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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

Principal component analysis. The transmembrane (black) and nontransmembrane (blue) segments form two different clusters.
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Related In: Results  -  Collection


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fig3: Principal component analysis. The transmembrane (black) and nontransmembrane (blue) segments form two different clusters.

Mentions: Figure 3 shows the results from PCA analysis, where the transmembrane and non-transmembrane data are projected on 2D space defined by their first two principal components. PC1 contains 56.05% of the total variance, whereas PC2 contains 5.52% of the remaining variance. In total, the first two principal components contain 61.57% of the total variance present in the data. As we can see, the transmembrane and non-transmembrane segments, represented by the black and blue circles, respectively, are well separated over the first and second principal components. The region of overlap between the two clusters is very small with an overall distinction between the two groups. The PCA analysis is performed as a preliminary test. We have validated that the mathematical descriptors chosen are important to bring out the characteristic features of the protein segments. The descriptors are able to represent and distinguish the sequence characteristics of the two types of protein segments and group them successfully.


Mathematical characterization of protein transmembrane regions.

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

Principal component analysis. The transmembrane (black) and nontransmembrane (blue) segments form two different clusters.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Principal component analysis. The transmembrane (black) and nontransmembrane (blue) segments form two different clusters.
Mentions: Figure 3 shows the results from PCA analysis, where the transmembrane and non-transmembrane data are projected on 2D space defined by their first two principal components. PC1 contains 56.05% of the total variance, whereas PC2 contains 5.52% of the remaining variance. In total, the first two principal components contain 61.57% of the total variance present in the data. As we can see, the transmembrane and non-transmembrane segments, represented by the black and blue circles, respectively, are well separated over the first and second principal components. The region of overlap between the two clusters is very small with an overall distinction between the two groups. The PCA analysis is performed as a preliminary test. We have validated that the mathematical descriptors chosen are important to bring out the characteristic features of the protein segments. The descriptors are able to represent and distinguish the sequence characteristics of the two types of protein segments and group them successfully.

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