De novo design of protein interactions with learned surface fingerprints
SIB Swiss Institute of Bioinformatics · École Polytechnique Fédérale de Lausanne · +5 more institutions
Abstract
Abstract Physical interactions between proteins are essential for most biological processes governing life 1 . However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein–protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications 2–9 . Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein–protein interactions…
Citation impact
- FWCI
- 41.65
- Percentile
- 100%
- References
- 90
Authors
30- PGPablo Gaínza
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
- SWSarah Wehrle
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
- AVAlexandra Van Hall‐Beauvais
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
- AMAnthony Marchand
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
- ASAndreas Scheck
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
Topics & keywords
- Computational biology
- In silico
- Protein design
- Synthetic biology
- Protein–protein interaction
- Surface protein
- Function (biology)
- Molecular recognition