ArticleBioinformatics (Oxford, England)2019
AlphaFold at CASP13.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 2 of them syntheses 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
134 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Inframe insertion and splice site variants in MFGE8 associate with protection against coronary atherosclerosis.Communications biology · 2022Pooled it
- Evolutionary Comparative Analyses of DNA-Editing Enzymes of the Immune System: From 5-Dimensional Description of Protein Structures to Immunological Insights and Applications to Protein Engineering.Frontiers in immunology · 2021Pooled it
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Optimization of Indole- and Pyrazole-fused Glycyrrhetinic Acid Derivatives as Potent PTP1B Inhibitors: In Silico, In Vitro, In Vivo, and Metabolomic Studies.ACS bio & med chem Au · 2026Article
- Neuromorphic photonic computing with an electro-optic analog memory.Nature communications · 2026Article
- Nanobodies as tools for studying human frataxin biology.Communications biology · 2026Article
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- A Comprehensive Review on Homology Modelling in Drug Development: Methodologies, Tools, and Evaluation.Mini reviews in medicinal chemistry · 2026Review
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Article
- The Inaugural Flatiron Institute Cryo-EM Conformational Heterogeneity Challenge.bioRxiv : the preprint server for biology · 2025Article
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Review
- A comprehensive application of FiveFold for conformation ensemble-based protein structure prediction.Scientific reports · 2025Review
- Graph neural network and diffusion model for modeling RNA interatomic interactions.Bioinformatics (Oxford, England) · 2025Article
- In Vitro Silencing of MHC-I in Keratinocytes by Herpesvirus US11 Protein to Model Alloreactive Suppression.European burn journal · 2025Article
- Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
74 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
1 author.
Funding
Abstract
summaryComputational prediction of protein structure from sequence is broadly viewed as a foundational problem of biochemistry and one of the most difficult challenges in bioinformatics. Once every two years the Critical Assessment of protein Structure Prediction (CASP) experiments are held to assess the state of the art in the field in a blind fashion, by presenting predictor groups with protein sequences whose structures have been solved but have not yet been made publicly available. The first CASP was organized in 1994, and the latest, CASP13, took place last December, when for the first time the industrial laboratory DeepMind entered the competition. DeepMind's entry, AlphaFold, placed first in the Free Modeling (FM) category, which assesses methods on their ability to predict novel protein folds (the Zhang group placed first in the Template-Based Modeling (TBM) category, which assess methods on predicting proteins whose folds are related to ones already in the Protein Data Bank.) DeepMind's success generated significant public interest. Their approach builds on two ideas developed in the academic community during the preceding decade: (i) the use of co-evolutionary analysis to map residue co-variation in protein sequence to physical contact in protein structure, and (ii) the application of deep neural networks to robustly identify patterns in protein sequence and co-evolutionary couplings and convert them into contact maps. In this Letter, we contextualize the significance of DeepMind's entry within the broader history of CASP, relate AlphaFold's methodological advances to prior work, and speculate on the future of this important problem.
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.