Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide–protein interactions. Our simple implementation of AlphaFold2 generates peptide–protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide–protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.
Bibliographical noteFunding Information:
We are grateful to Sergey Ovchinnikov, Martin Steinegger and Milot Mirdita, and anyone else that has helped provide notebooks to run AF2. This work was supported, in whole or in part, by the Israel Science Foundation, founded by the Israel Academy of Science and Humanities (grant numbers 717/2017 and 301/2021 to O.S.-F.) and the US-Israel Binational Science Foundation (grant number 2015207). J.K.V. is supported by a Marie Sklodowska-Curie European Training Network Grant #860517.
© 2022, The Author(s).
- Amino Acid Sequence
- Binding Sites
- Models, Molecular
- Molecular Docking Simulation
- Neural Networks, Computer
- Protein Binding
- Protein Conformation, alpha-Helical
- Protein Conformation, beta-Strand
- Protein Folding
- Protein Interaction Domains and Motifs
- Protein Interaction Mapping