著者

Yuto Amano Hiroshi Honda Ryusuke Sawada Yuko Nukada Masayuki Yamane Naohiro Ikeda Osamu Morita Yoshihiro Yamanishi

出版者

The Japanese Society of Toxicology

雑誌

The Journal of Toxicological Sciences (ISSN:03881350)

巻号頁・発行日

vol.45, no.3, pp.137-149, 2020 (Released:2020-03-06)

参考文献数

47

被引用文献数

5

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In silico models for predicting chemical-induced side effects have become increasingly important for the development of pharmaceuticals and functional food products. However, existing predictive models have difficulty in estimating the mechanisms of side effects in terms of molecular targets or they do not cover the wide range of pharmacological targets. In the present study, we constructed novel in silico models to predict chemical-induced side effects and estimate the underlying mechanisms with high general versatility by integrating the comprehensive prediction of potential chemical-protein interactions (CPIs) with machine learning. First, the potential CPIs were comprehensively estimated by chemometrics based on the known CPI data (1,179,848 interactions involving 3,905 proteins and 824,143 chemicals). Second, the predictive models for 61 side effects in the cardiovascular system (CVS), gastrointestinal system (GIS), and central nervous system (CNS) were constructed by sparsity-induced classifiers based on the known and potential CPI data. The cross validation experiments showed that the proposed CPI-based models had a higher or comparable performance than the traditional chemical structure-based models. Moreover, our enrichment analysis indicated that the highly weighted proteins derived from predictive models could be involved in the corresponding functions of the side effects. For example, in CVS, the carcinogenesis-related pathways (e.g., prostate cancer, PI3K-Akt signal pathway), which were recently reported to be involved in cardiovascular side effects, were enriched. Therefore, our predictive models are biologically valid and would be useful for predicting side effects and novel potential underlying mechanisms of chemical-induced side effects.

著者

Shigeki Mitaku Ryusuke Sawada

出版者

一般社団法人 日本生物物理学会

雑誌

Biophysics and Physicobiology (ISSN:21894779)

巻号頁・発行日

vol.13, pp.305-310, 2016 (Released:2016-11-18)

参考文献数

7

被引用文献数

1 5

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“Life” is a particular state of matter, and matter is composed of various molecules. The state corresponding to “life” is ultimately determined by the genome sequence, and this sequence determines the conditions necessary for survival of the organism. In order to elucidate one parameter characterizing the state of “life”, we analyzed the amino acid sequences encoded in the total genomes of 557 prokaryotes and 40 eukaryotes using a membrane protein prediction online tool called SOSUI. SOSUI uses only the physical parameters of the encoded amino acid sequences to make its predictions. The ratio of membrane proteins in a genome predicted by the SOSUI online tool was around 23% for all genomes, indicating that this parameter is controlled by some mechanism in cells. In order to identify the property of genome DNA sequences that is the possible cause of the constant ratio of membrane proteins, we analyzed the nucleotide compositions at codon positions and observed the existence of systematic biases distinct from those expected based on random distribution. We hypothesize that the constant ratio of membrane proteins is the result of random mutations restricted by the systematic biases inherent to nucleotide codon composition. A new approach to the biological sciences based on the holistic analysis of whole genomes is discussed in order to elucidate the principles underlying “life” at the biological system level.