The COSMOS method developed by the research team at the Tokyo University of Science (TUS) offers a platform for “personalized drug discovery” based on a novel drug discovery methodology. The concept is to identify an optimal binding peptide that interacts with the active/regulatory site (Hot Spot) of the target drug discovery protein by using in silico methods.
Background:
“Personalized medicine” based on disease genomic big data analysis is anticipated for genome medicine in the twenty-first century. To make this a reality, it is essential to establish a platform for “personalized drug discovery” based on a novel drug discovery methodology that allows molecular design of optimal pharmaceutical lead compounds against disease target molecules in individuals. Currently, the conventional drug discovery method of high-throughput screening of combinatorial chemistry (HTS/CC) conducted in many pharmaceutical companies requires enormous amounts of work, time, and cost; however, in reality, it has a low success rate. To radically change this situation, the research team at TUS have established a novel in silico genome-based drug discovery system “COSMOS” and are conducting research to achieve higher success rates in drug discovery.
Summary of technology:
The team has developed a novel drug discovery methodology implementation system, the COSMOS method, targeting protein-protein interactions, which are considered to be difficult to target by conventional drug discovery methods. The concept is to identify an optimal binding peptide that interacts with the active/regulatory site (Hot Spot) of the target drug discovery protein by using in silico methods. Using this as a design element, transformation design to small molecule and automatic optimization is possible by refining the unique binding 3D coordinates.
Applications:
By designing an “optimal binding peptide” that responds to changes in the microstructure of amino acid mutation target proteins as a means of overcoming drug resistance.
By designing several related "optimal binding peptides" from PPI analysis data such as antibody epitopes and creating functional peptides.
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