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

• 成果类型: Article
• 署名作者: 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.
• 署名单位: Wageningen University & Research; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; United States Department of Energy (DOE); Lawrence Berkeley National Laboratory; Howard Hughes Medical Institute
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14934
• DOI: 10.1073/pnas.2314646121
• 发表日期: 2024-03-19
• 关键词: nanoparticle vaccine; accurate design; language; prediction; EVOLUTION

摘要

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.