​연구개발 성과

The Hippo pathway is an important signaling pathway modulating growth control and cancer cell proliferation. Dysregulation of the Hippo pathway is a common feature of several types of cancer cells. The modulation of the interaction between yes-associated protein (YAP) and transcriptional enhancer associated domain (TEAD) in the Hippo pathway is considered an attractive target for cancer therapeutic development, although the inhibition of PPI is a challenging task. In order to investigate the hot spots of the YAP and TEAD1 interacting complex, an ab initio Fragment Molecular Orbital (FMO) method was introduced. With the hot spots, pharmacophores for the inhibitor design were constructed, then virtual screening was performed to an in-house library. Next, we performed molecular docking simulations and FMO calculations for screening results to study the binding modes and affinities between PPI inhibitors and TEAD1. As a result of the virtual screening, three compounds were selected as virtual hit compounds. In order to confirm their biological activities, cellular (luciferase activity, proximity ligation assay and wound healing assay in A375 cells, qRT-PCR in HEK 293T cells) and biophysical assays (surface plasmon resonance assays) were performed. Based on the findings of the study, we propose a novel PPI inhibitor BY03 and demonstrate a profitable strategy to analyze YAP–TEAD PPI and discover novel PPI inhibitors.

The Bioinformatics & Molecular Design Research Center Mass Spectral Library – Natural Products (BMDMS-NP) is a library containing hte 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.

Ecotoxicological assays have examined the risk of nanoparticles (NPs) to Daphnia magna (D. magna). However, significant inconsistencies in assay results have been found among studies conducted according to D. magna test guidelines (TGs) issued from the OECD (Organization for Economic Co-operation and Development) and US EPA (United States Environmental Protection Agency). Moreover, the inconsistencies have not yet been explained as a diverse range of factors may cause heterogeneity in the assay results. Here, a meta-analysis was performed to identify the causes of these inconsistencies. Data from experimental studies were collected when they were in accordance with TGs. A dataset was compiled by extracting the physicochemical properties of NPs, the experimental conditions of the assays, and the measured toxicities to D. magna. In total, 882 data points (NPs per set of experimental conditions) were obtained from 83 publications. Meta-analyses revealed that the toxicity of NPs was higher than that of non-NPs (TEM diameter >200 nm), and the toxicity of non-coated NPs did not correlate with the TEM diameter of NPs. In addition, the dataset was divided into four classes according to the state of the NPs, including metal oxide, metal, coated metal, and carbon nanomaterials, and a prediction model was developed for each class using a support vector machine to identify the features that influenced toxicity. Dispersion methods were identified as the most important experimental conditions that explained inconsistencies in results as they explained the most variation in the model. Therefore, dispersion methods for NPs may need to be adjusted in the TGs to reduce the heterogeneity in toxicity assay results for NPs.

Computational approaches have been suggested as an informative tool for risk assessment of nanomaterials. Nano (quantitative) structure-activity relationship, nano-(Q)SAR, models have been developed to predict toxicity of metal oxide (MOx) nanoparticles (NPs); however, the packing structure and cluster of nanoparticle have been included for the descriptor calculation in only two studies. This study proposed spherical cluster and hydroxyl metal coordination complex to calculate descriptors for development of nanoparticle cytotoxicity classification model. The model cluster was generated from metal (M) or MOx crystal structure to calculate physicochemical properties of M/MOx NPs and the hydroxyl metal coordination complex was used to calculate the properties of the metal cation in an aqueous environment. Data were collected for 2 M and 19 MOx NPs in human bronchial epithelial cell lines and murine myeloid cell lines at 100 μg/ml after 24 hours exposure. The model was developed with scaled HOMO energy of the model cluster and polarizability of the hydroxyl metal coordination complex, as reactivity of the particles and the cations explained cause of cytotoxic action by M/MOx NPs. As the developed model achieved 90.31% accuracy, the classification model in this work can be used for virtual screening of toxic action of M/MOx NPs.

Objectives: For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided.

Methods: There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models.

Results: We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability.

Conclusions: We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.

Boiling point (BP) is one of the most fundamental physicochemical properties of organic compounds to characterize and identify the thermal characteristics of target compounds. Previously developed QSPR equations, however, still had some limitation for the specific compounds, like high-energy molecules, mainly because of the lack of experimental data and less coverage. A large BP dataset of 5,923 solid organic compounds was finally secured in this study, after dedicated pre-filtration of experimental data from different sources, mostly consisting of compounds not only from common organic molecules but also from some specially used molecules, and those dataset was used to build the new BP prediction model. Various machine learning methods were performed for newly collected data based on meaningful 2D descriptor set. Results of combined check showed acceptable validity and robustness of our models, and consensus approaches of each model were also performed. Applicability domain of BP prediction model was shown based on descriptor of training set.

A pioneering version of an on-line management system for high-energy molecules (MS-HEMs) was developed by the ADD and BMDRC in Korea. The current system can manage the physicochemical and explosive properties of virtual and existing HEMs. The on-line MS-HEMs consist of three main routines: management, calculation, and search. The management routine contains a user-friendly interface to store and manage molecular structures and other properties of the new HEMs. The calculation routine automatically calculates a number of compositional and topological molecular descriptors when a new HEM is stored in the MS-HEMs. Physical properties, such as the heat of formation and density, can also be calculated using group additivity methods. In addition, the calculation routine for the impact sensitivity can be used to obtain the safety nature of new HEMs. The impact sensitivity was estimated in a knowledge-based manner using in-house neural network code. The search routine enables general users to find an exact HEM and its properties by sketching a 2D chemical structure, or to retrieve HEMs and their properties by giving a range of properties. These on-line MS-HEMs are expected be powerful tool for deriving novel promising HEMs.