ArticlePloS one2019
DeepCDpred: Inter-residue distance and contact prediction for improved prediction of protein structure.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed, 30 citations in OpenAlex.
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.Scientific reports · 2022Article
- Evaluation of Deep Neural Network ProSPr for Accurate Protein Distance Predictions on CASP14 Targets.International journal of molecular sciences · 2021Article
- Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding.Journal of chemical theory and computation · 2021Article
- Protein Structure Prediction: Conventional and Deep Learning Perspectives.The protein journal · 2021Review
- Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Evaluation of residue-residue contact prediction methods: From retrospective to prospective.PLoS computational biology · 2021Article
- The whole is greater than its parts: ensembling improves protein contact prediction.Scientific reports · 2021Article
- Deep learning methods in protein structure prediction.Computational and structural biotechnology journal · 2020Review
- Homology modeling in the time of collective and artificial intelligence.Computational and structural biotechnology journal · 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
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid, accurate prediction of protein structure from amino acid sequence would accelerate fields as diverse as drug discovery, synthetic biology and disease diagnosis. Massively improved prediction of protein structures has been driven by improving the prediction of the amino acid residues that contact in their 3D structure. For an average globular protein, around 92% of all residue pairs are non-contacting, therefore accurate prediction of only a small percentage of inter-amino acid distances could increase the number of constraints to guide structure determination. We have trained deep neural networks to predict inter-residue contacts and distances. Distances are predicted with an accuracy better than most contact prediction techniques. Addition of distance constraints improved de novo structure predictions for test sets of 158 protein structures, as compared to using the best contact prediction methods alone. Importantly, usage of distance predictions allows the selection of better models from the structure pool without a need for an external model assessment tool. The results also indicate how the accuracy of distance prediction methods might be improved further.
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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.