ArticleProtein science : a publication of the Protein Society2016
Structural propensities of kinase family proteins from a Potts model of residue co-variation.
Article in Protein science : a publication of the Protein Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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49 citing papers in PubMed, 64 citations in OpenAlex.
- Inference of fitness landscapes with heterogeneous patterns of epistasis across sites.bioRxiv : the preprint server for biology · 2026Article
- Results of a Large-Scale Study of the Binding of 50 Type II Inhibitors to 348 Kinases: The Role of Protein Reorganization.Journal of medicinal chemistry · 2026Article
- Results of a large scale study of the binding of 50 type II inhibitors to 348 kinases: The role of protein reorganization.bioRxiv : the preprint server for biology · 2026Article
- Inference and Visualization of Complex Genotype-Phenotype Maps.Molecular biology and evolution · 2026Article
- Phylogenetic corrections and higher-order sequence statistics in protein families: Potts vs multiple sequence alignment transformer machine learning models.Physical review research · 2025Article
- Predicting side chain conformations in folded proteins by AlphaFold: Perspective and challenges.Biophysical journal · 2025Article
- Inference and visualization of complex genotype-phenotype maps withbioRxiv : the preprint server for biology · 2025Article
- PhosF3C: a feature fusion architecture with fine-tuned protein language model and conformer for prediction of general phosphorylation site.Briefings in bioinformatics · 2025Article
- Using AlphaFold2 to Predict the Conformations of Side Chains in Folded Proteins.bioRxiv : the preprint server for biology · 2025Article
- Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases.Scientific reports · 2024Article
- Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases.Nature communications · 2024Article
- Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases.Research square · 2024Article
- Evolutionary sequence and structural basis for the distinct conformational landscapes of Tyr and Ser/Thr kinases.bioRxiv : the preprint server for biology · 2024Article
- Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants.Protein science : a publication of the Protein Society · 2024Review
- Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.The journal of physical chemistry. B · 2024Article
- Mechanisms of HIV-1 integrase resistance to dolutegravir and potent inhibition of drug-resistant variants.Science advances · 2023Article
- Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures.Bioinformatics advances · 2023Article
- Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases.eLife · 2022Article
- Evolution and folding of repeat proteins.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Unique features of different classes of G-protein-coupled receptors revealed from sequence coevolutionary and structural analysis.Proteins · 2022Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Understanding the conformational propensities of proteins is key to solving many problems in structural biology and biophysics. The co-variation of pairs of mutations contained in multiple sequence alignments of protein families can be used to build a Potts Hamiltonian model of the sequence patterns which accurately predicts structural contacts. This observation paves the way to develop deeper connections between evolutionary fitness landscapes of entire protein families and the corresponding free energy landscapes which determine the conformational propensities of individual proteins. Using statistical energies determined from the Potts model and an alignment of 2896 PDB structures, we predict the propensity for particular kinase family proteins to assume a "DFG-out" conformation implicated in the susceptibility of some kinases to type-II inhibitors, and validate the predictions by comparison with the observed structural propensities of the corresponding proteins and experimental binding affinity data. We decompose the statistical energies to investigate which interactions contribute the most to the conformational preference for particular sequences and the corresponding proteins. We find that interactions involving the activation loop and the C-helix and HRD motif are primarily responsible for stabilizing the DFG-in state. This work illustrates how structural free energy landscapes and fitness landscapes of proteins can be used in an integrated way, and in the context of kinase family proteins, can potentially impact therapeutic design strategies.
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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.