ArticleBioinformatics (Oxford, England)2017
NeBcon: protein contact map prediction using neural network training coupled with naïve Bayes classifiers.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- NEFFy: a versatile tool for computing the number of effective sequences.Bioinformatics (Oxford, England) · 2026Article
- Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.Nature biotechnology · 2026Article
- Memorization bias impacts modeling of alternative conformational states of solute carrier membrane proteins with methods from deep learning.PLoS computational biology · 2025Article
- Memorization Bias Impacts Modeling of Alternative Conformational States of Symmetric Solute Carrier Membrane Proteins with Methods from Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction.Biomolecules · 2024Review
- Foundation models in molecular biology.Biophysics reports · 2024Article
- Application of the C3-Methyltransferase StspM1 for the Synthesis of the Natural Pyrroloindole Motif.ACS catalysis · 2024Article
- Microbiome-based enrichment pattern mining has enabled a deeper understanding of the biome-species-function relationship.Communications biology · 2023Article
- Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.ACS omega · 2023Article
- Contact-Assisted Threading in Low-Homology Protein Modeling.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Prediction of the tetramer protein complex interaction based on CNN and SVM.Frontiers in genetics · 2023Article
- I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.Nature protocols · 2022Review
- Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).The protein journal · 2022Article
- Inter-Residue Distance Prediction From Duet Deep Learning Models.Frontiers in genetics · 2022Article
- Decoding the link of microbiome niches with homologous sequences enables accurately targeted protein structure prediction.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.Proteins · 2021Article
- Article
- Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.Briefings in bioinformatics · 2021Article
- Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions.Nature communications · 2021Article
- Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.Cell reports methods · 2021Article
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5 authors.
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Abstract
motivationRecent CASP experiments have witnessed exciting progress on folding large-size non-humongous proteins with the assistance of co-evolution based contact predictions. The success is however anecdotal due to the requirement of the contact prediction methods for the high volume of sequence homologs that are not available to most of the non-humongous protein targets. Development of efficient methods that can generate balanced and reliable contact maps for different type of protein targets is essential to enhance the success rate of the ab initio protein structure prediction.
resultsWe developed a new pipeline, NeBcon, which uses the naïve Bayes classifier (NBC) theorem to combine eight state of the art contact methods that are built from co-evolution and machine learning approaches. The posterior probabilities of the NBC model are then trained with intrinsic structural features through neural network learning for the final contact map prediction. NeBcon was tested on 98 non-redundant proteins, which improves the accuracy of the best co-evolution based meta-server predictor by 22%; the magnitude of the improvement increases to 45% for the hard targets that lack sequence and structural homologs in the databases. Detailed data analysis showed that the major contribution to the improvement is due to the optimized NBC combination of the complementary information from both co-evolution and machine learning predictions. The neural network training also helps to improve the coupling of the NBC posterior probability and the intrinsic structural features, which were found particularly important for the proteins that do not have sufficient number of homologous sequences to derive reliable co-evolution profiles. AVAILIABLITY AND IMPLEMENTATION: On-line server and standalone package of the program are available at http://zhanglab.ccmb.med.umich.edu/NeBcon/ . CONTACT: zhng@umich.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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