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| Titel: |
Remote Homology Detection Using a Kernel Method that Combines Sequence and Secondary-Structure Similarity Scores |
| Auteur: |
Wieser, Daniela Niranjan, Mahesan |
| Verschenen in: |
In silico biology |
| Paginering: | Jaargang 9 (2009) nr. 3 pagina's 89-103 |
| Jaar: |
2009-06-24 |
| Inhoud: |
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. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 67 van 88 gevonden artikelen
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