There are limitations in interactions with molecular objects in laboratory experiments due to the very small size of the objects. Common media to show the experimental results of molecular objects is still lack of observer interaction to understand it intuitively. In order to overcome this lack of interaction, this research takes tensegrity representation of molecular objects reproducing experimental results and creates interactive 3D objects to be presented in a virtual reality (VR) environment. The tensegrity representation enables us to enhance the interaction experience with the natural user interface with haptic technology and hand tracking controller. A particle simulation system that utilizes multiple GPUs resources is used to fulfill haptic VR requirements. We developed a unified particle object model using springs and particles which we call anchors which act as tensegrity structure of the object to support conformation of filament-type objects such as microtubules. Some object parameters can be set to match the flexural rigidity of the object with some experimental results. The bending shape of the object is evaluated using the classic bending equation and the results show high compatibility. Viscoelastic behavior also shows similarities with the viscosity reported in other studies. The object's flexural rigidity can be adjusted to match the target value with the direction of the prediction equation. The object model provides a better insight about molecular objects with natural and real-time interactions to provide a more intuitive understanding with the molecular objects presented. The results show that this model can also be applied to any filament-type or rod-like molecular object.
Although the open-field test has been widely used, its reliability and compatibility are frequently questioned. Many indicating parameters were introduced for this test; however, they did not take data distributions into consideration. This oversight may have caused the problems mentioned above. Here, an exploratory approach for the analysis of video records of tests of elderly mice was taken that described the distributions using the least number of parameters. The locomotor activity of the animals was separated into two clusters: dash and search. The accelerations found in each of the clusters were distributed normally. The speed and the duration of the clusters exhibited an exponential distribution. Although the exponential model includes a single parameter, an additional parameter that indicated instability of the behaviour was required in many cases for fitting to the data. As this instability parameter exhibited an inverse correlation with speed, the function of the brain that maintained stability would be required for a better performance. According to the distributions, the travel distance, which has been regarded as an important indicator, was not a robust estimator of the animals’ condition.
HLA (Human Leucocyte Antigen) class I molecules present a variable but limited repertoire of antigenic peptides for T-cell recognition. Identification of specific antigenic peptides is essential for the development of immunotherapy. High polymorphism of HLA genes and a large number of possible peptides to be evaluated, however, have made the identification by experiments costly and time-consuming. Computational methods have been proposed to address this problem. In cases where plenty number of binding affinity data of peptides are available, various QSAR and machine learning approaches efficiently evaluate the affinity of test peptides, while in the cases where just a little data are available, structure-based approaches like elaborate docking have been proposed. We have developed a software named HLABAP that is designed to predict the binding affinities for a set of peptides against a particular HLA class I allele. By the combination of homology modeling for posing instead of docking and geometry optimization of the complex structures between the HLA molecule and peptides, HLABAP well predicts the binding affinities for the peptides. The results have shown that HLABAP should be applicable to identify possible antigenic peptides against a particular allele of HLA class I prior to the experiments far efficiently than the ordinary docking methods.
Skin sensitization is an important aspect of occupational and consumer safety. Because of the ban on animal testing for skin sensitization in Europe, in silico approaches to predict skin sensitizers are needed. Recently, several machine learning approaches, such as the gradient boosting decision tree (GBDT) and deep neural networks (DNNs), have been applied to chemical reactivity prediction, showing remarkable accuracy. Herein, we performed a study on DNN- and GBDT-based modeling to investigate their potential for use in predicting skin sensitizers. We separately input two types of chemical properties (physical and structural properties) in the form of one-hot labeled vectors into single- and dual-input models. All the trained dual-input models achieved higher accuracy than single-input models, suggesting that a multi-input machine learning model with different types of chemical properties has excellent potential for skin sensitizer classification.
The emergence of antibiotic-resistant bacteria is a serious public health concern. Understanding the relationships between antibiotic compounds and phenotypic changes related to the acquisition of resistance is important to estimate the effective characteristics of drug seeds. It is important to analyze the relationships between phenotypic changes and compound structures; hence, we performed a canonical correlation analysis (CCA) for high dimensional phenotypic and compound structure datasets. For the CCA, the required sample number must be larger than the feature number; however, collecting a large amount of data can sometimes be difficult. Thus, we combined consensus clustering to gather and reduce features. The CCA was performed using the clustered features, and it revealed relationships between the features of chemical substructures and the expression level of genes related to several types of antibiotic resistance.
In recent years, with the emergence of new technologies employing information science, open innovation and collaborative drug discovery research, utilizing biological and chemical experimental data, have been actively conducted. The Young Researcher Association of Chem-Bio Informatics Society (“CBI Wakate”) has constructed an online discussion space using Slack and provided a cloud-based collaborative platform in which researchers have freely discussed specific issues and aimed at raising the level of cross-sectoral communication regarding technology and knowledge. On this platform, we created three channels—dataset, model evaluation and scripts—where participants with different backgrounds co-developed a solution for solubility prediction. In the dataset channel, we exchanged our knowledge and methodology for calculations using the chemical descriptors for the original dataset and also discussed methods to improve the dataset for pharmaceutical purposes. We have also developed a protocol for evaluating the applicability of solubility prediction models for drug discovery by using the ChEMBL database and for sharing the dataset among users on the cloud. In the model evaluation channel, we discussed the necessary conditions for the prediction model to be used in daily drug discovery research. We examined the effect of these discussions on script development and suggested future improvements. This study provides an example of a new cloud-based open collaboration that can be useful for various projects in the early stage of drug discovery.