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Compugen Discloses Discovery Method For Drug Candidates That Interfere With Protein Conformations

Compugen Ltd. (NASDAQ: CGEN) announced today the development of a method to identify novel therapeutic candidates to interfere with disease associated protein conformations and protein-protein interactions. This new in silico method relies on the prediction of hidden conformations of the proteins of interest, which is the subject of a scientific paper to be published in the journal Bioinformatics, and is now available online at this link.

Proteins are dynamic entities and can adopt a series of different conformations. However, some of these conformations are “hidden”, since they are short-lived or difficult to study experimentally for other reasons. Since this dynamic property of proteins is important for their function in healthy and diseased states, a broad view of a protein’s conformational space is crucial in many aspects of drug discovery.

Dr. Zurit Levine, Compugen’s V.P. of R&D, explained, “This new method has been developed to detect protein conformations that have not been observed experimentally in target proteins. This is accomplished through in silico prediction based on structures of other proteins that are remotely related to the target proteins of interest. Algorithms, enabling the appropriate weighting of the structural information extracted from the various remote homologous proteins, are a key component of the new method, resulting in the prediction of previously unknown conformations for the target protein. Detecting these hidden conformations extends our ability to design novel peptide product candidates that interfere with disease associated conformations (DAC) and protein-protein interactions (PPI), in addition to providing important new insights for our protein therapeutic product candidate discovery activities.”

Dr. Levine continued, "The concepts underlying this method have been recently incorporated into PEPPER, a previously undisclosed Compugen software infrastructure. PEPPER, which was designed to provide a basis for efficient and systematic development of new product candidate discovery platforms, tools and systems, consists of software modules for the extraction, analysis and integration of information from multiple and diverse sources and a number of distinct, but mutually supporting proprietary therapeutic discovery methodologies. These methodologies include the previously disclosed PPI and DAC Blockers Platforms, as well as other disclosed and undisclosed discovery approaches."

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