Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps

Background: Protein sequence alignment is one of the basic tools in bioinformatics. Correct alignments are required for a range of tasks including the derivation of phylogenetic trees and protein structure prediction. Numerous studies have shown that the incorporation of predicted secondary structure information into alignment algorithms improves their performance. Secondary structure predictors have to be trained on a set of somewhat arbitrarily defined states (e.g. helix, strand, coil), and it has been shown that the choice of these states has some effect on alignment quality. However, it is not unlikely that prediction of other structural features also could provide an improvement. In this study we use an unsupervised clustering method, the self-organizing map, to assign sequence profile windows to "structural states" and assess their use in sequence alignment. Results: The addition of self-organizing map locations as inputs to a profile-profile scoring function improves ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Tomas Ohlson, Varun Aggarwal, Arne Elofsson, Robert M. MacCallum
• Keywords: BMCBI 2006, Protein Structure Prediction, Secondary Structure, Self-Organizing Map