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Tipps, S. W. K.

Publications and source records attributed to Tipps, S. W. K..

2 recordsLinked to original sources

Discovering and designing multistate protein assemblies

Evolved protein assemblies exploit multiple weak interfaces to package cargo, facilitate metabolism, and direct cell movement, often through architectures that depart from strict geometric symmetry. Designed protein assemblies can also break symmetry through conformational flexibility or strain-based defects, but have generally lacked the capacity for large-scale structural rearrangements characteristic of evolved systems. Here, we characterize the structures and biophysical properties of emergent protein assemblies and establish a strategy for designing quasisymmetric materials that switch between assembly states. Rather than relying on intrinsic flexibility, our approach programs multiple self-assembling interfaces onto rigid oligomers. Using multistate design and ensemble predictions, we generate assemblies in which protomers can adopt alternative designed interface modes, enabling identical amino acid sequences to form distinct configurations depending on assembly pathway and environmental conditions. As a proof of principle, we construct quasisymmetric capsids that transition into micron-scale 2D arrays in response to temperature. This work establishes a strategy for encoding alternative assembly geometries within a single protein sequence and provides a foundation for engineering materials with environmentally tunable properties.

biochemistry↗

Joint Generation of Protein Sequence and Structure with RoseTTAFold Sequence Space Diffusion

Protein denoising diffusion probabilistic models (DDPMs) show great promise in the de novo generation of protein backbones but are limited in their inability to guide generation of proteins with sequence specific attributes and functional properties. To overcome this limitation, we develop ProteinGenerator, a sequence space diffusion model based on RoseTTAfold that simultaneously generates protein sequences and structures. Beginning from random amino acid sequences, our model generates sequence and structure pairs by iterative denoising, guided by any desired sequence and structural protein attributes. To explore the versatility of this approach, we designed proteins enriched for specific amino acids, with internal sequence repeats, with masked bioactive peptides, with state dependent structures, and with key sequence features of specific protein families. ProteinGenerator readily generates sequence-structure pairs satisfying the input conditioning (sequence and/or structural) criteria, and experimental validation showed that the designs were monomeric by size exclusion chromatography (SEC), had the desired secondary structure content by circular dichroism (CD), and were thermostable up to 95{degrees}C. By enabling the simultaneous optimization of both sequence and structure, ProteinGenerator allows for the design of functional proteins with specific sequence and structural attributes, and paves the way for protein function optimization by active learning on sequence-activity datasets.

biochemistry↗