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Di Bari L, Bisardi M, Cotogno S, Weigt M, Zamponi F. Emergent time scales of epistasis in protein evolution. Proceedings of the National Academy of Sciences. 121, pp.e2406807121 (2024). |
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Chen JZ, Bisardi M, Lee D, Cotogno S, Zamponi F, Weigt M, Tokuriki N. Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning. Nature Communications. 15, pp.8441 (2024). |
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Calvanese F, Weigt M, Nghe P Generating Artificial Ribozymes Using Sparse Coevolutionary Models. in RNA Design: Methods and Protocols. Springer. pp. 217–228 (2024) |
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Calvanese F, Lambert CN, Nghe P, Zamponi F, Weigt M. Towards parsimonious generative modeling of RNA families. Nucleic Acids Research. pp.gkae289 (2024). |
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Meynard-Piganeau B, Feinauer C, Weigt M, Walczak AM, Mora T. TULIP: A transformer-based unsupervised language model for interacting peptides and T cell receptors that generalizes to unseen epitopes. Proceedings of the National Academy of Sciences. 121, pp.e2316401121 (2024). |
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Meynard-Piganeau B, Fabbri C, Weigt M, Pagnani A, Feinauer C. Generating interacting protein sequences using domain-to-domain translation. Bioinformatics. 39, pp.btad401 (2023). |
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Ciarella S, Trinquier J, Weigt M, Zamponi F. Machine-learning-assisted Monte Carlo fails at sampling computationally hard problems. Machine Learning: Science and Technology. 4, pp.010501 (2023). |
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Gandarilla-Pérez CA, Pinilla S, Bitbol A-F, Weigt M. Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins. PLoS Comp Biol. 19(3), pp.e1011010 (2023). |
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Patteson JB, Fortinez CMarie, Putz AT, Rodriguez-Rivas J, L. Bryant H, Adhikari K, Weigt M, T. Schmeing M, Li B. Structure and Function of a Dehydrating Condensation Domain in Nonribosomal Peptide Biosynthesis. Journal of the American Chemical Society. 144(31), pp.14057 - 14070 (2022). |
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Vigué L, Croce G, Petitjean M, Ruppé E, Tenaillon O, Weigt M. Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes. Nature Communications. 13(1), pp.4030 (2022). |
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Rodriguez-Rivas J, Croce G, Muscat M, Weigt M. Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. Proceedings of the National Academy of Sciences. 119, pp. e2113118119 (2022). |
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Trinquier J, Uguzzoni G, Pagnani A, Zamponi F, Weigt M. Efficient generative modeling of protein sequences using simple autoregressive models. Nature Communications. 12(1), pp.5800 (2021). |
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Muntoni AP, Pagnani A, Weigt M, Zamponi F. adabmDCA: adaptive Boltzmann machine learning for biological sequences. BMC Bioinformatics. 22(1), pp.528 (2021). |
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Bisardi M, Rodriguez-Rivas J, Zamponi F, Weigt M. Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution. Molecular Biology and Evolution. (2021). |
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Barrat-Charlaix P, Muntoni AP, Shimagaki K, Weigt M, Zamponi F. Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families. Phys. Rev. E. 104, pp.024407 (2021). |
