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Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models

Physical review. E, 2013, Vol.87 (1), p.012707-012707, Article 012707

2013

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How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?

Molecular biology and evolution, 2018-04, Vol.35 (4), p.1018-1027

2018

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Inverse statistical physics of protein sequences: a key issues review

Reports on progress in physics, 2018-03, Vol.81 (3), p.032601-032601

2018

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On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins

PLoS computational biology, 2021-05, Vol.17 (5), p.e1008957-e1008957

2021

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Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

Molecular biology and evolution, 2016-01, Vol.33 (1), p.268-280

2016

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From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction

PLoS computational biology, 2013-08, Vol.9 (8), p.e1003176-e1003176

2013

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An evolution-based model for designing chorismate mutase enzymes

Science (American Association for the Advancement of Science), 2020-07, Vol.369 (6502), p.440-445

2020

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Efficient generative modeling of protein sequences using simple autoregressive models

Nature communications, 2021-10, Vol.12 (1), p.5800-5800, Article 5800

2021

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Structures of a dimodular nonribosomal peptide synthetase reveal conformational flexibility

Science (American Association for the Advancement of Science), 2019-11, Vol.366 (6466)

2019

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Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon

PloS one, 2016-02, Vol.11 (2), p.e0149166-e0149166

2016

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Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Proceedings of the National Academy of Sciences - PNAS, 2011-12, Vol.108 (49), p.E1293-E1301

2011

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Phylogenetic correlations can suffice to infer protein partners from sequences

PLoS computational biology, 2019-10, Vol.15 (10), p.e1007179-e1007179

2019

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Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners

PloS one, 2014-03, Vol.9 (3), p.e92721-e92721

2014

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Phase transitions in combinatorial optimization problems: basics, algorithms and statistical mechanics.

1, 2006

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Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

PLoS computational biology, 2023-03, Vol.19 (3), p.e1011010-e1011010

2023

Volltextzugriff (PDF)

adabmDCA: adaptive Boltzmann machine learning for biological sequences

BMC bioinformatics, 2021-10, Vol.22 (1), p.1-528, Article 528

2021

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Identification of direct residue contacts in protein-protein interaction by message passing

Proceedings of the National Academy of Sciences - PNAS, 2009-01, Vol.106 (1), p.67-72

2009

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FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution

PLoS computational biology, 2020-10, Vol.16 (10), p.e1007621-e1007621

2020

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Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

Proceedings of the National Academy of Sciences - PNAS, 2017-03, Vol.114 (13), p.E2662-E2671

2017

Volltextzugriff (PDF)

Genomics-aided structure prediction

Proceedings of the National Academy of Sciences - PNAS, 2012-06, Vol.109 (26), p.10340-10345

2012

Volltextzugriff (PDF)

Deep template-based protein structure prediction

PLoS computational biology, 2021-05, Vol.17 (5), p.e1008954-e1008954

2021

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Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes

Nature communications, 2022-07, Vol.13 (1), p.4030-4030, Article 4030

2022

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GENERALIST: A latent space based generative model for protein sequence families

PLoS computational biology, 2023-11, Vol.19 (11), p.e1011655-e1011655

2023

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Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis

Proceedings of the National Academy of Sciences - PNAS, 2012-06, Vol.109 (26), p.E1733-E1742

2012

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Author Correction: Efficient generative modeling of protein sequences using simple autoregressive models

Nature communications, 2022-04, Vol.13 (1), p.1889-1889, Article 1889

2022

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Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models

How Pairwise Coevolutionary Models Capture the Collective Residue Variability in Proteins?

Inverse statistical physics of protein sequences: a key issues review

On the effect of phylogenetic correlations in coevolution-based contact prediction in proteins

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1

From principal component to direct coupling analysis of coevolution in proteins: low-eigenvalue modes are needed for structure prediction

An evolution-based model for designing chorismate mutase enzymes

Efficient generative modeling of protein sequences using simple autoregressive models

Structures of a dimodular nonribosomal peptide synthetase reveal conformational flexibility

Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

Phylogenetic correlations can suffice to infer protein partners from sequences

Fast and accurate multivariate Gaussian modeling of protein families: predicting residue contacts and protein-interaction partners

Phase transitions in combinatorial optimization problems: basics, algorithms and statistical mechanics.

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

adabmDCA: adaptive Boltzmann machine learning for biological sequences

Identification of direct residue contacts in protein-protein interaction by message passing

FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution

Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis

Genomics-aided structure prediction

Deep template-based protein structure prediction

Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes

GENERALIST: A latent space based generative model for protein sequence families

Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis

Author Correction: Efficient generative modeling of protein sequences using simple autoregressive models

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