![]() ![]() Computational modeling, and particularly blind peptide–protein docking 12, is hindered by the lack of known structure for the peptide side, in contrast to classical domain-domain docking, where the structure of the free individual domains is usually defined. Widely used structure determination methods (e.g., X-ray crystallography) are not applicable to many of these interactions. However, peptide-mediated interactions pose significant challenges, both for their experimental as well as their computational characterization: These interactions are in many cases weak, transient, and considerably influenced by their context, resulting in often noisy experiments. They could help to better understand disease-causing mutations and also serve as a starting point for the design of strong and stable peptidomimetics 10, 11. They can provide the basis to identify hotspot residues that are crucial for binding 6, 7, 8, and by mutating these hotspots, the functional importance of a given interaction can be uncovered 9. In addition, peptides are often used for biotechnological applications, drug delivery, imaging, as therapeutic agents, and other applications 4, 5, by binding proteins and mediating or blocking interactions.ĭetermining the 3-dimensional structure of these peptide–protein complexes is an important step for their further study. It is estimated that up to 40% of interactions in cells are mediated by peptide–protein interactions, or peptide-like interaction: 2 short segments, isolated or embedded within unstructured regions that mediate binding to a partner 3. Peptide–protein interactions are highly abundant in living cells and are important for many biological processes 1. 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. 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. 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. 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. Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |