Literature DB >> 12441387

Prediction of novel archaeal enzymes from sequence-derived features.

Lars Juhl Jensen1, Marie Skovgaard, Søren Brunak.   

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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Year:  2002        PMID: 12441387      PMCID: PMC2373754          DOI: 10.1110/ps.0225102

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  20 in total

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3.  GenBank.

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4.  Predictome: a database of putative functional links between proteins.

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5.  Evolutionary analysis by whole-genome comparisons.

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8.  The Pfam protein families database.

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9.  Predicting protein function from structure: unique structural features of proteases.

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Journal:  Proc Natl Acad Sci U S A       Date:  2000-04-11       Impact factor: 11.205

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4.  Predicting protein function by machine learning on amino acid sequences--a critical evaluation.

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Review 6.  A survey of computational intelligence techniques in protein function prediction.

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Journal:  Int J Proteomics       Date:  2014-12-11

7.  Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs.

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8.  Inaccurate secondary structure predictions often indicate protein fold switching.

Authors:  Soumya Mishra; Loren L Looger; Lauren L Porter
Journal:  Protein Sci       Date:  2019-06-17       Impact factor: 6.725

9.  Identification of Enzymes-specific Protein Domain Based on DDE, and Convolutional Neural Network.

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  9 in total

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