Scalable protein design using optimization in a relaxed sequence space

Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes.

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Publication Details

Journal
Science
Published
2024-10-24
DOI
https://doi.org/10.1126/science.adq1741
Citations
59
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
13.80

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article

Scalable protein design using optimization in a relaxed sequence space

Yosta de Stigter, Shihao Feng, L W Weber, Ali Khoshouei et al.
59 citations
Science
Protein Structure and Dynamics
13.80
article

Scalable protein design using optimization in a relaxed sequence space

Yosta de Stigter, Shihao Feng, L W Weber, Ali Khoshouei, Hendrik Dietz, Sergey Ovchinnikov, Motoyuki Hattori, Christopher L. Frank, Zhixuan Zhao, Lara Fuβ, Dominik Schiwietz, Dominik Putz
article en
59 citations

Abstract

Machine learning (ML)-based design approaches have advanced the field of de novo protein design, with diffusion-based generative methods increasingly dominating protein design pipelines. Here, we report a "hallucination"-based protein design approach that functions in relaxed sequence space, enabling the efficient design of high-quality protein backbones over multiple scales and with broad scope of application without the need for any form of retraining. We experimentally produced and characterized more than 100 proteins. Three high-resolution crystal structures and two cryo-electron microscopy density maps of designed single-chain proteins comprising up to 1000 amino acids validate the accuracy of the method. Our pipeline can also be used to design synthetic protein-protein interactions, as validated experimentally by a set of protein heterodimers. Relaxed sequence optimization offers attractive performance with respect to designability, scope of applicability for different design problems, and scalability across protein sizes.

ScienceVol. 386(6720)
Harvard University (US), Fudan University (CN), Technical University of Munich (DE), Massachusetts Institute of Technology (US)
Salt Science Research Foundation, Deutsche Forschungsgemeinschaft, École Polytechnique Fédérale de Lausanne, Gottfried Wilhelm Leibniz Universität Hannover, Technische Universität München, Université de Genève, Université de Lausanne, Leibniz-Gemeinschaft
Openalex Percentile: Top 1%
Protein Structure and Dynamics
13.80
DOIOpen Access PDFOpenAlex
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