Kawasaki :Fujitsu Laboratories Ltd., the Insight Centre for Data Analytics(1), a data analytics research institution based in Ireland, and Fujitsu (Ireland) Limited today announced the development of a technology that makes it possible to predict large volumes of unknown chemical reactions, about twice as many as the conventional procedure. In serious diseases, including cancer, it is common for there to be abnormalities in phosphorylation reactions, which are chemical reactions that occur between proteins. Accordingly, there are high expectations that clarifying phosphorylation reactions will lead to effective treatments. At present, however, because only a few phosphorylation reactions have been identified, there has been a problem in predicting large volumes of phosphorylation reactions caused by combinations of unknown proteins. Now, by building a knowledge graph(2) that can encompass an overview of the interrelations between proteins, it is possible to check the relationship between new proteins where phosphorylation reactions can be predicted. In this way, this technology will contribute to the advancement of medicine, as it can be expected to be useful on the front lines of drug discovery research, and have customized applications in the field of precision medicine(3).

Development Background

Biological systems within the body are maintained by exchanges of information through the chemical reactions of various proteins within cells. In recent years, science has come to understand that many serious diseases, such as cancer, are partially caused by abnormalities in phosphorylation reactions, which are representative of the chemical reactions between proteins. If pharmaceuticals that repaired abnormal phosphorylation reactions could be developed, that would enable more effective treatments. At present, however, only a few phosphorylation reactions are well understood, so there is a need for the discovery of unknown phosphorylation reactions, and to enrich the data on phosphorylation reactions.

Issues

Phosphorylation reactions are chemical reactions in which a protein attaches a phosphoryl group to the amino acids that make up another protein. In order to discover them, it is necessary to check the combinations of proteins that cause phosphorylation reactions through biological experiments. Nonetheless, as there are more than about 800,000 possible combinations just with proteins, and because significant costs and time are required for biological experiments, it is necessary to right from the start predict high-probability combinations. It is known that whether a phosphorylation reaction will occur depends on the structure of the amino acid sequence that makes up the protein. AI technology is therefore already being used to predict new phosphorylation reactions by training the AI on the structure of amino acid sequences that are already known to cause phosphorylation reactions. While this technology can predict reactions in which the structure of the amino acid sequence is similar to those that are known to cause phosphorylation reactions, it has not been capable of predicting those in which the structure of the amino acid sequence is significantly different from the already known phosphorylation reactions.