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ABSTRACT Accurate prediction of protein–peptide complex structures plays a critical role in structure‐based drug design, including antibody design. Most peptide‐docking benchmark studies were conducted using crystal structures of protein–peptide complexes; as such, the performance of the current peptide docking tools in the practical setting is unknown. Here, the practical setting implies there are no crystal or other experimental structures for the complex, nor for the receptor and peptide. In this work, we have developed a practical docking protocol that incorporated two famous machine learning models, AlphaFold 2 for structural prediction and ANI‐2x for ab initio potential prediction, to achieve a high success rate in modeling protein–peptide complex structures. The docking protocol consists of three major stages. In the first stage, the 3D structure of the receptor is predicted by AlphaFold 2 using the monomer mode, and that of the peptide is predicted by AlphaFold 2 using the multimer mode. We found that it is essential to include the receptor information to generate a high‐quality 3D structure of the peptide. In the second stage, rigid protein–peptide docking is performed using ZDOCK software. In the last stage, the top 10 docking poses are relaxed and refined by ANI‐2x in conjunction with our in‐house geometry optimization algorithm—conjugate gradient with backtracking line search (CG‐BS). CG‐BS was developed by us to more efficiently perform geometry optimization, which takes the potential and force directly from ANI‐2x machine learning models. The docking protocol achieved a very encouraging performance for a set of 62 very challenging protein–peptide systems which had an overall success rate of 34% if only the top 1 docking poses were considered. This success rate increased to 45% if the top 3 docking poses were considered. It is emphasized that this encouraging protein–peptide docking performance was achieved without using any crystal or experimental structures.
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Modeling beta‐sheet peptide‐protein interactions: Rosetta FlexPepDock in CAPRI rounds 38‐45
Abstract Peptide‐protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38‐45 included two peptide‐protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using theRosetta FlexPepDockpeptide docking protocol we generated top‐performing, high‐accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L‐MAG with DLC8. In addition, we were able to generate the only medium‐accuracy models for a particularly challenging target, T121. In contrast to the classical peptide‐mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta‐sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide‐protein interactions, we extractedPeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta‐sheet complementation, and tested our protocol for global peptide‐dockingPIPER‐FlexPepDockon this dataset. We find that the beta‐strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.
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- PAR ID:
- 10456904
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- Volume:
- 88
- Issue:
- 8
- ISSN:
- 0887-3585
- Page Range / eLocation ID:
- p. 1037-1049
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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