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Peptide–protein docking: from physics-based models to generative intelligence
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DOI：10.1039/D6CC00583G.png)
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Abstract
En 中文
Peptide–protein interactions (PepPIs) play a pivotal role in cellular signaling and regulation; representing a significant category of therapeutic agents. However; determining peptide–protein complex structures by experiment is costly and often challenging. Computational peptide–protein complex structure prediction; therefore; plays an important role in mapping binding modes and guiding design. Classical pipelines combine template-based; local; or global docking conformational search algorithms with physics-based or empirical scoring; but they often struggle with highly flexible peptides; induced fit at shallow interfaces; and non-canonical chemistries. In this review; we describe an ongoing shift from such conventional search-and-score workflows to deep learning-based pipelines. We categorize the modern methods into three modules: (i) approaches that predict likely peptide-binding regions on the protein surface and use these predictions to guide or filter docking models; (ii) AlphaFold-based protocols that use general structure prediction methods for peptide–protein co-folding and refinement; and (iii) deep generative models that sample peptide conformations given a target protein structure. We highlight that recent methods have substantially improved the accuracy and applicability of peptide–protein docking; while also identifying shared remaining challenges; including limited avaiability of training data and weak performance on long; disordered; or chemically modified peptides. We conclude by outlining directions for integrating richer biophysical constraints; better-curated peptide–protein datasets; and large-scale generative models to move toward robust; design-ready peptide docking.
Keywords:
peptide–protein interactions
computational docking
deep learning
structure prediction
generative models