Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our ex...
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2010
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| Online Access: | https://ink.library.smu.edu.sg/sis_research/1527 https://ink.library.smu.edu.sg/context/sis_research/article/2526/viewcontent/10_015.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Optimally combining available information is one of the key challenges in knowledge-driven prediction techniques. In this study, we evaluate six Phi and Psi-based backbone alphabets. We show that the addition of predicted backbone conformations to SVM classifiers can improve fold recognition. Our experimental results show that the inclusion of predicted backbone conformations in our feature representation leads to higher overall accuracy compared to when using amino acid residues alone. |
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