Scaffolding protein functional sites using deep learning.

Journal: Science (New York, N.Y.)
Published Date: Jul 21, 2022

Abstract

The binding and catalytic functions of proteins are generally mediated by a small number of functional residues held in place by the overall protein structure. Here, we describe deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, "constrained hallucination," optimizes sequences such that their predicted structures contain the desired functional site. The second approach, "inpainting," starts from the functional site and fills in additional sequence and structure to create a viable protein scaffold in a single forward pass through a specifically trained RoseTTAFold network. We use these two methods to design candidate immunogens, receptor traps, metalloproteins, enzymes, and protein-binding proteins and validate the designs using a combination of in silico and experimental tests.

Authors

  • Jue Wang
    State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Taipa, Macau SAR, China.
  • Sidney Lisanza
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • David Juergens
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Doug Tischer
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Joseph L Watson
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Karla M Castro
    Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
  • Robert Ragotte
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Amijai Saragovi
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Lukas F Milles
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Minkyung Baek
    Department of Biochemistry and Institute for Protein Design, University of Washington, Washington, WA, USA.
  • Ivan Anishchenko
    Computational Biology Program, The University of Kansas, Lawrence, Kansas.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Derrick R Hicks
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Marc Expòsit
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Thomas Schlichthaerle
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Jung-Ho Chun
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Justas Dauparas
    Department of Biochemistry and Institute for Protein Design, University of Washington, Washington, WA, USA.
  • Nathaniel Bennett
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Basile I M Wicky
    Department of Biochemistry, University of Washington, Seattle, WA 98105.
  • Andrew Muenks
    Department of Biochemistry, University of Washington, Seattle, WA 98105, USA.
  • Frank DiMaio
    Department of Biochemistry, University of Washington, Seattle, WA 98195, USA.
  • Bruno Correia
    Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.
  • Sergey Ovchinnikov
    Center for Systems Biology, Harvard University, Cambridge, MA 02138, United States.
  • David Baker
    Department of Biochemistry, University of Washington, Seattle, Washington.

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