Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Design of proteins presenting discontinuous functional sites using deep learning

View ORCID ProfileDoug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, View ORCID ProfileIvan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker
doi: https://doi.org/10.1101/2020.11.29.402743
Doug Tischer
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Doug Tischer
Sidney Lisanza
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
cGraduate program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jue Wang
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Runze Dong
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
cGraduate program in Biological Physics, Structure and Design, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ivan Anishchenko
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ivan Anishchenko
Lukas F. Milles
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sergey Ovchinnikov
dFAS Division of Science, Harvard University, Cambridge, MA 02138, USA
eJohn Harvard Distinguished Science Fellowship Program, Harvard University, Cambridge, MA 02138, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
David Baker
aDepartment of Biochemistry, University of Washington, Seattle, WA 98105, USA
bInstitute for Protein Design, University of Washington, Seattle, WA 98105, USA
fHoward Hughes Medical Institute, University of Washington, Seattle, WA 98105, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: dabaker@uw.edu so@fas.harvard.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

An outstanding challenge in protein design is the design of binders against therapeutically relevant target proteins via scaffolding the discontinuous binding interfaces present in their often large and complex binding partners. There is currently no method for sampling through the almost unlimited number of possible protein structures for those capable of scaffolding a specified discontinuous functional site; instead, current approaches make the sampling problem tractable by restricting search to structures composed of pre-defined secondary structural elements. Such restriction of search has the disadvantage that considerable trial and error can be required to identify architectures capable of scaffolding an arbitrary discontinuous functional site, and only a tiny fraction of possible architectures can be explored. Here we build on recent advances in de novo protein design by deep network hallucination to develop a solution to this problem which eliminates the need to pre-specify the structure of the scaffolding in any way. We use the trRosetta residual neural network, which maps input sequences to predicted inter-residue distances and orientations, to compute a loss function which simultaneously rewards recapitulation of a desired structural motif and the ideality of the surrounding scaffold, and generate diverse structures harboring the desired binding interface by optimizing this loss function by gradient descent. We illustrate the power and versatility of the method by scaffolding binding sites from proteins involved in key signaling pathways with a wide range of secondary structure compositions and geometries. The method should be broadly useful for designing small stable proteins containing complex functional sites.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted November 29, 2020.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Design of proteins presenting discontinuous functional sites using deep learning
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Design of proteins presenting discontinuous functional sites using deep learning
Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, Ivan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker
bioRxiv 2020.11.29.402743; doi: https://doi.org/10.1101/2020.11.29.402743
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Design of proteins presenting discontinuous functional sites using deep learning
Doug Tischer, Sidney Lisanza, Jue Wang, Runze Dong, Ivan Anishchenko, Lukas F. Milles, Sergey Ovchinnikov, David Baker
bioRxiv 2020.11.29.402743; doi: https://doi.org/10.1101/2020.11.29.402743

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Biochemistry
Subject Areas
All Articles
  • Animal Behavior and Cognition (3505)
  • Biochemistry (7348)
  • Bioengineering (5324)
  • Bioinformatics (20266)
  • Biophysics (10019)
  • Cancer Biology (7744)
  • Cell Biology (11305)
  • Clinical Trials (138)
  • Developmental Biology (6437)
  • Ecology (9953)
  • Epidemiology (2065)
  • Evolutionary Biology (13325)
  • Genetics (9361)
  • Genomics (12586)
  • Immunology (7702)
  • Microbiology (19024)
  • Molecular Biology (7443)
  • Neuroscience (41041)
  • Paleontology (300)
  • Pathology (1229)
  • Pharmacology and Toxicology (2138)
  • Physiology (3161)
  • Plant Biology (6861)
  • Scientific Communication and Education (1273)
  • Synthetic Biology (1896)
  • Systems Biology (5313)
  • Zoology (1089)