ArticleNature machine intelligence2021
Improved protein structure prediction by deep learning irrespective of co-evolution information.
Article in Nature machine intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers, 1 of them a synthesis that pooled it.
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Who cites it
100 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- Structure-based virulence factor classification using a dual-driven graph transformer with a pretrained language model.Briefings in bioinformatics · 2026Article
- The Transcriptional Regulator Crt1 is Involved in The Pathogenic Lifestyle ofPolish journal of microbiology · 2026Article
- Coevolutionary signals in multiple sequence alignments improve virulence factor prediction with an MSA Transformer.Scientific reports · 2025Article
- Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints.Communications biology · 2025Article
- Design of a multi-epitope vaccine against drug-resistant mycobacterium tuberculosis and mycobacterium bovis using reverse vaccinology.Scientific reports · 2025Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling.BMC bioinformatics · 2025Article
- AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Deep learning methods for protein function prediction.Proteomics · 2025Review
- Optimization design of interior space based on the two-stage deep learning network and Single sample-driven method.PloS one · 2025Article
- Crystal structure of Alzheimer's disease phospholipase D3 provides a molecular basis for understanding its normal and pathological functions.The FEBS journal · 2024Article
- Leishmania protein KMP-11 modulates cholesterol transport and membrane fluidity to facilitate host cell invasion.EMBO reports · 2024Article
- Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.Briefings in bioinformatics · 2024Article
- OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization.Nature methods · 2024Article
- Prediction of protein secondary structure by the improved TCN-BiLSTM-MHA model with knowledge distillation.Scientific reports · 2024Article
- A Perspective on Protein Structure Prediction Using Quantum Computers.Journal of chemical theory and computation · 2024Review
- Single-sequence protein structure prediction by integrating protein language models.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
40 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Predicting the tertiary structure of a protein from its primary sequence has been greatly improved by integrating deep learning and co-evolutionary analysis, as shown in CASP13 and CASP14. We describe our latest study of this idea, analyzing the efficacy of network size and co-evolution data and its performance on both natural and designed proteins. We show that a large ResNet (convolutional residual neural networks) can predict structures of correct folds for 26 out of 32 CASP13 free-modeling (FM) targets and L/5 long-range contacts with precision over 80%. When co-evolution is not used ResNet still can predict structures of correct folds for 18 CASP13 FM targets, greatly exceeding previous methods that do not use co-evolution either. Even with only primary sequence ResNet can predict structures of correct folds for all tested human-designed proteins. In addition, ResNet may fare better for the designed proteins when trained without co-evolution than with co-evolution. These results suggest that ResNet does not simply denoise co-evolution signals, but instead may learn important protein sequence-structure relationship. This has important implications on protein design and engineering especially when co-evolutionary data is unavailable.
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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.