A system for identification of structural markers of Ames mutagenicity based on similarity of xenobiotic structure fingerprints

Abstract

The article focuses on the assessment of the genotoxic potential of chemical compounds that may be released into the environment. The necessity of changing the basic vector of development of modern toxicology in view of the achievements in the field of computer science and information technology is proved. In the framework of the study, attention was focused on the in silico approach, which allows to draw conclusions about the genotoxicity of a chemical compound in accordance with the identified functional groups that may underlie the manifestations of mutagenicity. The Ames system for determining structural markers of mutagenicity was implemented in accordance with publicly available databases of chemical compounds (EFSA, Kazius/Bursi and Hansen). The initial number of the merged dataset was increased by mycotoxins, and duplicates were removed. For each xenobiotic presented in the dataset, the mutagenic potential was determined using the in vitro Ames test. In order to effectively identify functional groups that may be signals of mutagenicity, it was decided to divide the xenobiotics of the combined data set into five structural classes. Such an approach to the formation of homogeneous groups of xenobiotics that may exhibit potential genotoxic properties allows us to identify structural markers of Ames mutagenicity within each class of mutagens. To obtain reliable information on the presence of a certain functional group - mutagenicity signal, taking into account the studied structural class of xenobiotics, it was proposed to use distance matrices calculated for each mutagen/non-mutagen pair of the combined data set. The similarity between the compounds was evaluated using classical similarity evaluation metrics (Tanimoto and Heming) according to the calculated three types of molecular fingerprints for each xenobiotic. The last stage of the implementation of the Ames system for detecting structural markers of mutagenicity was associated with the search for and application of an effective algorithm for visualizing multidimensional data. The literature analysis allowed us to choose the optimal algorithm for solving this problem. The chosen algorithm (t-SNE) allows multidimensional data (distance matrices for all mutagens and non-mutagens) to be represented in two-dimensional space. This visualization allows us to find all pairs (mutagen/non-mutagen) that have a sufficiently high similarity index and draw conclusions about the presence of certain functional groups that may underlie the manifestations of mutagenicity for each of the five structural classes of potential mutagens. It is quite interesting from the scientific point of view to analyze the effectiveness of using different types of structure fingerprints to identify structural warnings of Ames mutagenicity, which was carried out in the framework of this study. The result of the work is the developed software that allows determining structural markers of Ames mutagenicity based on the similarity of the structure fingerprints of chemical compounds represented in the combined data set. The possibility of using the proposed approach to solve the problem of finding cause-and-effect relationships between mutagenicity and the presence of certain functional groups in the structure of the studied xenobiotics is demonstrated