An open index of research

A status.lu publication

Biology & Genetics

De novo design of picomolar SARS-CoV-2 miniprotein inhibitors

Longxing Cao, Inna Goreshnik, Brian Coventry, David Baker · Full author list: Cao L, Goreshnik I, Coventry B, Case JB, Miller L, Kozodoy L, Chen RE, Carter L, Walls AC, Park YJ, Strauch EM, Stewart L, Diamond MS, Veesler D, Baker D.

Published 23 October 2020 · Science · Journal article

Summary

The authors used computational de novo protein design to create small, stable miniproteins that bind the SARS-CoV-2 spike receptor-binding domain and block its interaction with ACE2. Two design strategies were used: incorporating the ACE2 helix into a designed scaffold, and building entirely new binders against the RBD. The best designs bound with picomolar affinity and neutralized the virus, with cryo-EM confirming the binding modes matched the computational models.

Key findings

  • De novo designed miniproteins bound the spike RBD with affinities reaching the picomolar range.
  • The top inhibitors potently neutralized SARS-CoV-2 in vitro and are small and hyperstable, easing manufacture and delivery.
  • Cryo-EM structures confirmed the binders engaged the RBD essentially as designed.

Subjects & keywords

Cite this paper

APA

Longxing Cao, Inna Goreshnik, Brian Coventry, & David Baker [Full author list: Cao L, Goreshnik I, Coventry B, Case JB, Miller L, Kozodoy L, Chen RE, Carter L, Walls AC, Park YJ, Strauch EM, Stewart L, Diamond MS, Veesler D, Baker D.] (2020). De novo design of picomolar SARS-CoV-2 miniprotein inhibitors. Science. https://doi.org/10.1126/science.abd9909

BibTeX
@article{cao2020novo,
  author    = {Longxing Cao and Inna Goreshnik and Brian Coventry and David Baker and {Full author list: Cao L, Goreshnik I, Coventry B, Case JB, Miller L, Kozodoy L, Chen RE, Carter L, Walls AC, Park YJ, Strauch EM, Stewart L, Diamond MS, Veesler D, Baker D.}},
  title     = {De novo design of picomolar SARS-CoV-2 miniprotein inhibitors},
  journal   = {Science},
  year      = {2020},
  doi       = {10.1126/science.abd9909},
  url       = {https://doi.org/10.1126/science.abd9909}
}

Related in Biology & Genetics

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Josh Abramson, Jonas Adler and John M. Jumper

This paper introduced AlphaFold 3, a unified deep learning model that predicts the joint structure of complexes containing proteins, nucleic acids, small-molecule ligands, ions, and modified residues. It replaces much of the prior architecture with a diffusion-based module that directly generates atomic coordinates. The model achieved substantially improved accuracy over specialized tools across many interaction types, including protein-ligand and protein-nucleic acid complexes.

Nature Open access

De novo design of protein structure and function with RFdiffusion

Joseph L. Watson, David Juergens and David Baker

This paper introduced RFdiffusion, a generative diffusion model built on the RoseTTAFold network for de novo protein design. It enables a range of design tasks, including unconditional generation, symmetric oligomer design, functional motif scaffolding, and binder design. Many designs were experimentally validated, with solved structures closely matching the intended models.

Nature Open access

A draft human pangenome reference

Wen-Wei Liao, Mobin Asri and Jana Ebler

The Human Pangenome Reference Consortium presents a first draft human pangenome built from 47 phased, diploid genome assemblies of genetically diverse individuals. The assemblies cover more than 99% of the expected sequence per genome at over 99% base-level and structural accuracy, and are combined into a graph-based reference. Relative to GRCh38, the pangenome adds about 119 million base pairs of euchromatic polymorphic sequence and 1,115 gene duplications, improving representation of variation at structurally complex loci.

Nature Open access