Researchers have created a deep learning pipeline for designing soluble analogues of key protein structures used in pharmaceutical development, sidestepping the prohibitive cost of extracting these proteins from cell membranes.
Many drug and antibody discovery pathways focus on intricately folded cell membrane proteins: when molecules of a drug candidate bind to these proteins, like a key going into a lock, they trigger chemical cascades that alter cellular behavior. But because these proteins are embedded in the lipid-containing outer layer of cells, they are tricky to access and insoluble in water-based solutions , making them difficult to study.
In a nutshell, Goverde and a research team in the LPDI, led by Bruno Correia, used deep learning to design synthetic soluble versions of cell membrane proteins commonly used in pharmaceutical research. Whereas traditional screening methods rely on indirectly observing cellular reactions to drug and antibody candidates, or painstakingly extracting small quantities of membrane proteins from mammalian cells, the researchers' computational approach allows them to remove cells from the equation.
To achieve this, the team used the structure prediction platform AlphaFold2 from Google DeepMind to produce amino acid sequences for soluble versions of several key cell membrane proteins, based on their 3D structure. Then, they used a second deep learning network, ProteinMPNN, to optimize those sequences for functional, soluble proteins.
The researchers also see these results as a proof-of-concept for their pipeline's application to vaccine research, and even cancer therapeutics. For example, they designed a soluble analogue of a protein type called a claudin, which plays a role in making tumors resistant to the immune system and chemotherapy.
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