Modeling Protein Evolution via Generative Inference From Monte Carlo Chains to Population Genetics
Nucleic Acids Research (2026)
PUBLICATIONS
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Integrating experimental feedback improves generative models for biological sequences.
Nucleic Acids Research (2025)
Towards parsimonious generative modeling of RNA families
Nucleic Acids Research (2024)
Generating interacting protein sequences using domain-to-domain translation
Bioinformatics (2023)
Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins
PLOS Computational Biology (2023)
Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems
Machine Learning: Science and Technology (2023)
TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes
Preprint (2023)
Structure and Function of a Dehydrating Condensation Domain in Nonribosomal Peptide Biosynthesis
Journal of the American Chemical Society (2022)
Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes
Nature Communications (2022)
Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution
Molecular Biology and Evolution (2022)