ArticleBMC bioinformatics2006
Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep learning in structural bioinformatics: current applications and future perspectives.Briefings in bioinformatics · 2024Review
- RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites.Computational and structural biotechnology journal · 2020Article
- Coronavirus envelope protein: current knowledge.Virology journal · 2019Review
- Role of amino-terminal half of the S4-S5 linker in type 1 ryanodine receptor (RyR1) channel gating.The Journal of biological chemistry · 2011Article
- Article
- Union of geometric constraint-based simulations with molecular dynamics for protein structure prediction.Biophysical journal · 2010Article
- A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins.BMC genomics · 2009Article
- Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction.Structure (London, England : 1993) · 2009Article
- Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs.BMC structural biology · 2007Article
- STAR: predicting recombination sites from amino acid sequence.BMC bioinformatics · 2006Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models.
resultsSeveral probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues.
conclusionCascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.
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