The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

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

Remote homology detection using a kernel method that combines sequence and secondary-structure similarity scores
Remote homology detection using a kernel method that combines sequence and secondary-structure similarity scores
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.
9
Wieser, Daniela
613cebb9-0a7c-427b-a695-cb7fa613b3a5
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Wieser, Daniela
613cebb9-0a7c-427b-a695-cb7fa613b3a5
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

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, 9.

Record type: Article

Abstract

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.

This record has no associated files available for download.

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
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

Catalogue record

Date deposited: 11 Nov 2009 14:32
Last modified: 08 Jan 2022 03:06

Export record

Contributors

Author: Daniela Wieser
Author: Mahesan Niranjan ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×