ArticleScientific reports2019
DESTINI: A deep-learning approach to contact-driven protein structure prediction.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Review
- Conserved structural topologies in RNase A-like and trypsin-like serine proteases: a sequence-based folding analysis.BMC molecular and cell biology · 2025Article
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries.Molecular diversity · 2022Review
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
- Article
- High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function.Workshop on Machine Learning in HPC Environments. Workshop on Machine Learning in HPC Environments · 2021Article
- AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function.Journal of chemical information and modeling · 2021Article
- Mapping the glycosyltransferase fold landscape using interpretable deep learning.Nature communications · 2021Article
- Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.International journal of molecular sciences · 2021Review
- The role of local versus nonlocal physicochemical restraints in determining protein native structure.Current opinion in structural biology · 2021Review
- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
- A novel sequence alignment algorithm based on deep learning of the protein folding code.Bioinformatics (Oxford, England) · 2021Article
- AptaNet as a deep learning approach for aptamer-protein interaction prediction.Scientific reports · 2021Article
- Incorporating Machine Learning into Established Bioinformatics Frameworks.International journal of molecular sciences · 2021Review
- Genomic insights into the host specific adaptation of the Pneumocystis genus.Communications biology · 2021Article
- Deep Learning in Proteomics.Proteomics · 2020Review
- GLN: a method to reveal unique properties of lasso type topology in proteins.Scientific reports · 2020Article
- Experimentally-driven protein structure modeling.Journal of proteomics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DESTINI (deep structural inference for proteins), a novel computational approach that combines a deep-learning algorithm for protein residue/residue contact prediction with template-based structural modelling. For the first time, the significantly improved predictive ability is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain proteins. DESTINI successfully predicts the tertiary structure of four times the number of "hard" targets (those with poor quality templates) that were previously intractable, viz, a "glass-ceiling" for previous template-based approaches, and also improves model quality for "easy" targets (those with good quality templates). The significantly better performance by DESTINI is largely due to the incorporation of better contact prediction into template modelling. To understand why deep-learning accomplishes more accurate contact prediction, systematic clustering reveals that deep-learning predicts coherent, native-like contact patterns compared to co-evolutionary analysis. Taken together, this work presents a promising strategy towards solving the protein structure prediction 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.