ArticleProteins2018
Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age.
Article in Proteins, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
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
89 citing papers in PubMed, 1 synthesis or guideline pooled it, 199 citations in OpenAlex.
- A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic.Frontiers in genetics · 2022Pooled it
- An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms.Current research in structural biology · 2025Article
- Computational analyses of drug resistance mutations in katG and emb complexes in Mycobacterium tuberculosis.Proteins · 2025Article
- Interpreting forces as deep learning gradients improves quality of predicted protein structures.Biophysical journal · 2024Article
- Assessing the functional roles of coevolving PHD finger residues.Protein science : a publication of the Protein Society · 2024Article
- Tertiary structure assessment at CASP15.Proteins · 2023Article
- Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data.The Journal of chemical physics · 2023Article
- Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development.International journal of molecular sciences · 2023Review
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- Building Protein Atomic Models from Cryo-EM Density Maps and Residue Co-Evolution.Biomolecules · 2022Article
- Towards Molecular Understanding of the Functional Role of UbiJ-UbiKInternational journal of molecular sciences · 2022Article
- Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.Bioinformatics (Oxford, England) · 2022Article
- From systems to structure - using genetic data to model protein structures.Nature reviews. Genetics · 2022Review
- Evolutionary Insights into the Microneme-Secreted, Chitinase-Containing High-Molecular-Weight Protein Complexes Involved inInfection and immunity · 2022Article
- Combining cysteine scanning with chemical labeling to map protein-protein interactions and infer bound structure in an intrinsically disordered region.Frontiers in molecular biosciences · 2022Article
- The Importance of Weakly Co-Evolving Residue Networks in Proteins is Revealed by Visual Analytics.Frontiers in bioinformatics · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- Using artificial intelligence technology to fight COVID-19: a review.Artificial intelligence review · 2022Article
- Article
- The trRosetta server for fast and accurate protein structure prediction.Nature protocols · 2021Review
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Following up on the encouraging results of residue-residue contact prediction in the CASP11 experiment, we present the analysis of predictions submitted for CASP12. The submissions include predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology-based modeling due to a lack of structural templates. CASP11 saw a rise of coevolution-based methods outperforming other approaches. The improvement of these methods coupled to machine learning and sequence database growth are most likely the main driver for a significant improvement in average precision from 27% in CASP11 to 47% in CASP12. In more than half of the targets, especially those with many homologous sequences accessible, precisions above 90% were achieved with the best predictors reaching a precision of 100% in some cases. We furthermore tested the impact of using these contacts as restraints in ab initio modeling of 14 single-domain free modeling targets using Rosetta. Adding contacts to the Rosetta calculations resulted in improvements of up to 26% in GDT_TS within the top five structures.
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.