| Autor: |
Maleki, Mina, Hall, Michael, Rueda, Luis |
| Zdroj: |
2012 IEEE Symposium on Computational Intelligence in Bioinformatics & Computational Biology (CIBCB); 1/ 1/2012, p9-15, 7p |
| Abstrakt: |
The identification and prediction of particular types of protein-protein interactions (PPIs) based on knowledge of their interacting domains is a problem that has drawn the attention of researchers in the past few years. We focus on the prediction and analysis of obligate and non-obligate complexes by using structural domains from the CATH database. Our proposed prediction model uses desolvation energies of domain-domain interactions (DDIs) present in the interfaces of such complexes. The prediction is performed via linear dimensionality reduction (LDR) and support vector machines (SVMs). Our results on two well-known datasets show that DDI features of the first three levels of CATH, especially level 2, are more powerful and discriminative than features of other levels in predicting these types of complexes. Furthermore, a detailed analysis shows that different DDIs are present in obligate and non-obligate complexes, and that homo-DDIs are more likely to be present in obligate interactions. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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