ArticleNature communications2021
Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
47 citing papers in PubMed, 93 citations in OpenAlex.
- Performance of pyDock in 8th CAPRI: Energy-Based Scoring Applied to Docking and AlphaFold Models.Proteins · 2026Article
- Immunoinformatics-based design of artificial chimeric proteins as universal vaccine candidates against foot-and-mouth disease virus serotypes A, O, and SAT2.Scientific reports · 2026Article
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Unstructured, disulfide-bridged C-terminus in helminth α-helical antimicrobial peptides enhances and modulates their activity.Scientific reports · 2026Article
- Revisiting Eck and Dayhoff's Building Block Model of Ferredoxin Evolution on Dayhoff's 100th Birthday.Journal of molecular evolution · 2026Article
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- From latent dynamics to data geometry: Nonlinear diffusion modelling for protein structures.Computational and structural biotechnology journal · 2026Article
- Natural variants of von Willebrand factor R1205 causing von Willebrand disease with accelerated von Willebrand factor clearance: In silico docking models and energetics of the interaction with both LRP1 and GpIb A1 domain.PLoS computational biology · 2025Article
- Article
- A comprehensive benchmarking of the AlphaFold3 for predicting biomacromolecules and their interactions.Briefings in bioinformatics · 2025Article
- Enhanced Methodology for Peptide Tertiary Structure Prediction Using GRSA and Bio-Inspired Algorithm.International journal of molecular sciences · 2025Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- A precision medicine approach to primary immunodeficiency disease: Ataluren strikes nonsense mutations once again.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Article
- Article
- Insight into Protein Engineering: FromCurrent pharmaceutical design · 2025Review
- ProT-GFDM: A generative fractional diffusion model for protein generation.Computational and structural biotechnology journal · 2025Article
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Overview of Molecular Modeling in Drug Discovery with a Special Emphasis on the Applications of Artificial Intelligence.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Article
- Role of Peptide Associations in Enhancing the Antimicrobial Activity of Adepantins: Comparative Molecular Dynamics Simulations and Design Assessments.International journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.
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.