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Rodriguez-Horta E, Lage A, Weigt M, Barrat-Charlaix P. Global multivariate model learning from hierarchically correlated data. J. Stat. Mech. (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). |
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Danko D, Bezdan D, Afshin EE, Ahsanuddin S, Bhattacharya C, Butler DJ, Chng KRei, Donnellan D, Hecht J, Jackson K, Kuchin K, Karasikov M, Lyons A, Mak L, Meleshko D, Mustafa H, Mutai B, Neches RY, Ng A, Nikolayeva O, Nikolayeva T, Png E, Ryon KA, Sanchez JL, Shaaban H, Sierra MA, Thomas D, Young B, Abudayyeh OO, Alicea J, Bhattacharyya M, Blekhman R, Castro-Nallar E, Cañas AM, Chatziefthimiou AD, Crawford RW, De Filippis F, Deng Y, Desnues C, Dias-Neto E, Dybwad M, Elhaik E, Ercolini D, Frolova A, Gankin D, Gootenberg JS, Graf AB, Green DC, Hajirasouliha I, Hastings JJA, Hernandez M, Iraola G, Jang S, Kahles A, Kelly FJ, Knights K, Kyrpides NC, Łabaj PP, Lee PKH, Leung MHY, Ljungdahl PO, Mason-Buck G, McGrath K, Meydan C, Mongodin EF, Moraes MOzorio, Nagarajan N, Nieto-Caballero M, Noushmehr H, Oliveira M, Ossowski S, Osuolale OO, Özcan O, Paez-Espino D, Rascovan N, Richard H, Rätsch G, Schriml LM, Semmler T, Sezerman OU, et al.. A global metagenomic map of urban microbiomes and antimicrobial resistance. Cell . (2021). |
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Muntoni AP, Pagnani A, Weigt M, Zamponi F. Aligning biological sequences by exploiting residue conservation and coevolution. Phys. Rev. E. 102, pp.062409 (2020). |
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Nivina A, Grieb MSvea, Loot C, Bikard D, Cury J, Shehata L, Bernardes JS, Mazel D. Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement. Science Advances. 6, (2020). |
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Bernardes JS, Eberle RJ, Vieira FRJ, Coronado MA. A comparative pan-genomic analysis of 53 C. pseudotuberculosis strains based on functional domains. Journal of Biomolecular Structure and Dynamics. pp.1-13 (2020). |
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Gandarilla-Pérez CA, Mergny P, Weigt M, Bitbol A-F. Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences. Phys. Rev. E. 101, pp.032413 (2020). |
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Muscat M, Croce G, Sarti E, Weigt M. FilterDCA: interpretable supervised contact prediction using inter-domain coevolution. PLOS Computational Biology. 16, (2020). |
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Russ WP, Figliuzzi M, Stocker C, Barrat-Charlaix P, Socolich M, Kast P, Hilvert D, Monasson R, Cocco S, Weigt M, Ranganathan R. An evolution-based model for designing chorismate mutase enzymes. Science. 369, pp.440–445 (2020). |
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Gueudré T, Baldassi C, Pagnani A, Weigt M Predicting Interacting Protein Pairs by Coevolutionary Paralog Matching. in Protein-Protein Interaction Networks. Methods in Molecular Biology,. Edited by: Canzar S., Ringeling F. 2074, New York, NY. Humana. (2020) |
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Reimer JM, Eivaskhani M, Harb I, Guarné A, Weigt M, T. Schmeing M. Structures of a dimodular nonribosomal peptide synthetase reveal conformational flexibility. Science. 366, (2019). |
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Rodriguez-Horta E, Barrat-Charlaix P, Weigt M. Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data. Entropy. 21, pp.1090 (2019). |
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Croce G, Gueudré T, Cuevas MVirginia R, Keidel V, Figliuzzi M, Szurmant H, Weigt M. A multi-scale coevolutionary approach to predict interactions between protein domains. PLOS Computational BiologyPLOS Computational Biology. 15(10), pp.e1006891 - (2019). |
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Marmier G, Weigt M, Bitbol A-F. Phylogenetic correlations can suffice to infer protein partners from sequences. PLOS Computational BiologyPLOS Computational Biology. 15(10), pp.e1007179 - (2019). |
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Shimagaki K, Weigt M. Selection of sequence motifs and generative Hopfield-Potts models for protein families. Phys. Rev. E. 100, pp.032128 (2019). |
