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Supervised multivariate analysis of sequence groups to identify specificity determining residues.

Wallace IM, Higgins DG - BMC Bioinformatics (2007)

Bottom Line: BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.BGA is especially useful because it can be used to analyse any number of functional classes.In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland. iain.wallace@ucd.ie

ABSTRACT

Background: Proteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA), can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments.

Results: We demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.

Conclusion: This overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.

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Phylogenetic tree of the nucleotidyl cyclases sequences. The guanylate sequences are coloured in blue.
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Figure 2: Phylogenetic tree of the nucleotidyl cyclases sequences. The guanylate sequences are coloured in blue.

Mentions: Nucleotidyl cyclases are a family of cytosolic proteins that catalyse the reaction that transforms a nucleotide triphosphate into a cyclic nucleotide monophosphate. The cyclases act on either guanylate (GUC) or adenylate (ADC). The alignment used in this example is the same one used by Hannenhalli and Russell [8] and contains 41 adenylate and 29 guanylate sequences. The phylogenetic tree in Figure 2 was calculated from the alignment using the Neighbor-Joining method [36] implemented in ClustalW [37]. The tree was rooted using the add_root programme supplied by Manolo Gouy. Two point mutations, Glu-Lys and Cys-Asp, are sufficient to change the specificity of the enzyme from GUC to ADC [38]. These are positions 938 and 1018 of the protein sequence corresponding to the 3D structure of adenylate cyclase, 1ab8 [39].


Supervised multivariate analysis of sequence groups to identify specificity determining residues.

Wallace IM, Higgins DG - BMC Bioinformatics (2007)

Phylogenetic tree of the nucleotidyl cyclases sequences. The guanylate sequences are coloured in blue.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1878507&req=5

Figure 2: Phylogenetic tree of the nucleotidyl cyclases sequences. The guanylate sequences are coloured in blue.
Mentions: Nucleotidyl cyclases are a family of cytosolic proteins that catalyse the reaction that transforms a nucleotide triphosphate into a cyclic nucleotide monophosphate. The cyclases act on either guanylate (GUC) or adenylate (ADC). The alignment used in this example is the same one used by Hannenhalli and Russell [8] and contains 41 adenylate and 29 guanylate sequences. The phylogenetic tree in Figure 2 was calculated from the alignment using the Neighbor-Joining method [36] implemented in ClustalW [37]. The tree was rooted using the add_root programme supplied by Manolo Gouy. Two point mutations, Glu-Lys and Cys-Asp, are sufficient to change the specificity of the enzyme from GUC to ADC [38]. These are positions 938 and 1018 of the protein sequence corresponding to the 3D structure of adenylate cyclase, 1ab8 [39].

Bottom Line: BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.BGA is especially useful because it can be used to analyse any number of functional classes.In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.

View Article: PubMed Central - HTML - PubMed

Affiliation: The Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland. iain.wallace@ucd.ie

ABSTRACT

Background: Proteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA), can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments.

Results: We demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.

Conclusion: This overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.

Show MeSH