De novo design of high-affinity binders of bioactive helical peptides

Abstract Many peptide hormones form an α-helix on binding their receptors 1–4 , and sensitive methods for their detection could contribute to better clinical management of disease 5 . De novo protein design can now generate binders with high affinity and specificity to structured proteins 6,7 . However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion 8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion),…

• NS
  National Science Foundation

  Awards: P30 GM124165, EF-2021552, DE-AC02-06CH11357, 2021552
• UD
  U.S. Department of Energy

  Awards: AC02-06CH11357, P30 GM124165, DE-AC02, 06CH11357, DE-AC02-06CH11357, DE-AC02-
• BA
  Bill and Melinda Gates Foundation

  Award: INV-010680
• A
  Amgen
• WS
  Washington State University

  Award: DE-AC02-06CH11357
• PF
  Partnership for Clean Competition
• OP
  Open Philanthropy Project
• NE
  National Energy Research Scientific Computing Center
• NN
  Novo Nordisk
• NN
  Novo Nordisk Fonden

  Award: NNF19OC0054441
• NI
  National Institutes of Health

  Awards: U01 DK121289, P30 GM124165, DE-AC02-06CH11357, GM124165, U19 AG065156
• OO
  Office of Science

  Awards: DE-AC02-06CH11357, DE-AC02, 06CH11357, AC02-06CH11357
• NI
  National Institute of General Medical Sciences

  Awards: DE-AC02-06CH11357, P30 GM124165, GM124165
• NI
  National Institute of Biomedical Imaging and Bioengineering

  Award: DE-AC02-06CH11357
• AN
  Argonne National Laboratory

  Awards: DE-AC02, 06CH11357, P30 GM124165, AC02-06CH11357