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Martin Weigt
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Found 48 results
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Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. Proceedings of the National Academy of Sciences. 119, pp. e2113118119 (2022).
. adabmDCA: adaptive Boltzmann machine learning for biological sequences. BMC Bioinformatics. 22(1), pp.528 (2021).
. On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins. PLOS Computational Biology. 17, pp.1-17 (2021).
. Efficient generative modeling of protein sequences using simple autoregressive models. Nature Communications. 12(1), pp.5800 (2021).
. Global multivariate model learning from hierarchically correlated data. J. Stat. Mech. (2021).
. Modeling Sequence-Space Exploration and Emergence of Epistatic Signals in Protein Evolution. Molecular Biology and Evolution. (2021).
. Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families. Phys. Rev. E. 104, pp.024407 (2021).
. Aligning biological sequences by exploiting residue conservation and coevolution. Phys. Rev. E. 102, pp.062409 (2020).
. An evolution-based model for designing chorismate mutase enzymes. Science. 369, pp.440–445 (2020).
. FilterDCA: interpretable supervised contact prediction using inter-domain coevolution. PLOS Computational Biology. 16, (2020).
. 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)
Statistical physics of interacting proteins: Impact of dataset size and quality assessed in synthetic sequences. Phys. Rev. E. 101, pp.032413 (2020).
. A multi-scale coevolutionary approach to predict interactions between protein domains. PLOS Computational BiologyPLOS Computational Biology. 15(10), pp.e1006891 - (2019).
. Phylogenetic correlations can suffice to infer protein partners from sequences. PLOS Computational BiologyPLOS Computational Biology. 15(10), pp.e1007179 - (2019).
. Selection of sequence motifs and generative Hopfield-Potts models for protein families. Phys. Rev. E. 100, pp.032128 (2019).
. Structures of a dimodular nonribosomal peptide synthetase reveal conformational flexibility. Science. 366, (2019).
. Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data. Entropy. 21, pp.1090 (2019).
. The evolution of the temporal program of genome replication. Nat Commun. 9(1), pp.2199 (2018).
. How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?. Molecular Biology and Evolution. pp.msy007 (2018).
. Meet-U: Educating through research immersion. PLOS Computational Biology. 14, pp.1-10 (2018).
. 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).
. Inter-residue, inter-protein and inter-family coevolution: bridging the scales. Curr Opin Struct Biol. 50, pp.26-32 (2017).
. Inverse Statistical Physics of Protein Sequences: A Key Issues Review. Rep. Prog. Phys. (2017).
. 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).
. 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).
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