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Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

Proceedings of the National Academy of Sciences - PNAS, 2024-03, Vol.121 (13), p.e2314646121-e2314646121 [Peer Reviewed Journal]

Copyright National Academy of Sciences Mar 26, 2024 ;Copyright © 2024 the Author(s). Published by PNAS. 2024 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2314646121 ;PMID: 38502697

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Title:
Rapid and automated design of two-component protein nanomaterials using ProteinMPNN
Author: de Haas, Robbert J ; Brunette, Natalie ; Goodson, Alex ; Dauparas, Justas ; Yi, Sue Y ; Yang, Erin C ; Dowling, Quinton ; Nguyen, Hannah ; Kang, Alex ; Bera, Asim K ; Sankaran, Banumathi ; de Vries, Renko ; Baker, David ; King, Neil P
Subjects: Amino Acid Sequence ; Assemblies ; Biological Sciences ; Biotechnology ; Computation ; Deep learning ; Design ; Interfaces ; Models, Molecular ; Nanomaterials ; Nanostructures ; Nanotechnology ; Physics ; Protein Conformation ; Protein sequencing ; Proteins ; Proteins - chemistry ; Self-assembly ; Teaching methods
Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2024-03, Vol.121 (13), p.e2314646121-e2314646121
Description: 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.
Publisher: United States: National Academy of Sciences
Language: English
Identifier: ISSN: 0027-8424
EISSN: 1091-6490
DOI: 10.1073/pnas.2314646121
PMID: 38502697
Source: GFMER Free Medical Journals
MEDLINE

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Rapid and automated design of two-component protein nanomaterials using ProteinMPNN

Copyright National Academy of Sciences Mar 26, 2024 ;Copyright © 2024 the Author(s). Published by PNAS. 2024 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.2314646121 ;PMID: 38502697

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