8VD6

Crystal structure of CAP Repeat

Summary for 8VD6

• Entry DOI: 10.2210/pdb8vd6/pdb
• Descriptor: CAP REPEAT (1 entity in total)
• Functional Keywords: diffusion, generation, de novo, rosettafold, sequence, proteingenerator, protein generator, deep learning, machine learning, generative model, de novo protein
• Biological source: synthetic construct
• Total number of polymer chains: 1
• Total formula weight: 21141.96

Authors

Bera, A.K.,Gershon, J.M.,Kang, A.,Baker, D. (deposition date: 2023-12-14, release date: 2024-09-18, Last modification date: 2025-08-20)

Primary citation

Lisanza, S.L.,Gershon, J.M.,Tipps, S.W.K.,Sims, J.N.,Arnoldt, L.,Hendel, S.J.,Simma, M.K.,Liu, G.,Yase, M.,Wu, H.,Tharp, C.D.,Li, X.,Kang, A.,Brackenbrough, E.,Bera, A.K.,Gerben, S.,Wittmann, B.J.,McShan, A.C.,Baker, D.
Multistate and functional protein design using RoseTTAFold sequence space diffusion.
Nat.Biotechnol., 43:1288-1298, 2025

PubMed Abstract: Protein denoising diffusion probabilistic models are used for the de novo generation of protein backbones but are limited in their ability to guide generation of proteins with sequence-specific attributes and functional properties. To overcome this limitation, we developed ProteinGenerator (PG), a sequence space diffusion model based on RoseTTAFold that simultaneously generates protein sequences and structures. Beginning from a noised sequence representation, PG generates sequence and structure pairs by iterative denoising, guided by desired sequence and structural protein attributes. We designed thermostable proteins with varying amino acid compositions and internal sequence repeats and cage bioactive peptides, such as melittin. By averaging sequence logits between diffusion trajectories with distinct structural constraints, we designed multistate parent-child protein triples in which the same sequence folds to different supersecondary structures when intact in the parent versus split into two child domains. PG design trajectories can be guided by experimental sequence-activity data, providing a general approach for integrated computational and experimental optimization of protein function.

PubMed: 39322764
DOI: 10.1038/s41587-024-02395-w

Experimental method

X-RAY DIFFRACTION (3.7 Å)