ArticlePLoS computational biology2014
Improving contact prediction along three dimensions.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed.
- adabmDCA 2.0-A Flexible but Easy-to-Use Package for Direct Coupling Analysis.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Reconstruction of Ancestral Protein Sequences Using Autoregressive Generative Models.Molecular biology and evolution · 2025Article
- Interpretable pairwise distillations for generative protein sequence models.PLoS computational biology · 2022Article
- On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction.NAR genomics and bioinformatics · 2021Article
- Hybrid methods for combined experimental and computational determination of protein structure.The Journal of chemical physics · 2020Review
- Accurate inference of the full base-pairing structure of RNA by deep mutational scanning and covariation-induced deviation of activity.Nucleic acids research · 2020Article
- Coevolutionary Analysis of Protein Subfamilies by Sequence Reweighting.Entropy (Basel, Switzerland) · 2020Article
- Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.Scientific reports · 2019Article
- Genome-wide epistasis and co-selection study using mutual information.Nucleic acids research · 2019Article
- The intrinsic dimension of protein sequence evolution.PLoS computational biology · 2019Article
- Article
- Influence of multiple-sequence-alignment depth on Potts statistical models of protein covariation.Physical review. E · 2019Article
- hoDCA: higher order direct-coupling analysis.BMC bioinformatics · 2018Article
- Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction.PLoS computational biology · 2018Article
- MISTIC2: comprehensive server to study coevolution in protein families.Nucleic acids research · 2018Article
- SuperDCA for genome-wide epistasis analysis.Microbial genomics · 2018Article
- Identifying functionally informative evolutionary sequence profiles.Bioinformatics (Oxford, England) · 2018Article
- Applications of sequence coevolution in membrane protein biochemistry.Biochimica et biophysica acta. Biomembranes · 2018Review
- Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age.Proteins · 2018Article
- Patterns of coevolving amino acids unveil structural and dynamical domains.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
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4 authors.
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Abstract
Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members. The typical pipeline to address this task, which we in this paper refer to as the three dimensions of contact prediction, is to (i) filter and align the raw sequence data representing the evolutionarily related proteins; (ii) choose a predictive model to describe a sequence alignment; (iii) infer the model parameters and interpret them in terms of structural properties, such as an accurate contact map. We show here that all three dimensions are important for overall prediction success. In particular, we show that it is possible to improve significantly along the second dimension by going beyond the pair-wise Potts models from statistical physics, which have hitherto been the focus of the field. These (simple) extensions are motivated by multiple sequence alignments often containing long stretches of gaps which, as a data feature, would be rather untypical for independent samples drawn from a Potts model. Using a large test set of proteins we show that the combined improvements along the three dimensions are as large as any reported to date.
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