Abstract
Deep learning is revolutionizing structural biology to an unprecedented extent. Spearheaded by DeepMind's Alphafold2, structural models of high quality can be generated, and are now available for most known proteins and many protein interactions. The next challenge will be to leverage this rich structural corpus to learn about binding: which protein can contact which partner(s), and at what affinity? In a recent study, Chang and Perez have presented an elegant approach towards this challenging goal for interactions that involve a short peptide binding to its receptor. The basic idea is straightforward: given a receptor that binds to two peptides, if the receptor sequence is presented with both peptides together at the same time, AlphaFold2 should model the tighter binding peptide into the binding site, while excluding the second. A simple idea that works!.
| Original language | American English |
|---|---|
| Article number | e202303526 |
| Pages (from-to) | 1-3 |
| Journal | Angewandte Chemie - International Edition |
| Volume | 62 |
| Issue number | 28 |
| DOIs | |
| State | Published - Jul 2023 |
Bibliographical note
Funding Information:This work was supported, in whole or in part, by the Israel Science Foundation, founded by the Israel Academy of Science and Humanities (grant number 301/2021 to O.S.‐F.). J.K.V. is supported by a Marie Sklodowska‐Curie European Training Network Grant #860517 (Ubimotif). The Figures were created with BioRender.
Publisher Copyright:
© 2023 Wiley-VCH GmbH.
Keywords
- Alphafold2
- Binding Affinity Prediction
- Competitive Binding
- Deep Learning
- Peptide-Protein Interactions