ArticleeLife2022
Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.Protein science : a publication of the Protein Society · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Rapid directed evolution guided by protein language models and epistatic interactions.Science (New York, N.Y.) · 2026Article
- Seven complete comparative maps of allosteric mutations in a protein family.Nature communications · 2026Article
- Mapping the phenotypic landscape of a transcriptional repressor using deep mutational scanning and growth-based quantitative sequencing.Nucleic acids research · 2026Article
- Dynamic Allostery: Evolution's Double-Edged Sword in Protein Function and Disease.Journal of molecular biology · 2025Review
- The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives.Journal of molecular biology · 2025Review
- Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.Journal of molecular biology · 2025Review
- Employing deep mutational scanning in theProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Breaking the oncogenic alliance: advances in disrupting the MTDH-SND1 complex for cancer therapy.RSC advances · 2025Review
- Understanding, inhibiting, and engineering membrane transporters with high-throughput mutational screens.Cell chemical biology · 2025Review
- Machine learning in molecular biophysics: Protein allostery, multi-level free energy simulations, and lipid phase transitions.Biophysics reviews · 2025Review
- Combining Oligo Pools and Golden Gate Cloning to Create Protein Variant Libraries or Guide RNA Libraries for CRISPR Applications.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Highly multiplexed design of an allosteric transcription factor to sense new ligands.Nature communications · 2024Article
- Structural and functional diversity of Resistance-Nodulation-Division (RND) efflux pump transporters with implications for antimicrobial resistance.Microbiology and molecular biology reviews : MMBR · 2024Review
- Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.Briefings in bioinformatics · 2024Article
- Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis.The journal of physical chemistry. B · 2024Article
- Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.Protein science : a publication of the Protein Society · 2024Article
- Adsorption of Flavonoids in a Transcriptional Regulator TtgR: Relative Binding Free Energies and Intermolecular Interactions.The journal of physical chemistry. B · 2024Article
- A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery.eLife · 2024Article
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Authors and funding
4 authors.
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Abstract
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.
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