ArticleProteins2021
Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.
Article in Proteins, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Alphafuser: a parsimonious approach to predicting higher-order protein complexes.Acta crystallographica. Section D, Structural biology · 2026Article
- AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases.Frontiers in immunology · 2026Review
- Protein Structure Prediction Methods.Advances in experimental medicine and biology · 2026Review
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
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- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- In silico design ofHeliyon · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality.Biomolecules · 2024Article
- Evaluation of AlphaFold antibody-antigen modeling with implications for improving predictive accuracy.Protein science : a publication of the Protein Society · 2024Article
- Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy.bioRxiv : the preprint server for biology · 2023Article
- Prediction of CD44 Structure by Deep Learning-Based Protein Modeling.Biomolecules · 2023Article
- Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes.Annual review of biophysics · 2023Review
- PDC: a highly compact file format to store protein 3D coordinates.Database : the journal of biological databases and curation · 2023Article
- Structural Characterization of the Chlorophyllide a Oxygenase (CAO) Enzyme Through an In Silico Approach.Journal of molecular evolution · 2023Article
- De novo protein fold design through sequence-independent fragment assembly simulations.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- SEN1990 is a predicted winged helix-turn-helix protein involved in the pathogenicity ofFrontiers in microbiology · 2023Article
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8 authors.
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Abstract
In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues.
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