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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Main Author:	SHIM, Kyong Jin
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2010
Subjects:	Databases and Information Systems
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

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spelling	sg-smu-ink.sis_research-25262018-08-16T06:49:23Z Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition SHIM, Kyong Jin 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. 2010-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1527 https://ink.library.smu.edu.sg/context/sis_research/article/2526/viewcontent/10_015.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Databases and Information Systems
spellingShingle	Databases and Information Systems SHIM, Kyong Jin Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
description	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.
format	text
author	SHIM, Kyong Jin
author_facet	SHIM, Kyong Jin
author_sort	SHIM, Kyong Jin
title	Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
title_short	Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
title_full	Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
title_fullStr	Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
title_full_unstemmed	Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition
title_sort	evaluation of protein backbone alphabets: using predicted local structure for fold recognition
publisher	Institutional Knowledge at Singapore Management University
publishDate	2010
url	https://ink.library.smu.edu.sg/sis_research/1527 https://ink.library.smu.edu.sg/context/sis_research/article/2526/viewcontent/10_015.pdf
_version_	1770571229420322816

Evaluation of Protein Backbone Alphabets: Using Predicted Local Structure for Fold Recognition

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