Computational methods for modeling protein–protein interactions in the AI era: Current status and future directions

2025/05/19 by Hao Li, Chandran Nithin, Sebastian Kmiecik +2 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Microbial Metabolic Engineering and Bioproduction #Protein Structure and Dynamics

paper · doi:10.1016/j.drudis.2025.104382

openalex publication_date 2025/05/19 · crossref created 2025/05/19 · crossref issued 2025/06/01 · crossref published 2025/06/01 · crossref published-print 2025/06/01 · crossref deposited 2025/06/11 · openalex created_date 2025/10/10 · crossref indexed 2026/07/17 · openalex updated_date 2026/08/01

Citations

• The HDOCK server for integrated protein–protein docking
• Performance and Its Limits in Rigid Body Protein-Protein Docking
• Highly accurate protein structure prediction with AlphaFold
• Harnessing protein folding neural networks for peptide–protein docking
• ColabFold: making protein folding accessible to all
• DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
• Recent advances in predicting and modeling protein–protein interactions
• Impact of AlphaFold on structure prediction of protein complexes: The CASP15‐CAPRI experiment
• Predicting multiple conformations via sequence clustering and AlphaFold2
• Direct prediction of intrinsically disordered protein conformational properties from sequence
• AlphaFold Meets Flow Matching for Generating Protein Ensembles
• Accurate structure prediction of biomolecular interactions with AlphaFold 3

Discussions

• New review in Drug Discovery Today: Modeling protein–protein interactions in the AI era 🔗 doi.org/10.1016/j.dr... AI is shiny. Biology is messy — flexibility, disorder, weak signals, big complexes. [bsky, 0 points, 0 comments]