ArticleBMC genomics2020
Template-based prediction of protein structure with deep learning.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Protein foundation models: a comprehensive survey.Science China. Life sciences · 2026Review
- Structure evaluation coupled with all-atom molecular dynamics and advanced quantum mechanical DFT revealed kaempferol as a potent binding flavonol for the epigenetic-target HDAC9.Journal of biosciences · 2026Article
- Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.Molecular biotechnology · 2025Review
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- AI-Driven Deep Learning Techniques in Protein Structure Prediction.International journal of molecular sciences · 2024Review
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- An interactive visualization tool for educational outreach in protein contact map overlap analysis.Frontiers in bioinformatics · 2024Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Advancements in small molecule drug design: A structural perspective.Drug discovery today · 2023Review
- Scalable Inhibitors of the Nsp3-Nsp4 Coupling in SARS-CoV-2.ACS omega · 2023Article
- Engagement of intrinsic disordered proteins in protein-protein interaction.Frontiers in molecular biosciences · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- RNA Cleavage Properties of Nucleobase-Specific RNase MC1 and Cusativin Are Determined by the Dinucleotide-Binding Interactions in the Enzyme-Active Site.International journal of molecular sciences · 2022Article
- DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins.Proteins · 2022Article
- Current Approaches in Supersecondary Structures Investigation.International journal of molecular sciences · 2021Review
- Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading.Frontiers in molecular biosciences · 2021Review
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2020: Scalable techniques and algorithms for computational genomics.BMC genomics · 2020Article
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2 authors.
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Abstract
backgroundAccurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available.
resultsWe propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13's TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively.
conclusionsThese results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.
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