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Mathematical characterization of protein transmembrane regions.

Roy Choudhury A, Zhukov N, Novič M - ScientificWorldJournal (2013)

Bottom Line: Encoding protein sequences into amino acid adjacency matrix is already well established.We have shown its application in classification of transmembrane and nontransmembrane regions of membrane protein sequences.We have introduced the dodecagonal isometries matrix, which is a novel method of encoding protein sequences based on decagonal isometries group.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Chemometrics, National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia.

ABSTRACT
Graphical bioinformatics has paved a unique way of mathematical characterization of proteins and proteomic maps. The graphics representations and the corresponding mathematical descriptors have proved to be useful and have provided unique solutions to problems related to identification, comparisons, and analyses of protein sequences and proteomics maps. Based on sequence information alone, these descriptors are independent from physiochemical properties of amino acids and evolutionary information. In this work, we have presented invariants from amino acid adjacency matrix and decagonal isometries matrix as potential descriptors of protein sequences. Encoding protein sequences into amino acid adjacency matrix is already well established. We have shown its application in classification of transmembrane and nontransmembrane regions of membrane protein sequences. We have introduced the dodecagonal isometries matrix, which is a novel method of encoding protein sequences based on decagonal isometries group.

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Related in: MedlinePlus

Top map of the optimized network. The transmembrane (green) and nontransmembrane (brown) segments form two different clusters. Empty neurons are dark blue.
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fig4: Top map of the optimized network. The transmembrane (green) and nontransmembrane (brown) segments form two different clusters. Empty neurons are dark blue.

Mentions: The following network parameters are found to be optimal: network size—40 × 40, number of epochs—500, and maximum correction factor—0.9. Figure 4 shows the top map of the optimized network with the transmembrane and non-transmembrane segments in two distinct clusters. The network shows only 4.33% error in recall ability; that is, it is able to correctly classify 95.67% of the segments in the training set. For the test set, the error is 8.67%. Table 2 presents the detailed results of CPNN network.


Mathematical characterization of protein transmembrane regions.

Roy Choudhury A, Zhukov N, Novič M - ScientificWorldJournal (2013)

Top map of the optimized network. The transmembrane (green) and nontransmembrane (brown) segments form two different clusters. Empty neurons are dark blue.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3649804&req=5

fig4: Top map of the optimized network. The transmembrane (green) and nontransmembrane (brown) segments form two different clusters. Empty neurons are dark blue.
Mentions: The following network parameters are found to be optimal: network size—40 × 40, number of epochs—500, and maximum correction factor—0.9. Figure 4 shows the top map of the optimized network with the transmembrane and non-transmembrane segments in two distinct clusters. The network shows only 4.33% error in recall ability; that is, it is able to correctly classify 95.67% of the segments in the training set. For the test set, the error is 8.67%. Table 2 presents the detailed results of CPNN network.

Bottom Line: Encoding protein sequences into amino acid adjacency matrix is already well established.We have shown its application in classification of transmembrane and nontransmembrane regions of membrane protein sequences.We have introduced the dodecagonal isometries matrix, which is a novel method of encoding protein sequences based on decagonal isometries group.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Chemometrics, National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia.

ABSTRACT
Graphical bioinformatics has paved a unique way of mathematical characterization of proteins and proteomic maps. The graphics representations and the corresponding mathematical descriptors have proved to be useful and have provided unique solutions to problems related to identification, comparisons, and analyses of protein sequences and proteomics maps. Based on sequence information alone, these descriptors are independent from physiochemical properties of amino acids and evolutionary information. In this work, we have presented invariants from amino acid adjacency matrix and decagonal isometries matrix as potential descriptors of protein sequences. Encoding protein sequences into amino acid adjacency matrix is already well established. We have shown its application in classification of transmembrane and nontransmembrane regions of membrane protein sequences. We have introduced the dodecagonal isometries matrix, which is a novel method of encoding protein sequences based on decagonal isometries group.

Show MeSH
Related in: MedlinePlus