ReviewF1000Research2017
Co-evolution techniques are reshaping the way we do structural bioinformatics.
Review in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- PlasmoFP: leveraging deep learning to predict protein function of uncharacterized proteins across the malaria parasite genus.bioRxiv : the preprint server for biology · 2025Article
- State-of-the-Art and Future Directions in Structural Proteomics.Molecular & cellular proteomics : MCP · 2025Review
- Article
- Enhanced antibody-antigen structure prediction from molecular docking using AlphaFold2.Scientific reports · 2023Article
- Article
- Computational Docking Reveals Co-Evolution of C4 Carbon Delivery Enzymes in Diverse Plants.International journal of molecular sciences · 2022Article
- ConPlot: web-based application for the visualization of protein contact maps integrated with other data.Bioinformatics (Oxford, England) · 2021Article
- Machine Learning Approaches for Quality Assessment of Protein Structures.Biomolecules · 2020Review
- iScore: a novel graph kernel-based function for scoring protein-protein docking models.Bioinformatics (Oxford, England) · 2020Article
- StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map.Scientific reports · 2019Article
- Molecular replacement using structure predictions from databases.Acta crystallographica. Section D, Structural biology · 2019Article
- AlphaFold at CASP13.Bioinformatics (Oxford, England) · 2019Article
- ProteinNet: a standardized data set for machine learning of protein structure.BMC bioinformatics · 2019Article
- High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.Bioinformatics (Oxford, England) · 2018Article
- Integrating co-evolutionary signals and other properties of residue pairs to distinguish biological interfaces from crystal contacts.Protein science : a publication of the Protein Society · 2018Article
- Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.Bioinformatics (Oxford, England) · 2018Article
- Combined approaches from physics, statistics, and computer science forF1000Research · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Co-evolution techniques were originally conceived to assist in protein structure prediction by inferring pairs of residues that share spatial proximity. However, the functional relationships that can be extrapolated from co-evolution have also proven to be useful in a wide array of structural bioinformatics applications. These techniques are a powerful way to extract structural and functional information in a sequence-rich world.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.