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Using Computational Methods to Solubilize Membrane Proteins Membrane proteins are not members of the soluble proteome, i.e. their structure cannot be maintained outside of the membrane/in solution. Traditionally, the solubilization would be done using detergent, but this has its own problems as well. As an alternative, below are two papers that employ AI to solubilize membrane proteins to maintain their structural features for further study. And how to use them on Tamarind Bio! I previously discussed how the AI tool, ProteinMPNN, can be used to generate de novo sequences that fold into a given input structure. A variant of this method, trained only on soluble proteins, called Soluble ProteinMPNN can be used to generate sequences that maintain the original structure, while also improving solubility. Computational design of soluble and functional membrane protein analogues The main focus of this approach is to generate alternative sequences to an input structure, that do not lose their structure in solution. The authors achieve designs for "complex protein topologies and [enrich] them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space" The authors use AlphaFold2 to continuously test different replacement sequences and qualitatively evaluate if the sequence's predicted structure matches that of the native structure. Then, the authors feed these predicted structures to Soluble ProteinMPNN and find that the sequences produced via MPNN show significant experimental success. See below on how to use this methodology on Tamarind: AlphaFold inversion step: https://lnkd.in/gCW3QuZJ Soluble ProteinMPNN: https://lnkd.in/gi99zbwi Solubilization of Membrane Proteins using designed protein WRAPS Another approach to this problem is to design proteins that replicate the effect of detergent, i.e. to keep the protein stable and water soluble in the absence of the cells's lipid bilayer. The authors introduce the de novo protein category "Water-soluble RFdiffused Amphipathic Proteins". To create these WRAPS, the authors first create idealized helical and beta-barrel backbones manually, that match the shape and size of the transmembrane domain of the target. Then they feed this to the RFdiffusion fold-conditioning protocol, which will use this structure and as a template and generate a biophysically feasible alternative to this idealized backbone. Then, the authors use RFdiffusion's partial diffusion protocol to diversify the generated structures to ensure complementarity to the starting structure. Since RFdiffusion produces structures without their corresponding residues, they use Soluble ProteinMPNN to generate a viable sequence to fold into this backbone. Finally, they use AlphaFold to evaluate the fitness of each design. Use this protocol on Tamarind: https://lnkd.in/e9gj2Rtt ------------ See the comments for citations!