ArticleeLife2024
Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 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
- Benchmarking AI Protein Structure Predictors Reveals a Persistent Bias in Multi-State Proteins.bioRxiv : the preprint server for biology · 2026Article
- Proton hopping and secondary gating reveal a universal transport mechanism in a minimal bacterial transporter EmrE.Biophysical journal · 2026Article
- Bridging structure and function: artificial intelligence-based modelling of kidney proteins.Nature reviews. Nephrology · 2026Review
- Advances and challenges in multiscale biomolecular simulations: artificial intelligence-driven paradigm shift.Quantitative biology (Beijing, China) · 2026Article
- Distance-Restraint-Guided Diffusion Models for Sampling Protein Conformational Changes and Ligand Dissociation Pathways.Journal of chemical theory and computation · 2026Article
- Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling.Journal of chemical information and modeling · 2026Article
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- AlphaFold-RandomWalk and AlphaFold-Ensemble: Sampling Alternative Protein Conformations with Perturbed Versions of AlphaFold.Journal of chemical information and modeling · 2026Article
- Advanced Computational Methods in Protein Simulations: A Case Study of Enhanced Sampling Applied to Membrane Transporters.Advances in experimental medicine and biology · 2026Review
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Selective Identification of Allosteric Inhibitors and Co-Drug Combinations Targeting Kinases by Using a Nanopore Tweezer Approach.ACS nano · 2025Article
- AlphaFold2 captures conformational transitions in the voltage-gated channel superfamily.Biophysical journal · 2025Article
- Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 CaJournal of chemical theory and computation · 2025Article
- MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.BMC genomics · 2025Article
- af2rave: protein ensemble generation with physics-based sampling.Digital discovery · 2025Article
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
- Covalent-Allosteric Inhibitors: Do We Get the Best of Both Worlds?Journal of medicinal chemistry · 2025Review
- Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.Journal of chemical theory and computation · 2024Article
- AlphaFold2-Based Characterization of Apo and Holo Protein Structures and Conformational Ensembles Using Randomized Alanine Sequence Scanning Adaptation: Capturing Shared Signature Dynamics and Ligand-Induced Conformational Changes.International journal of molecular sciences · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.
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Registered trials
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 Socratic graph.