ArticleBioinformatics (Oxford, England)2010
Improving protein secondary structure prediction using a simple k-mer model.
Article in Bioinformatics (Oxford, England), 2010. 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.
- The statistical power ofSynthetic and systems biotechnology · 2019Article
- Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees.Scientific reports · 2019Article
- Large-scale protein function prediction using heterogeneous ensembles.F1000Research · 2018Article
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- Article
- Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.Journal of molecular modeling · 2013Article
- Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction.Journal of molecular modeling · 2012Article
- Fast side chain replacement in proteins using a coarse-grained approach for evaluating the effects of mutation during evolution.Journal of molecular evolution · 2011Article
- A k-mer scheme to predict piRNAs and characterize locust piRNAs.Bioinformatics (Oxford, England) · 2011Article
- A series of PDB related databases for everyday needs.Nucleic acids research · 2011Article
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Authors and funding
5 authors.
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Abstract
motivationSome first order methods for protein sequence analysis inherently treat each position as independent. We develop a general framework for introducing longer range interactions. We then demonstrate the power of our approach by applying it to secondary structure prediction; under the independence assumption, sequences produced by existing methods can produce features that are not protein like, an extreme example being a helix of length 1. Our goal was to make the predictions from state of the art methods more realistic, without loss of performance by other measures.
resultsOur framework for longer range interactions is described as a k-mer order model. We succeeded in applying our model to the specific problem of secondary structure prediction, to be used as an additional layer on top of existing methods. We achieved our goal of making the predictions more realistic and protein like, and remarkably this also improved the overall performance. We improve the Segment OVerlap (SOV) score by 1.8%, but more importantly we radically improve the probability of the real sequence given a prediction from an average of 0.271 per residue to 0.385. Crucially, this improvement is obtained using no additional information. AVAILABILITY: http://supfam.cs.bris.ac.uk/kmer
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