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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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Hydrophobicity Profiles generated before preprocessing for PAK4 & PAK5. Hydrophobicity profiles of the sequences of kinases PAK4 and PAK5 generated by substituting the amino acid characters with their respective property value (hydrophobicity values given in table 1). The two sequences are known to be closely similar. These profiles would subsequently be divided in equal segments and the neighborhood around the maximum peak in each segment would be converted to an orthogonal plane using Fast Fourier Transformation.
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Figure 1: Hydrophobicity Profiles generated before preprocessing for PAK4 & PAK5. Hydrophobicity profiles of the sequences of kinases PAK4 and PAK5 generated by substituting the amino acid characters with their respective property value (hydrophobicity values given in table 1). The two sequences are known to be closely similar. These profiles would subsequently be divided in equal segments and the neighborhood around the maximum peak in each segment would be converted to an orthogonal plane using Fast Fourier Transformation.

Mentions: The sequences of floating point values thus generated is plotted with the position of amino acid as abscissa and its attribute measure as ordinate for each dimension p. The attribute is analogous to the amplitude of a time-varying non static signal, and the generated graph to the amplitude profile of the signal. Figure 1 describes the hydrophobicity profile of two closely related kinases PAK4 (Swiss-Prot [32] accession no: Q8N4E1) and PAK5 (Swiss-Prot accession no: O95547). Thereafter, the profile is segmented in equal segments of fixed length and the local maximum is found in each segment. The width of the segment would matter in the quality of results.


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

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

Hydrophobicity Profiles generated before preprocessing for PAK4 & PAK5. Hydrophobicity profiles of the sequences of kinases PAK4 and PAK5 generated by substituting the amino acid characters with their respective property value (hydrophobicity values given in table 1). The two sequences are known to be closely similar. These profiles would subsequently be divided in equal segments and the neighborhood around the maximum peak in each segment would be converted to an orthogonal plane using Fast Fourier Transformation.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Hydrophobicity Profiles generated before preprocessing for PAK4 & PAK5. Hydrophobicity profiles of the sequences of kinases PAK4 and PAK5 generated by substituting the amino acid characters with their respective property value (hydrophobicity values given in table 1). The two sequences are known to be closely similar. These profiles would subsequently be divided in equal segments and the neighborhood around the maximum peak in each segment would be converted to an orthogonal plane using Fast Fourier Transformation.
Mentions: The sequences of floating point values thus generated is plotted with the position of amino acid as abscissa and its attribute measure as ordinate for each dimension p. The attribute is analogous to the amplitude of a time-varying non static signal, and the generated graph to the amplitude profile of the signal. Figure 1 describes the hydrophobicity profile of two closely related kinases PAK4 (Swiss-Prot [32] accession no: Q8N4E1) and PAK5 (Swiss-Prot accession no: O95547). Thereafter, the profile is segmented in equal segments of fixed length and the local maximum is found in each segment. The width of the segment would matter in the quality of results.

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