ArticleProtein science : a publication of the Protein Society2002
Prediction of novel archaeal enzymes from sequence-derived features.
Article in Protein science : a publication of the Protein Society, 2002. 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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Who cites it
12 citing papers in PubMed.
- Protein feature engineering framework for AMPylation site prediction.Scientific reports · 2024Article
- A general model for predicting enzyme functions based on enzymatic reactions.Journal of cheminformatics · 2024Article
- PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes.BMC bioinformatics · 2024Article
- Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.Frontiers in genetics · 2021Article
- Identification of Human Enzymes Using Amino Acid Composition and the Composition ofBioMed research international · 2020Article
- Inaccurate secondary structure predictions often indicate protein fold switching.Protein science : a publication of the Protein Society · 2019Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
- A top-down approach to classify enzyme functional classes and sub-classes using random forest.EURASIP journal on bioinformatics & systems biology · 2012Article
- Identification of protein functions using a machine-learning approach based on sequence-derived properties.Proteome science · 2009Article
- An evidence of laccases in archaea.Indian journal of microbiology · 2009Article
- Prediction of enzymes and non-enzymes from protein sequences based on sequence derived features and PSSM matrix using artificial neural network.Bioinformation · 2007Article
- Predicting protein function by machine learning on amino acid sequences--a critical evaluation.BMC genomics · 2007Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
The completely sequenced archaeal genomes potentially encode, among their many functionally uncharacterized genes, novel enzymes of biotechnological interest. We have developed a prediction method for detection and classification of enzymes from sequence alone (available at http://www.cbs.dtu.dk/services/ArchaeaFun/). The method does not make use of sequence similarity; rather, it relies on predicted protein features like cotranslational and posttranslational modifications, secondary structure, and simple physical/chemical properties.
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Registered trials
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