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Author

D. Potoyan

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#generative ai Open access Oct 2026

A structure-aware generative AI framework for revealing functional relationships in protein families.

Proteins can be studied through their sequence statistics or structural properties. These represent complementary views that are useful but lack a quantitative framework to tell, family by family, which is most informative and how to combine them. We introduce a framework that builds both views in parallel: amino acid...

Divyanshu Shukla, Jonathan Martin, F. Morcos et al. · 0 citations
Open access Sep 2026

Integrating NMR and contact-response analysis reveals the allosteric network driving domain closure in Enzyme I

It is shown that ligand binding reshapes a distributed network of coupled interactions that spans the active site, interdomain linker, and domain interfaces, driving domain closure, and independent perturbations converge on the same network, demonstrating that it reflects intrinsic features of the protein energy landsc...

Aayushi Singh, Daniel Burns, Sergey L. Sedinkin et al. · 0 citations
Open access Aug 2026

Coevolution-informed Bayesian optimization for sample-efficient protein design

This work introduces ALSEBO (Active Learning Sequence Exploration via Bayesian Optimization), which couples a generative latent sequence landscape to Bayesian optimization and featurizes candidates with direct-coupling-analysis (DCA) coevolutionary statistics.

D. P. Kulathunga, Divyanshu Shukla, D. Potoyan · 0 citations

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