The Features of Shared Genes among Transcriptomes Probed in Atopic Dermatitis, Psoriasis, and Inflammatory Acne: S100A9 Selection as the Target Gene.

BACKGROUND: Atopic dermatitis (AD), psoriasis (PS), and inflammatory acne (IA) are well-known as inflammatory skin diseases. Studies of the transcriptome with altered expression levels have reported a large number of dysregulated genes and gene clusters, particularly those involved in inflammatory skin diseases. OBJECTIVE: To identify genes commonly shared in AD, PS, and IA that are potential therapeutic targets, we have identified consistently dysregulated genes and disease modules that overlap with AD, PS, and IA. METHODS: Microarray data from AD, PS, and IA patients were downloaded from Gene Expression Omnibus (GEO), and identification of differentially expressed genes from microarrays of AD, PS, and IA was conducted. Subsequently, gene ontology and gene set enrichment analysis, detection of disease modules with known disease-associated genes, construction of the protein-protein interaction (PPI) network, and PPI sub-mapping analysis of shared genes were performed. Finally, the computational docking simulations between the selected target gene and inhibitors were conducted. RESULTS: We identified 50 shared genes (36 up-regulated and 14 down-regulated) and disease modules for each disease. Among the shared genes, 20 common genes in PPI network were detected such as LCK, DLGAP5, SELL, CEP55, CDC20, RRM2, S100A7, S100A9, MCM10, AURKA, CCNB1, CHEK1, BTC, IL1F7, AGTR1, HABP4, SERPINB13, RPS6KA4, GZMB, and TRIP13. Finally, S100A9 was selected as the target gene for therapeutics. Docking simulations between S100A9 and known inhibitors indicated several key binding residues, and based on this result, we suggested several cannabinoids such as WIN-55212-2, JZL184, GP1a, Nabilone, Ajulemic acid, and JWH-122 could be potential candidates for a clinical study for AD, PS, and IA via inhibition of S100A9-related pathway. CONCLUSION: Overall, our approach may become an effective strategy for discovering new disease candidate genes for inflammatory skin diseases with a reevaluation of clinical data.

ToxSTAR: drug-induced liver injury prediction tool for the web environment.

SUMMARY: Drug-induced liver injury (DILI) is a challenging endpoint in predictive toxicology because of the complex reactive metabolites that cause liver damage and the wide range of mechanisms involved in the development of the disease. ToxSTAR provides structural similarity-based DILI analysis and in-house DILI prediction models that predict four DILI subtypes (cholestasis, cirrhosis, hepatitis and steatosis) based on drug and drug metabolite molecules. AVAILABILITY AND IMPLEMENTATION: ToxSTAR is freely available at https://toxstar.kitox.re.kr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interaction Analysis of the Spike Protein of Delta and Omicron Variants of SARS-CoV-2 with hACE2 and Eight Monoclonal Antibodies Using the Fragment Molecular Orbital Method.

In the past 2 years, since the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), multiple SARS-CoV-2 variants have emerged. Whenever a new variant emerges, considerable time is required to analyze the binding affinity of the viral surface proteins to human angiotensin-converting enzyme 2 (hACE2) and monoclonal antibodies. To efficiently predict the binding affinities associated with hACE2 and monoclonal antibodies in a short time, herein, we propose a method applying statistical analysis to simulations performed using molecular and quantum mechanics. This method efficiently predicted the trend of binding affinity for the binding of the spike protein of each variant of SARS-CoV-2 to hACE2 and individually to eight commercial monoclonal antibodies. Additionally, this method accurately predicted interaction energy changes in the crystal structure for 10 of 13 mutated residues in the Omicron variant, showing a significant change in the interaction energy of hACE2. S375F was found to be a mutation that majorly changed the binding affinity of the spike protein to hACE2 and the eight monoclonal antibodies. Our proposed analysis method enables the prediction of the binding affinity of new variants to hACE2 or to monoclonal antibodies in a shorter time compared to that utilized by the experimental method.

Potent and Selective Inhibitors of Human Monoamine Oxidase A from an Endogenous Lichen Fungus Diaporthe mahothocarpus.

Using 126 endogenous lichen fungus (ELF) extracts, inhibitory activities against monoamine oxidases (MAOs) and cholinesterases (ChEs) were evaluated. Among them, extract ELF29 of the endogenous fungus Diaporthe mahothocarpus of the lichen Cladonia symphycarpia showed the highest inhibitory activity against hMAO-A. Compounds alternariol (AT), 5'-hydroxy-alternariol (HAT), and mycoepoxydiene (MED), isolated from the extract, had potent inhibitory activities against hMAO-A with IC50 values of 0.020, 0.31, and 8.68 µM, respectively. AT, HAT, and MED are reversible competitive inhibitors of hMAO-A with Ki values of 0.0075, 0.116, and 3.76 µM, respectively. The molecular docking studies suggested that AT, HAT, and MED had higher binding affinities for hMAO-A (-9.1, -6.9, and -5.6 kcal/mol, respectively) than for hMAO-B (-6.3, -5.2, and -3.7 kcal/mol, respectively). The relative tight binding might result from a hydrogen bond interaction of the three compounds with a Tyr444 residue in hMAO-A, whereas no hydrogen bond interaction was proposed in hMAO-B. In silico pharmacokinetics, the three compounds showed high gastrointestinal absorption without violating Lipinski's five rules, but only MED showed high probability to cross the blood-brain barrier. These results suggest that AT, HAT, and MED are candidates for treating neuropsychiatric disorders, such as depression and cardiovascular disease.

Development of an Infinite Dilution Activity Coefficient Prediction Model for Organic Solutes in Ionic Liquids with Modified Partial Equalization Orbital Electronegativity Method Derived Descriptors.

The objective of this study was to develop a robust prediction model for the infinite dilution activity coefficients (γ ∞) of organic molecules in diverse ionic liquid (IL) solvents. Electrostatic, hydrogen bond, polarizability, molecular structure, and temperature terms were used in model development. A feed-forward model based on artificial neural networks was developed with 34,754 experimental activity coefficients, a combination of 195 IL solvents (88 cations and 38 anions), and 147 organic solutes at a temperature range of 298 to 408 K. The root mean squared error (RMSE) of the training set and test set was 0.219 and 0.235, respectively. The R 2 of the training set and the test set was 0.984 and 0.981, respectively. The applicability domain was determined through a Williams plot, which implied that water and halogenated compounds were outside of the applicability domain. The robustness test shows that the developed model is robust. The web server supports using the developed prediction model and is freely available at https://preadmet.bmdrc.kr/activitycoefficient_mainpage/prediction/.

Evaluation of Selective COX-2 Inhibition and In Silico Study of Kuwanon Derivatives Isolated from Morus alba.

Six kuwanon derivatives (A/B/C/E/H/J) extracted from the roots of Morus alba L. were evaluated to determine their cyclooxygenase (COX)-1 and 2 inhibitory effects. Cyclooxygenase (COX) is known as the target enzyme of nonsteroidal anti-inflammatory drugs (NSAIDs), which are the most widely used therapeutic agents for pain and inflammation. Among six kuwanon derivatives, kuwanon A showed selective COX-2 inhibitory activity, almost equivalent to that of celecoxib, a known COX inhibitor. Kuwanon A showed high COX-2 inhibitory activity (IC50 = 14 µM) and a selectivity index (SI) range of >7.1, comparable to celecoxib (SI > 6.3). To understand the mechanisms underlying this effect, we performed docking simulations, fragment molecular orbital (FMO) calculations, and pair interaction energy decomposition analysis (PIEDA) at the quantum-mechanical level. As a result, kuwanon A had the strongest interaction with Arg120 and Tyr355 at the gate of the COX active site (-7.044 kcal/mol) and with Val89 in the membrane-binding domain (-6.599 kcal/mol). In addition, kuwanon A closely bound to Val89, His90, and Ser119, which are residues at the entrance and exit routes of the COX active site (4.329 Å). FMO calculations and PIEDA well supported the COX-2 selective inhibitory action of kuwanon A. It showed that the simulation and modeling results and experimental evidence were consistent.

PreMetabo: An in silico phase I and II drug metabolism prediction platform.

This study aimed to develop a drug metabolism prediction platform using knowledge-based prediction models. Site of Metabolism (SOM) prediction models for four cytochrome P450 (CYP) subtypes were developed along with uridine 5'-diphosphoglucuronosyltransferase (UGT) and sulfotransferase (SULT) substrate classification models. The SOM substrate for a certain CYP was determined using the sum of the activation energy required for the reaction at the reaction site of the substrate and the binding energy of the substrate to the CYP enzyme. Activation energy was calculated using the EaMEAD model and binding energy was calculated by docking simulation. Phase II prediction models were developed to predict whether a molecule is the substrate of a certain phase II conjugate protein, i.e., UGT or SULT. Using SOM prediction models, the predictability of the major metabolite in the top-3 was obtained as 72.5-84.5% for four CYPs, respectively. For internal validation, the accuracy of the UGT and SULT substrate classification model was obtained as 93.94% and 80.68%, respectively. Additionally, for external validation, the accuracy of the UGT substrate classification model was obtained as 81% in the case of 11 FDA-approved drugs. PreMetabo is implemented in a web environment and is available at https://premetabo.bmdrc.kr/.

BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds.

The Bioinformatics & Molecular Design Research Center Mass Spectral Library - Natural Products (BMDMS-NP) is a library containing the mass spectra of natural compounds, especially plant specialized metabolites. At present, the library contains the electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra of 2739 plant metabolites that are commercially available. The contents of the library were made comprehensive by incorporating data generated under various experimental conditions for compounds with diverse molecular structures. The structural diversity of the BMDMS-NP data was evaluated using molecular fingerprints, and it was sufficiently exhaustive enough to represent the structures of the natural products commercially available. The MS/MS spectra of each metabolite were obtained with different types/brands of ion traps (tandem-in-time) or combinations of mass analyzers (tandem-in-space) at multiple collision energies. All spectra were measured repeatedly in each environment because variations can occur in spectra, even under the same conditions. Moreover, the probability, separability of searching, and transferability of this spectral library were evaluated against those of MS/MS libraries, namely: NIST17 and MoNA.

Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development.

Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC's chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.

Investigation of Hot Spot Region in XIAP Inhibitor Binding Site by Fragment Molecular Orbital Method.

X-linked inhibitor of apoptosis protein (XIAP) is an important regulator of cancer cell survival whose BIR3 domain (XIAP-BIR3) recognizes the Smac N-terminal tetrapeptide sequence (AVPI), making it an attractive protein-protein interaction (PPI) target for cancer therapies. We used the fragment molecular orbital (FMO) method to study the binding modes and affinities between XIAP-BIR3 and a series of its inhibitors (1-8) that mimic the AVPI binding motif; the inhibitors had common interactions with key residues in a hot spot region of XIAP-BIR3 (P1-P4 subpockets) with increased binding affinity mainly attributed to specific interactions with the P1 and P4 subpockets. Based on the structural information from FMO results, we proposed a novel XIAP natural product inhibitor, neoeriocitrin 10, which was derived from our preciously reported XIAP-BIR3 inhibitor 9, can be used as a highly potent candidate for XIAP-BIR3 inhibition. We also performed pair interaction energy decomposition analysis to investigate the binding energies between specific binding residues and individual ligands, showing that the novel natural product neoeriocitrin 10 had a higher binding affinity than epicatechin gallate 9. Molecular docking and dynamics simulations were performed to explore the mode of binding between 10 and XIAP-BIR3, demonstrating that 10 binds more strongly to the P1 and P4 pockets than 9. Overall, we present a novel natural product, neoeriocitrin 10, and demonstrate that the FMO method can be used to identify hot spots in PPIs and design new compounds for XIAP inhibition.

Incorporation of Hydrogen Bond Angle Dependency into the Generalized Solvation Free Energy Density Model.

To describe the physically realistic solvation free energy surface of a molecule in a solvent, a generalized version of the solvation free energy density (G-SFED) calculation method has been developed. In the G-SFED model, the contribution from the hydrogen bond (HB) between a solute and a solvent to the solvation free energy was calculated as the product of the acidity of the donor and the basicity of the acceptor of an HB pair. The acidity and basicity parameters of a solute were derived using the summation of acidities and basicities of the respective acidic and basic functional groups of the solute, and that of the solvent was experimentally determined. Although the contribution of HBs to the solvation free energy could be evenly distributed to grid points on the surface of a molecule, the G-SFED model was still inadequate to describe the angle dependency of the HB of a solute with a polarizable continuum solvent. To overcome this shortcoming of the G-SFED model, the contribution of HBs was formulated using the geometric parameters of the grid points described in the HB coordinate system of the solute. We propose an HB angle dependency incorporated into the G-SFED model, i.e., the G-SFED-HB model, where the angular-dependent acidity and basicity densities are defined and parametrized with experimental data. The G-SFED-HB model was then applied to calculate the solvation free energies of organic molecules in water, various alcohols and ethers, and the log P values of diverse organic molecules, including peptides and a protein. Both the G-SFED model and the G-SFED-HB model reproduced the experimental solvation free energies with similar accuracy, whereas the distributions of the SFED on the molecular surface calculated by the G-SFED and G-SFED-HB models were quite different, especially for molecules having HB donors or acceptors. Since the angle dependency of HBs was included in the G-SFED-HB model, the SFED distribution of the G-SFED-HB model is well described as compared to that of the G-SFED model.