ArticleIEEE/ACM transactions on computational biology and bioinformatics
New Deep Learning Methods for Protein Loop Modeling.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.Scientific reports · 2025Article
- Selection among site-dependent structurally constrained substitution models of protein evolution by approximate Bayesian computation.Bioinformatics (Oxford, England) · 2024Article
- PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN.ACS omega · 2024Article
- Highly Accurate and Efficient Deep Learning Paradigm for Full-Atom Protein Loop Modeling with KarmaLoop.Research (Washington, D.C.) · 2024Article
- Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.Briefings in bioinformatics · 2023Article
- Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction.Neural computing & applications · 2023Article
- Deep Learning in Diverse Intelligent Sensor Based Systems.Sensors (Basel, Switzerland) · 2022Review
- Structural dissection of sequence recognition and catalytic mechanism of human LINE-1 endonuclease.Nucleic acids research · 2021Article
- AI applications in functional genomics.Computational and structural biotechnology journal · 2021Review
- Deep Learning in Protein Structural Modeling and Design.Patterns (New York, N.Y.) · 2020Review
- Computational design of structured loops for new protein functions.Biological chemistry · 2019Review
- A Graphic Encoding Method for Quantitative Classification of Protein Structure and Representation of Conformational Changes.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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4 authors.
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Abstract
Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1, and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique. ConLooper applies deep convolutional neural networks to predict ${\mathrm{C}}_{{{\alpha }}}$Cα atoms distance matrix as an orientation-independent representation of protein structure. ResLooper uses residual neural networks instead of deep convolutional neural networks. HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one.
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