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Remote homology detection using a kernel method that combines sequence and secondary-structure similarity scores

Record type: Article

Distant evolutionary relationships between proteins with low sequence similarity are difficult to recognise by computational methods. Consequently, many sequences obtained from large-scale sequencing projects cannot be assigned to any known proteins or families despite being evolutionarily related. To boost sensitivity, various sequence-based methods have been modified to make use of the better conserved secondary structure. Most of these methods are instance-based or generative. Here, we introduce a kernel-based remote homology detection method that allows for a combination of sequence and secondary-structure similarity scores in a discriminative approach. We studied the ability of the method to predict superfamily membership as defined by the SCOP database. We show that a kernel method that combined sequence similarity scores with predicted secondary-structure similarity scores performed similar to a classifier that used scores calculated from sequences and true secondary structures, but performed better than a sequence-only based classifier and achieved a better mean than recently published results on the same data-set. It can be concluded that SVM classifiers trained to predict homology between distantly related proteins, become more accurate, if a joint sequence/secondary-structure similarity score approach is used.

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Citation

Wieser, Daniela and Niranjan, Mahesan (2009) Remote homology detection using a kernel method that combines sequence and secondary-structure similarity scores In Silico Biology, 9, p. 9.

More information

Published date: 2009
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 268189
URI: http://eprints.soton.ac.uk/id/eprint/268189
PURE UUID: 4ea32249-2842-47d7-835c-9a4c80cac48d

Catalogue record

Date deposited: 11 Nov 2009 14:32
Last modified: 18 Jul 2017 06:56

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Contributors

Author: Daniela Wieser

University divisions

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