Current protein therapeutics have a major limitation: they generally cannot cross the cellular membrane or interact with cytosolic targets. The ability to design protein therapeutics that enter the cell cytosol would enable new therapeutic strategies across many disease areas, including cancer, autoimmunity, and neurological disease. Therapeutic “miniproteins” (30-60 residues in length) have the potential to address this challenge, and several miniproteins capable of efficiently reaching the cell cytosol have recently been identified. However, we lack a general understanding of the “design rules” for cell-penetrating miniproteins, limiting the development of this class of molecules. Furthermore, current approaches to measure cytosolic delivery require measuring each protein individually, which is slow and labor intensive. This makes it impossible to test large numbers of miniproteins to develop a robust, quantitative understanding of the determinants of cytosolic delivery. In this exploratory project, we will develop a new approach to measure delivery for each different protein in a large mixed pool, using argeted mass spectrometry to individually identify each miniprotein sequence. In our approach, a oluble ixed pool containing thousands of designed miniprotein sequences is incubated with cells, and miniproteins that enter those cells are captured by a cytosolic target. Miniproteins captured by the target are then purified out of the cellular contents and identified and quantified using targeted proteomics. The amount of each protein in the captured sample (relative to the starting sample) will provide a quantitative measure of delivery efficiency. This approach is unprecedented, and we will test and optimize this approach using different positive and negative control miniproteins, different library sizes, and different cell lines. With this method in hand, we will use approaches we previously pioneered to computationally design thousands of candidate cell-penetrating miniproteins with intentionally diverse sequence and structural properties. We will then quantify cytosolic delivery for these new proteins using our new high-throughput approach, creating unprecedented large-scale data on delivery efficiency. We will then use these data to build machine learning models that predict miniprotein delivery based on sequence and structural properties. Finally, we will repeatedly iterate, designing new miniprotein libraries based on our predictive models of delivery, testing these designs using our high-throughput experimental approach, and further updating our models. This iterative design-testlearn approach will build a robust, predictive understanding of the determinants of delivery. Ultimately, the ability to design cell-penetrating miniproteins will unlock a wide range of new therapeutic targets inside the cell.
|Effective start/end date||8/1/21 → 7/31/23|
- National Institute of General Medical Sciences (5R21GM143560-02)
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