ArticleJournal of chemical information and modeling2024
Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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42 citing papers in PubMed, 47 citations in OpenAlex.
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- AlphaFold reveals but sometimes distorts an organizational principle of protein folding.bioRxiv : the preprint server for biology · 2026Article
- Tyrosine kinases sample unique activation ensembles.bioRxiv : the preprint server for biology · 2026Article
- Tyrosine Kinase Receptors, Inhibition, and Potential Role in the Pharmacotherapy of Retinal Disorders.Ophthalmology and therapy · 2026Review
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- IDPForge: Deep Learning of Proteins with Global and Local Regions of Disorder.bioRxiv : the preprint server for biology · 2026Article
- Free Energy Calculation Method Based on Enhanced Sampling of Diverse Protein Conformations Predicted by Artificial Intelligence.The journal of physical chemistry letters · 2026Article
- Fine-tuning AlphaFold with limited cryo-EM observations.Communications chemistry · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
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- Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics.The journal of physical chemistry. B · 2025Article
- 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
- 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
- af2rave: protein ensemble generation with physics-based sampling.Digital discovery · 2025Article
- Large-scale predictions of alternative protein conformations by AlphaFold2-based sequence association.Nature communications · 2025Article
- Conformational Remodeling and Allosteric Regulation Underlying EGFR Mutant-Induced Activation: A Multi-Scale Analysis Using MD, MSMs, and NRI.International journal of molecular sciences · 2025Article
- Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence-based methods.Current opinion in structural biology · 2025Review
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3 authors at 1 institution in 1 country.
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Abstract
Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular, cancers. The ubiquitousness and structural similarities of kinases make specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved Asp-Phe-Gly (DFG) motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticeably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learned order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.; Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using AlphaFold.
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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.