ReviewCurrent opinion in structural biology2023
The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.
Review in Current opinion in structural biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Innovative approaches to therapeutic target discovery amid the global challenge of antimicrobial resistance.Journal of computer-aided molecular design · 2026Review
- DyVarMap: Integrating Conformational Dynamics and Interpretable Machine Learning for Cancer-Associated Missense Variant Classification in FGFR2.Bioengineering (Basel, Switzerland) · 2026Article
- Transcriptomic and miRNA Signatures of ChAdOx1 nCoV-19 Vaccine Response Using Machine Learning.Life (Basel, Switzerland) · 2025Article
- Computational prediction of CNP0387675 as a non-nucleoside inhibitor of MraY, from natural product-based multi-template screening againstFrontiers in microbiology · 2025Article
- Screening inhibitors against the Ef-Tu of Fusobacterium nucleatum: a docking, ADMET and PBPK assessment study.Molecular diversity · 2024Article
- Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit.Current protocols · 2024Article
- SERPINC1 c.1247dupC: a novel SERPINC1 gene mutation associated with familial thrombosis results in a secretion defect and quantitative antithrombin deficiency.Thrombosis journal · 2024Article
- Rosetta Energy Analysis of AlphaFold2 models: Point Mutations and Conformational Ensembles.bioRxiv : the preprint server for biology · 2024Article
- AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.Nucleic acids research · 2024Article
- The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.Proteins · 2023Article
- Channel Formation in Cry Toxins: An Alphafold-2 Perspective.International journal of molecular sciences · 2023Article
- The Impact of AI-Based Modeling on the Accuracy of Protein Assembly Prediction: Insights from CASP15.bioRxiv : the preprint server for biology · 2023Article
- The LightDock Server: Artificial Intelligence-powered modeling of macromolecular interactions.Nucleic acids research · 2023Article
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.
Funding
Abstract
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
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.