AI-guidad design för cykliska peptidläkemedel
Cyclic peptides combine the advantages of protein therapeutics such as high target specificity with the target reach of small drug molecules. Current selection techniques for finding novel peptide leads only select for affinity to the target, but not for cell permeability. New techniques for screening and design of cell permeable peptides are of high interest to tap into the vast potential of intracellular drug targets. We will address this challenge by developing a machine learning based pipeline to design potent and cell permeable cyclic peptides. Experimental data sets are generated through a new drug discovery platform using yeast synthesising a large library of cyclic peptide variants. Big data sets on screening for potency and permeability will be used as basis for machine learning approaches to in silico design peptide sequences with improved properties. Predicted peptides will be tested in vivo and iterative optimization cycles will be conducted. We will use this pipeline on a relevant anti-cancer target, hypoxia inducible transcription factor HIF-1 and expect to find superior cyclic peptide lead structures combining both potency and permeability. This high impact project will leverage on world leading expertise in Computational Drug Discovery at AstraZeneca and Synthetic Biology at Chalmers University. It is a unique opportunity to exploit the synergies between both institutions to enable the development of a transformative novel drug discovery workflow.
Florian David (contact)
Assistant Professor at Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology
Swedish Foundation for Strategic Research (SSF)
Project ID: ID20-0109
Funding Chalmers participation during 2021–2025
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