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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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Axis 1 of the Between Group Analysis for the Nucleotidyl cyclases test case. Details as Figure 4
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Figure 6: Axis 1 of the Between Group Analysis for the Nucleotidyl cyclases test case. Details as Figure 4

Mentions: The results for the Nucleotidyl cyclases are shown in Figure 6, using the two different representation schemes. In both plots there is clear separation of guanylate cyclases (GUC) and adenylate cyclases (ADC).). The two positions (158 and 68), Glu-Lys (E->K) and Cys-Asp (C->D) that are sufficient to change the specificity are both identified by the method.


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

Wallace IM, Higgins DG - BMC Bioinformatics (2007)

Axis 1 of the Between Group Analysis for the Nucleotidyl cyclases test case. Details as Figure 4
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Axis 1 of the Between Group Analysis for the Nucleotidyl cyclases test case. Details as Figure 4
Mentions: The results for the Nucleotidyl cyclases are shown in Figure 6, using the two different representation schemes. In both plots there is clear separation of guanylate cyclases (GUC) and adenylate cyclases (ADC).). The two positions (158 and 68), Glu-Lys (E->K) and Cys-Asp (C->D) that are sufficient to change the specificity are both identified by the method.

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