Martin Weigt

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).

Muntoni AP, Pagnani A, Weigt M, Zamponi F. adabmDCA: adaptive Boltzmann machine learning for biological sequences. BMC Bioinformatics. 22(1), pp.528 (2021).

Rodriguez-Horta E, Weigt M. On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins. PLOS Computational Biology. 17, pp.1-17 (2021).

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).

Rodriguez-Horta E, Lage A, Weigt M, Barrat-Charlaix P. Global multivariate model learning from hierarchically correlated data. J. Stat. Mech. (2021).

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).

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).

Muntoni AP, Pagnani A, Weigt M, Zamponi F. Aligning biological sequences by exploiting residue conservation and coevolution. Phys. Rev. E. 102, pp.062409 (2020).

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).

Muscat M, Croce G, Sarti E, Weigt M. FilterDCA: interpretable supervised contact prediction using inter-domain coevolution. PLOS Computational Biology. 16, (2020).

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)

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).

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).

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).

Shimagaki K, Weigt M. Selection of sequence motifs and generative Hopfield-Potts models for protein families. Phys. Rev. E. 100, pp.032128 (2019).

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).

Rodriguez-Horta E, Barrat-Charlaix P, Weigt M. Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data. Entropy. 21, pp.1090 (2019).

Agier N, Delmas S, Zhang Q, Fleiss A, Jaszczyszyn Y, van Dijk E, Thermes C, Weigt M, Cosentino Lagomarsino M, Fischer G. The evolution of the temporal program of genome replication. Nat Commun. 9(1), pp.2199 (2018).

Figliuzzi M, Barrat-Charlaix P, Weigt M. How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?. Molecular Biology and Evolution. pp.msy007 (2018).

Abdollahi N, Albani A, Anthony E, Baud A, Cardon M, Clerc R, Czernecki D, Conte R, David L, Delaune A, Djerroud S, Fourgoux P, Guiglielmoni N, Laurentie J, Lehmann N, Lochard C, Montagne R, Myrodia V, Opuu V, Parey E, Polit L, Privé S, Quignot C, Ruiz-Cuevas M, Sissoko M, Sompairac N, Vallerix A, Verrecchia V, Delarue M, Guérois R, Ponty Y, Sacquin-Mora S, Carbone A*, Froidevaux C, Le Crom S, Lespinet O, Weigt M, Abboud S, Bernardes JS, Bouvier G, Dequeker C, Ferré A, Fuchs P, Lelandais G, Poulain P, Richard H, Schweke H, Laine E, Lopes* A. Meet-U: Educating through research immersion. PLOS Computational Biology. 14, pp.1-10 (2018).

Barrat-Charlaix P, Weigt M. De la variabilité des séquences à la prédiction structurale et fonctionnelle : modélisation de familles de protéines homologues. Biologie Aujourd’hui. 211(3), pp.239-244 (2017).

Szurmant H, Weigt M. Inter-residue, inter-protein and inter-family coevolution: bridging the scales. Curr Opin Struct Biol. 50, pp.26-32 (2017).

Cocco S, Feinauer C, Figliuzzi M, Monasson R, Weigt M. Inverse Statistical Physics of Protein Sequences: A Key Issues Review. Rep. Prog. Phys. (2017).

Uguzzoni G, Lovis SJohn, Oteri F, Schug A, Szurmant H, Weigt M. Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis. Proceedings of the National Academy of Sciences. 114, pp.E2662-E2671 (2017).

Couce A, Caudwell LViraphong, Feinauer C, Hindré T, Feugeas J-P, Weigt M, Lenski RE, Schneider D, Tenaillon O. Mutator genomes decay, despite sustained fitness gains, in a long-term experiment with bacteria. Proceedings of the National Academy of Sciences. 114, pp.E9026-E9035 (2017).