Abstract
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.
Highlights
Accurate protein structure predictions by deep neural networks such as AlphaFold[2] and RoseTTAFold have tremendous impact on structural biology and beyond
We show that by connecting the peptide to the receptor, monomer folding neural network (NN) generate accurate peptide–protein complex structures
In contrast to AF2, a similar tactic using RoseTTAFold did not succeed but rather attempted to fold the polyglycine into a globular structure or create various loops with intra-loop interactions (Supplementary Figure 1). This can be explained by the fact that RoseTTAFold was not trained to identify unstructured regions[29], in contrast to AF2 where these regions were not removed before training
Summary
Accurate protein structure predictions by deep neural networks such as AlphaFold[2] and RoseTTAFold have tremendous impact on structural biology and beyond. We explore what AlphaFold[2] 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 AlphaFold[2] 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. Determining the 3-dimensional structure of these peptide–- 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 order to succeed in the study and design of peptide–protein interactions, we must gain a better understanding of the peptide conformational preferences
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