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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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This content will become publicly available on June 6, 2026
Modeling CAPRI Targets of Round 55 by Combining AlphaFold and Docking
In recent years, the field of structural biology has seen remarkable advancements, particularly in modeling of protein tertiary and quaternary structures. The AlphaFold deep learning approach revolutionized protein structure prediction by achieving near‐experimental accuracy on many targets. This paper presents a detailed account of structural modeling of oligomeric targets in Round 55 of CAPRI by combining deep learning‐based predictions (AlphaFold2 multimer pipeline) with traditional docking techniques in a hybrid approach to protein–protein docking. To complement the AlphaFold models generated for the given oligomeric state of the targets, we built docking predictions by combining models generated for lower‐oligomeric states—dimers for trimeric targets and trimers/dimers for tetrameric targets. In addition, we used a template‐based docking procedure applied to AlphaFold predicted structures of the monomers. We analyzed the clustering of the generated AlphaFold models, the confidence in the prediction of intra‐ and inter‐chain residue‐residue contacts, and the correlation of the AlphaFold predictions stability with the quality of the submitted models.
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- Award ID(s):
- 2224122
- PAR ID:
- 10649004
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Proteins: Structure, Function, and Bioinformatics
- ISSN:
- 0887-3585
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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