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. 

/pdf/harnessing-protein-folding-neural-networks-for-peptide-w6zo0tnv.pdf

Harnessing protein folding neural networks for peptide–protein docking

Cis peptide bonds in proteins: residues involved, their conformations, interactions and locations.

Determining the mechanism by which proteins attain their native structure is an important but difficult problem in basic biology. The study of protein folding is difficult because it involves the identification and characterization of folding intermediates that are only very transiently present. Disulfide bond formation is thermodynamically linked to protein folding. The availability of thiol trapping reagents and the relatively slow kinetics of disulfide bond formation have facilitated the isolation, purification, and characterization of disulfide-linked folding intermediates. As a result, the folding pathways of several disulfide-rich proteins are among the best known of any protein. This review discusses disulfide bond formation and its relationship to protein folding in vitro and in vivo.

Disulfide-Linked Protein Folding Pathways

Highly accurate protein structure predictions by the recently published deep neural networks such as AlphaFold2 and RoseTTAFold are truly impressive achievements, and will have a tremendous impact far beyond structural biology. If peptide-protein binding can be seen as a final complementing step in the folding of a protein monomer, we reasoned that these approaches might be applicable to the modeling of such interactions. We present a simple implementation of AlphaFold2 to model the structure of peptide-protein interactions, enabled by linking the peptide sequence to the protein c-terminus via a poly glycine linker. We show on a large non-redundant set of 162 peptide-protein complexes that peptide-protein interactions can indeed be modeled accurately. Importantly, prediction is fast and works without multiple sequence alignment information for the peptide partner. We compare performance on a smaller, representative set to the state-of-the-art peptide docking protocol PIPER-FlexPepDock, and describe in detail specific examples that highlight advantages of the two approaches, pointing to possible further improvements and insights in the modeling of peptide-protein interactions. Peptide-mediated interactions play important regulatory roles in functional cells. Thus the present advance holds much promise for significant impact, by bringing into reach a wide range of peptide-protein complexes, and providing important starting points for detailed study and manipulation of many specific interactions.

https://biorxiv.org/content/10.1101/2021.08.01.454656v1.full.pdf

Harnessing protein folding neural networks for peptide-protein docking

Disulfide-linked protein folding pathways.

The Tyr92-Pro93 peptide group of bovine pancreatic ribonuclease A (RNase A) exists in the cis conformation in the native state. From unfolding/refolding kinetic studies of the disulfide-intact wild-type protein and of a variant in which Pro93 had been replaced by Ala, it had been suggested that the Tyr92-Ala93 peptide group also exists in the cis conformation in the native state. Here, we report the crystal structure of the P93A variant. Although there is disorder in the region of residues 92 and 93, the best structural model contains a cis peptide at this position, lending support to the results of the kinetics experiments. We also report the crystal structure of the C[40, 95]A variant, which is an analog of the major rate-determining three-disulfide intermediate in the oxidative folding of RNase A, missing the 40-95 disulfide bond. As had been detected by NMR spectroscopy, the crystal structure of this analog shows disorder in the region surrounding the missing disulfide. However, the global chain fold of the remainder of the protein, including the disulfide bond between Cys65 and Cys72, appears to be unaffected by the mutation.

/pdf/crystal-structures-of-two-mutants-that-have-implications-for-598a8jupku.pdf

Crystal structures of two mutants that have implications for the folding of bovine pancreatic ribonuclease A

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Crystal structures of two mutants that have implications for the folding of bovine pancreatic ribonuclease A