Literature DB >> 6643432

Classification of proteins into groups based on amino acid composition and other characters. I. Angular distribution.

K Nishikawa, Y Kubota, T Ooi.   

Abstract

Data on amino acid composition were collected in order to classify proteins into groups. The composition of a protein is expressed as a point in an orthogonal coordinate system, taking fractions of amino acids along 18 axes, which represent 18 amino acids (we use Asx and Glx for the sum of Asp and Asn and that of Glu and Gln, respectively). Thus, proteins of known amino acid compositions (356 single polypeptides chains) are distributed as points in this composition space. Since the radial distribution of the points from the origin (the average composition) did not show any distinct separation into groups, we checked the angular distribution of points in the space. Thirteen groups were found by a computer method based on the density. Analysis of the groups in terms of various characters of proteins, such as source (eukaryote or prokaryote), location in an organism, biological function, etc. revealed that the groups have strong correlations to the location (inside or outside the cell) and functional character (enzyme or nonenzyme). Also, the presence of disulfide bond(s) seems to be characteristic of extracellular proteins. Protein source, molecular size and ability to form an oligomer had little correlation to the grouping. Therefore, proteins may be classified into four types as follows: BI, intracellular enzymes; BII, intracellular nonenzymes; BIII, extracellular enzymes; and BIV, extracellular nonenzymes.

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Year:  1983        PMID: 6643432     DOI: 10.1093/oxfordjournals.jbchem.a134442

Source DB:  PubMed          Journal:  J Biochem        ISSN: 0021-924X            Impact factor:   3.387


  8 in total

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5.  Hydrophobicity, expressivity and aromaticity are the major trends of amino-acid usage in 999 Escherichia coli chromosome-encoded genes.

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6.  Studies on the subunits of human myeloperoxidase.

Authors:  R L Olsen; C Little
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7.  SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data.

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8.  Annotating novel genes by integrating synthetic lethals and genomic information.

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  8 in total

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