ArticlePLoS computational biology2022
Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Kaposi's Sarcoma-associated herpesvirus uses a novel protein fold to hijack RNA Polymerase II for viral late gene transcription.bioRxiv : the preprint server for biology · 2026Article
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Revisiting Eck and Dayhoff's Building Block Model of Ferredoxin Evolution on Dayhoff's 100th Birthday.Journal of molecular evolution · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Large Numbers of New Human Paralogs Discovered.bioRxiv : the preprint server for biology · 2025Article
- Article
- Deep eutectic solvent enhances antibacterial activity of a modular lytic enzyme against Acinetobacter baumannii.Scientific reports · 2025Article
- Artificial Intelligence (AI)-Based Protein Structure Prediction and Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deep generative design of RNA aptamers using structural predictions.Nature computational science · 2024Article
- Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism.Biological research · 2024Article
- AI-Driven Deep Learning Techniques in Protein Structure Prediction.International journal of molecular sciences · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Article
- Article
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- In Silico Approaches for Prediction of Anti-CRISPR Proteins.Journal of molecular biology · 2023Review
- Computational and Functional Insights of Protein Misfolding in Neurodegeneration.Advances in experimental medicine and biology · 2023Article
- Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.Frontiers in bioinformatics · 2023Review
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Authors and funding
4 authors.
Funding
Abstract
Despite the immense progress recently witnessed in protein structure prediction, the modeling accuracy for proteins that lack sequence and/or structure homologs remains to be improved. We developed an open-source program, DeepFold, which integrates spatial restraints predicted by multi-task deep residual neural-networks along with a knowledge-based energy function to guide its gradient-descent folding simulations. The results on large-scale benchmark tests showed that DeepFold creates full-length models with accuracy significantly beyond classical folding approaches and other leading deep learning methods. Of particular interest is the modeling performance on the most difficult targets with very few homologous sequences, where DeepFold achieved an average TM-score that was 40.3% higher than trRosetta and 44.9% higher than DMPfold. Furthermore, the folding simulations for DeepFold were 262 times faster than traditional fragment assembly simulations. These results demonstrate the power of accurately predicted deep learning potentials to improve both the accuracy and speed of ab initio protein structure prediction.
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