ArticleeLife2025
Reliable protein-protein docking with AlphaFold, Rosetta, and replica exchange.
Article in eLife, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
35 citing papers in PubMed.
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- ProteinDock: A physics-informed layer to improve protein-protein docking reliability.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking AlphaFold and related deep learning approaches for modeling antibody and TCR antigen recognition.bioRxiv : the preprint server for biology · 2026Article
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- One-site polarity switch enhances catalytic efficiency and stability of D-allulose 3-epimerase via flexibility-rigidity rebalancing.Scientific reports · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Marine Bioactive Peptides for Colorectal Cancer Therapy: Mechanisms, Therapeutic Potential, and Translational Challenges.Marine drugs · 2026Review
- Benchmarking TCR-pMHC structure prediction: a unified evaluation and CDR3-based functional insights.Briefings in bioinformatics · 2026Article
- From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.Briefings in bioinformatics · 2026Review
- Comparative evaluation of the prediction accuracy of AlphaFold and ESMFold for monomeric and dimeric proteins.NAR genomics and bioinformatics · 2026Article
- Subtimizer: Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates.Journal of chemical information and modeling · 2026Article
- Ion Mobility Mass Spectrometry Guided Modeling with AlphaFold and Rosetta Improves Protein Complex Structure Prediction.bioRxiv : the preprint server for biology · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- Structural quality-tier assessment for TCR-pMHC functional enrichment.Frontiers in immunology · 2026Article
- Article
- Drug Discovery Strategies for Kallikrein-Related Peptidases.International journal of molecular sciences · 2025Review
- Adapting Co-Folding Models for Structure-Based Protein-Protein Docking Through Flow Matching.bioRxiv : the preprint server for biology · 2025Article
- What does AlphaFold3 learn about antibody and nanobody docking, and what remains unsolved?mAbs · 2025Article
- AlphaFlex: Ensembles of the human proteome representing disordered regions.bioRxiv : the preprint server for biology · 2025Article
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3 authors.
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Abstract
Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases (Yin et al., 2022). In this work, we combine AF as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AF confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol to complete a robust in silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions, including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep learning-based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at https://github.com/Graylab/AlphaRED.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.