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Detailed protein sequence alignment based on Spectral Similarity Score (SSS).

Gupta K, Thomas D, Vidya SV, Venkatesh KV, Ramakumar S - BMC Bioinformatics (2005)

Bottom Line: Detailed comparison established close similarities between subsequences that do not have any significant character identity.The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures.The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in

ABSTRACT

Background: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.

Results: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure.

Conclusion: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

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Related in: MedlinePlus

Preprocessing of Inputs in a single property plane. The property profile of one of the input sequences in a plane is subjected to segmentation of equal sizes. Maximum peak in each segmented is identified by simple comparison of the heights of the peaks and the a neighborhood of size F around the position containing the peak is taken. Each neighborhood is then collectively subjected to fourier transformation. This preprocessing is implemented in each plane of the property profile.
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Figure 2: Preprocessing of Inputs in a single property plane. The property profile of one of the input sequences in a plane is subjected to segmentation of equal sizes. Maximum peak in each segmented is identified by simple comparison of the heights of the peaks and the a neighborhood of size F around the position containing the peak is taken. Each neighborhood is then collectively subjected to fourier transformation. This preprocessing is implemented in each plane of the property profile.

Mentions: For each dimension p pertaining to property Pp, let the sequence be divided in N equal segments denoted by sp,i where i ∈ {1, 2 ..., N} and size of each segment be Sz. Also let the positions in each segment where local maximum was found be mp,i where i ∈ {1, 2, ..., N}. The maxima is found within the segment in the abcissa by simply comparing the peaks of the property values, as represented in figure 2.


Detailed protein sequence alignment based on Spectral Similarity Score (SSS).

Gupta K, Thomas D, Vidya SV, Venkatesh KV, Ramakumar S - BMC Bioinformatics (2005)

Preprocessing of Inputs in a single property plane. The property profile of one of the input sequences in a plane is subjected to segmentation of equal sizes. Maximum peak in each segmented is identified by simple comparison of the heights of the peaks and the a neighborhood of size F around the position containing the peak is taken. Each neighborhood is then collectively subjected to fourier transformation. This preprocessing is implemented in each plane of the property profile.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC1131888&req=5

Figure 2: Preprocessing of Inputs in a single property plane. The property profile of one of the input sequences in a plane is subjected to segmentation of equal sizes. Maximum peak in each segmented is identified by simple comparison of the heights of the peaks and the a neighborhood of size F around the position containing the peak is taken. Each neighborhood is then collectively subjected to fourier transformation. This preprocessing is implemented in each plane of the property profile.
Mentions: For each dimension p pertaining to property Pp, let the sequence be divided in N equal segments denoted by sp,i where i ∈ {1, 2 ..., N} and size of each segment be Sz. Also let the positions in each segment where local maximum was found be mp,i where i ∈ {1, 2, ..., N}. The maxima is found within the segment in the abcissa by simply comparing the peaks of the property values, as represented in figure 2.

Bottom Line: Detailed comparison established close similarities between subsequences that do not have any significant character identity.The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures.The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science & Engineering, Indian Institute of Technology, Bombay, Mumbai, India. kshitiz@cse.iitb.ac.in

ABSTRACT

Background: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.

Results: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure.

Conclusion: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.

Show MeSH
Related in: MedlinePlus