8T6N

Crystal structure of T33-27.1: Deep-learning sequence design of co-assembling tetrahedron protein nanoparticles

Summary for 8T6N

• Entry DOI: 10.2210/pdb8t6n/pdb
• Descriptor: T33-27.1 : B, T33-27.1 : A (2 entities in total)
• Functional Keywords: deep-learning, de novo design, proteinmpnn, rosetta, tetrahedrons, de novo protein
• Biological source: synthetic construct; More
• Total number of polymer chains: 9
• Total formula weight: 163765.76

Authors

Bera, A.K.,de Haas, R.J.,Kang, A.,Sankaran, B.,King, N.P. (deposition date: 2023-06-16, release date: 2024-04-24)

Primary citation

de Haas, R.J.,Brunette, N.,Goodson, A.,Dauparas, J.,Yi, S.Y.,Yang, E.C.,Dowling, Q.,Nguyen, H.,Kang, A.,Bera, A.K.,Sankaran, B.,de Vries, R.,Baker, D.,King, N.P.
Rapid and automated design of two-component protein nanomaterials using ProteinMPNN.
Proc.Natl.Acad.Sci.USA, 121:e2314646121-e2314646121, 2024

PubMed Abstract: The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

PubMed: 38502697
DOI: 10.1073/pnas.2314646121

Experimental method

X-RAY DIFFRACTION (3.63 Å)