AI-Assisted Protein-Peptide Complex Prediction in a Practical Setting.

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

SUBMITTER: Wang DY 

PROVIDER: S-EPMC12096808 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature