ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2020
Predicting the Real-Valued Inter-Residue Distances for Proteins.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Computationally Evidence-Grounded Sequence-First Design of Peptide Binders.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms.Current research in structural biology · 2025Article
- Freeprotmap: waiting-free prediction method for protein distance map.BMC bioinformatics · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Thermoresponsive Polypeptide Fused L-Asparaginase with Mitigated Immunogenicity and Enhanced Efficacy in Treating Hematologic Malignancies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Data Size and Quality Matter: Generating Physically-Realistic Distance Maps of Protein Tertiary Structures.Biomolecules · 2022Article
- Rapid prediction of protein natural frequencies using graph neural networks.Digital discovery · 2022Article
- Protein design via deep learning.Briefings in bioinformatics · 2022Review
- A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.Briefings in bioinformatics · 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
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- Improved protein structure prediction by deep learning irrespective of co-evolution information.Nature machine intelligence · 2021Article
- Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction.BMC bioinformatics · 2021Article
- Deep Learning-Based Advances in Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Generative Adversarial Learning of Protein Tertiary Structures.Molecules (Basel, Switzerland) · 2021Article
- Generating tertiary protein structures via interpretable graph variational autoencoders.Bioinformatics advances · 2021Article
- Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.Frontiers in molecular biosciences · 2021Review
- Predicting the Real-Valued Inter-Residue Distances for Proteins.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Predicting protein structure from the amino acid sequence has been a challenge with theoretical and practical significance in biophysics. Despite the recent progresses elicited by improved inter-residue contact prediction, contact-based structure prediction has gradually reached the performance ceiling. New methods have been proposed to predict the inter-residue distance, but unanimously by simplifying the real-valued distance prediction into a multiclass classification problem. Here, a lightweight regression-based distance prediction method is shown, which adopts the generative adversarial network to capture the delicate geometric relationship between residue pairs and thus could predict the continuous, real-valued inter-residue distance rapidly and satisfactorily. The predicted residue distance map allows quick structure modeling by the CNS suite, and the constructed models approach the same level of quality as the other state-of-the-art protein structure prediction methods when tested on CASP13 targets. Moreover, this method can be used directly for the structure prediction of membrane proteins without transfer learning.
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