ReviewF1000Research2016
Sequence co-evolutionary information is a natural partner to minimally-frustrated models of biomolecular dynamics.
Review in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Divergence in Dimerization and Activity of Primate APOBEC3C.Journal of molecular biology · 2021Article
- The generative capacity of probabilistic protein sequence models.Nature communications · 2021Article
- Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus.Biophysical journal · 2021Article
- Glutantβase: a database for improving the rational design of glucose-tolerant β-glucosidases.BMC molecular and cell biology · 2020Article
- Modular Organization of Residue-Level Contacts Shapes the Selection Pressure on Individual Amino Acid Sites of Ribosomal Proteins.Genome biology and evolution · 2017Article
- Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Connecting the Sequence-Space of Bacterial Signaling Proteins to Phenotypes Using Coevolutionary Landscapes.Molecular biology and evolution · 2016Article
- SMOG 2: A Versatile Software Package for Generating Structure-Based Models.PLoS computational biology · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Experimentally derived structural constraints have been crucial to the implementation of computational models of biomolecular dynamics. For example, not only does crystallography provide essential starting points for molecular simulations but also high-resolution structures permit for parameterization of simplified models. Since the energy landscapes for proteins and other biomolecules have been shown to be minimally frustrated and therefore funneled, these structure-based models have played a major role in understanding the mechanisms governing folding and many functions of these systems. Structural information, however, may be limited in many interesting cases. Recently, the statistical analysis of residue co-evolution in families of protein sequences has provided a complementary method of discovering residue-residue contact interactions involved in functional configurations. These functional configurations are often transient and difficult to capture experimentally. Thus, co-evolutionary information can be merged with that available for experimentally characterized low free-energy structures, in order to more fully capture the true underlying biomolecular energy landscape.
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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.