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A technology to predict biochemical reactions
January 2019
by DDNews Staff  |  Email the author
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KAWASAKI, Japan—In late 2018, Fujitsu Laboratories Ltd., along with the Ireland-based data analytics research institution Insight Centre for Data Analytics and Fujitsu (Ireland) Ltd., announced the development of a technology that they say makes it possible to predict large volumes of unknown chemical reactions—about twice as many as with current conventional procedures.
 
As they note, in serious diseases like cancer it is common for there to be abnormalities in phosphorylation reactions; 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,” the companies noted in a news release about their work. “Now, by building a knowledge graph 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.”
 
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 right from the start to predict high-probability combinations.
 
While artificial intelligence (AI) technology can predict reactions in which the structure of the amino acid sequence is similar to those that are known to cause phosphorylation reactions, Fujitsu notes, 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.
 
According to recent medical research, a phenomenon exists in which proteins that have undergone reactions may phosphorylate other proteins in a chain reaction (chained information), and some have conjectured that this could be the key to predicting new, unknown phosphorylation reactions related to that phenomenon.
 
Based on such research, Fujitsu Laboratories, the Insight Centre and Fujitsu Ireland have now included not only structural information about amino acid sequences in their knowledge graph, but also chained information—developing a technology (patent pending) to represent the complex patterns of chemical reactions as optimized attributes, which are attached to the lines in the knowledge graph. As these attributes were tailored to the sophisticated construction by the knowledge graph, they can lead to highly accurate prediction results.
 
“Conventionally, the relationship between proteins could only be checked through a single link in the chain,” the companies explain. “Yet by comprehensively displaying the relationship between proteins as connections of phosphorylation reactions (chained information), it becomes possible to clarify the positioning of the various proteins from a holistic perspective, and to predict unknown relationships.”
 
By combining data on new phosphorylation reactions predicted by this technology with other biomedical data, it is expected that researchers will be able to connect the chemical reactions from the causes of a disease (abnormalities in phosphorylation reactions) to the disease's symptoms, which can then be provided to those on the front lines of research as useful information in drug discovery.
 
Fujitsu Laboratories, the Insight Centre and Fujitsu Ireland say they will continue to further improve the accuracy of this technology to process biomedical data with knowledge graphs, extending the technology to biomedical projects at Fujitsu Ltd. in fiscal 2018. Moreover, by incorporating this technology into Fujitsu's AI technology, including Fujitsu Human Centric AI Zinrai, the organizations plan to accelerate the biomedical business.
 
Code: E011907

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