of various tautomers of uracil. The PES peak maximum is found at an electron binding energy of 2.4 eV, and the width of the main feature suggests that the parent anions are in a valence rather than a dipole-bound state. The canonical tautomer as well as four tautomers that result from proton transfer from an NH group to a C atom were investigated computationally. At the Hartree-Fock and second-order Moller-Plesset perturbation theory levels, the adiabatic electron affinity (AEA) and the VDE have been converged to the limit of a complete basis set to within +/-1 meV. Post-MP2 electron-correlation effects have been determined at the coupled-cluster level of theory including single, double, and noniterative triple excitations. The quantum chemical calculations suggest that the most stable valence anion of uracil is the anion of a tautomer that results from a proton transfer from N1H to C5. It is characterized by an AEA of 135 meV and a VDE of 1.38 eV. The peak maximum is as much as 1 eV larger, however, and the photoelectron intensity is only very weak at 1.38 eV. The PES does not lend support either to the valence anion of the canonical tautomer, which is the second most stable anion, and whose VDE is computed at about 0.60 eV. Agreement between the peak maximum and the computed VDE is only found for the third most stable tautomer, which shows an AEA of approximately -0.1 eV and a VDE of 2.58 eV. This tautomer results from a proton transfer from N3H to C5. The results illustrate that the characteristics of biomolecular anions are highly dependent on their tautomeric form. If indeed the third most stable anion is observed in the experiment, then it remains an open question why and how this species is formed under the given conditions.

The cardenolides strophanthidin, digoxigenin and dihydroouabain act as activators of the human RORγ/RORγT receptors.

Two isoforms of a ligand-activated nuclear receptor, RORγ and RORγT, have been implicated in various physiological functions, including energy metabolism, circadian rhythm and immune system development. Using a stably transfected reporter cell line, we screened two chemical libraries and identified three cardenolides (natural, plant-derived pesticides) as activators of RORγ-dependent transcription. These compounds increased G6PC and NPAS2 expression in HepG2 cells, accompanied by increased occupancy of RORγ within the promoters of these genes. Further, strophanthidin, digoxigenin and dihydroouabain upregulated IL17A and IL17F expression and enhanced IL17 secretion in Th17 human lymphocytes. Molecular docking analyses of these compounds to the RORγ LBD showed favorable docking scores, suggesting that cardenolides may act as agonists of the receptor. Thus, our results provide new chemical structures for further development of RORγ-selective modulators with virtual therapeutic potential.