ArticleProteins2019
Assessing the accuracy of contact predictions in CASP13.
Article in Proteins, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- The 3D mutational constraint on amino acid sites in the human proteome.Nature communications · 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
- Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Article
- Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.International journal of molecular sciences · 2021Article
- FALCON2: a web server for high-quality prediction of protein tertiary structures.BMC bioinformatics · 2021Article
- A Multitask Deep-Learning Method for Predicting Membrane Associations and Secondary Structures of Proteins.Journal of proteome research · 2021Article
- Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.Cell reports methods · 2021Article
- Improved protein structure prediction by deep learning irrespective of co-evolution information.Nature machine intelligence · 2021Article
- DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning.Scientific reports · 2021Article
- Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction.Proteins · 2021Article
- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
- Fold recognition by scoring protein maps using the congruence coefficient.Bioinformatics (Oxford, England) · 2021Article
- The whole is greater than its parts: ensembling improves protein contact prediction.Scientific reports · 2021Article
- Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation.Journal of chemical theory and computation · 2021Article
- Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.PLoS computational biology · 2021Article
- Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks.PLoS computational biology · 2020Article
- Deep Learning in Proteomics.Proteomics · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The accuracy of sequence-based tertiary contact predictions was assessed in a blind prediction experiment at the CASP13 meeting. After 4 years of significant improvements in prediction accuracy, another dramatic advance has taken place since CASP12 was held 2 years ago. The precision of predicting the top L/5 contacts in the free modeling category, where L is the corresponding length of the protein in residues, has exceeded 70%. As a comparison, the best-performing group at CASP12 with a 47% precision would have finished below the top 1/3 of the CASP13 groups. Extensively trained deep neural network approaches dominate the top performing algorithms, which appear to efficiently integrate information on coevolving residues and interacting fragments or possibly utilize memories of sequence similarities and sometimes can deliver accurate results even in the absence of virtually any target specific evolutionary information. If the current performance is evaluated by F-score on L contacts, it stands around 24% right now, which, despite the tremendous impact and advance in improving its utility for structure modeling, also suggests that there is much room left for further improvement.
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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.