ArticleCell2012
Three-dimensional structures of membrane proteins from genomic sequencing.
Article in Cell, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 277 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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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.
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Who cites it
277 citing papers in PubMed.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Organic cation transporter novel 1 (OCTN1): beyond an ergothioneine transporter.Cell communication and signaling : CCS · 2026Review
- Profile of David Baker, Demis Hassabis, and John Jumper: 2024 Nobel laureates in chemistry.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Disentangling coevolutionary constraints for modeling protein conformational heterogeneity.Communications chemistry · 2026Article
- Memorization bias impacts modeling of alternative conformational states of solute carrier membrane proteins with methods from deep learning.PLoS computational biology · 2025Article
- Long-range coupling regulates stator dynamics in the bacterial flagellar motor.bioRxiv : the preprint server for biology · 2025Article
- Memorization Bias Impacts Modeling of Alternative Conformational States of Symmetric Solute Carrier Membrane Proteins with Methods from Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Leveraging Sequence Purification for Accurate Prediction of Multiple Conformational States with AlphaFold2.Research square · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Continuous evolution of user-defined genes at 1 million times the genomic mutation rate.Science (New York, N.Y.) · 2024Article
- Accurate prediction of protein function using statistics-informed graph networks.Nature communications · 2024Article
- Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Simultaneous enhancement of multiple functional properties using evolution-informed protein design.Nature communications · 2024Article
- Extracellular domain 2 of TSPAN4 governs its functions.Biophysical reports · 2024Article
- pycofitness-Evaluating the fitness landscape of RNA and protein sequences.Bioinformatics (Oxford, England) · 2024Article
- Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction.Journal of chemical information and modeling · 2024Article
- Article
- Representing structures of the multiple conformational states of proteins.Current opinion in structural biology · 2023Review
- How AlphaFold2 shaped the structural coverage of the human transmembrane proteome.Scientific reports · 2023Article
217 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.
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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.