The rational design of linear peptides that assemble controllably and predictably in water is challenging. Short sequences must encode unique target structures and avoid alternative states. However, the non-covalent forces...

The rational design of linear peptides that assemble controllably and predictably in water is challenging. Sequences must encode unique target structures and avoid alternative states. However, the stabilizing and discriminating non-covalent forces available are weak in water. Nonetheless, for α-helical coiled-coil assemblies considerable progress has been made in rational de novo design. In these, sequence repeats of nominally hydrophobic (h) and polar (p) residues, hpphppp, direct the assembly of amphipathic helices into dimeric to tetrameric bundles. Expanding this pattern to hpphhph can produce larger α-helical barrels. Here, we show that pentamers to nonamers are achieved simply by varying the residue at one of these h sites. In L/I-K-E-I-A-x-Z repeats, decreasing the size of Z from threonine to serine to alanine to glycine gives progressively larger oligomers. X-ray crystal structures of the resulting α-helical barrels rationalize this: side chains at Z point directly into the helical interfaces, and smaller residues allow closer helix contacts and larger assemblies.

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings.

No abstract

Rational protein design requires understanding the contribution of each amino acid to a targeted protein fold. For a subset of protein structures, namely, α-helical coiled coils (CCs), knowledge is sufficiently advanced to allow the rational de novo design of many structures, including entirely new protein folds. Current CC design rules center on using aliphatic hydrophobic residues predominantly to drive the folding and assembly of amphipathic α helices. The consequences of using aromatic residueswhich would be useful for introducing structural probes, and binding and catalytic functionalitiesinto these interfaces are not understood. There are specific examples of designed CCs containing such aromatic residues, e.g., phenylalanine-rich sequences, and the use of polar aromatic residues to make buried hydrogen-bond networks. However, it is not known generally if sequences rich in tyrosine can form CCs, or what CC assemblies these would lead to. Here, we explore tyrosine-rich sequences in a general CC-forming background and resolve new CC structures. In one of these, an antiparallel tetramer, the tyrosine residues are solvent accessible and pack at the interface between the core and the surface. In another more complex structure, the residues are buried and form an extended hydrogen-bond network.