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Programmed inhibition of an innate immune receptor via de novo designed transmembrane proteins
Colleen A Maillie1, Minghao Zhang1, Nadia Gosiet1
1Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037.
Abstract:
Transmembrane domains of immune complexes transmit precise signals across lipid bilayers. Probing their interactions has the potential to yield mechanistic insights relevant to therapeutic design. However, our capability to generate molecules that bind lipid-embedded sites is limited. Here, we created synthetic polypeptides targeting a prominent mediator of inflammatory signaling directly within membranes, Toll-like receptor 4 (TLR4), and clarify structural principles of its cross-membrane signaling mechanism. Doing so, we test emerging design principles for computationally encoding protein interactions within lipid and validate a protein-protein interaction screening platform for rapid discovery of de novo transmembrane (TM) protein antagonist of inflammatory signaling via this innate immune receptor. Binding the TM domain of TLR4 poses a formidable de novo molecular recognition challenge not yet achieved, given the membrane-spanning region is predominantly apolar and lacks any recognizable interaction motifs (e.g., sticky small residue repeats). Likewise, TLR4's structure-function relationship and key residues for oligomerization and cross-membrane signaling are underdetermined. Our engineering strategy identifies lead synthetic TM proteins that specifically bind TLR4's transmembrane domain and antagonizes NFκB signaling in human cells. These transmembrane-directed chemical probes prove that receptor TM domain interaction geometries are essential for TLR4's cross-membrane conformational coupling, informing a revised structural mechanism to leverage in future drug design of this protein family. This work refines principles for encoding stable interactions in cellular membranes and expands the range of lipid-embedded mechanisms accessible to probe with computationally derived molecules.