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dc.contributor.authorNichols, Zacharyeng
dc.contributor.authorBondugula, Rajkumar, 1980-eng
dc.contributor.authorXu, Dong, 1965-eng
dc.contributor.corporatenameUniversity of Missouri--Columbia. Office of Undergraduate Researcheng
dc.contributor.meetingnameSummer Undergraduate Research and Creative Achievements Forum (2005 : University of Missouri--Columbia)eng
dc.date.issued2005eng
dc.descriptionAbstract only availableeng
dc.description.abstractProtein structure prediction is a growing field of interest for a many varied reasons, owing not only to its obvious utility, but also the success that applying newer mathematical tools has garnered in recent years. Despite the intractability of determining optimal protein structure directly by finding a lowest-energy conformation among a huge amount of candidates, many heuristic methods have emerged that sacrifice some degree of accuracy for reasonable speed of execution. Through the use of numerical techniques such as neural networks(1), neural networks bolstered by position-specific scoring matrices generated by psi-blast(2), and k-nearest neighbor algorithms(3), the success rate of protein structure prediction has been increasing over the past decade and a half. Each of these tools has particular strengths and weaknesses. To address this and to improve prediction accuracy, we are constructing a three-part meta-tool that combines k-nearest neighbor methods, neural network methods, and hidden markov models to predict the secondary structure of proteins based on their position-specific scoring matrices. The results from each of the individual tools will be integrated and filtered to form a final prediction. This tool will be available on the web through a simple interface for those wishing to evaluate or utilize it. References: 1: Rost and Sander. Predictions of protein secondary structure at better than 70% Accuracy; J. Mol. Biol. (1993) 232, 584-599 2: Jones. Protein secondary structure prediction based on position-specific scoring matrices; J. Mol. Boil. (1999) 292, 195-202 3: Bondugula, Duzlevski, Xu. Profiles and fuzzy k-nearest neighbor algorithm for protein secondary structure prediction; (unpublished).eng
dc.description.sponsorshipNSF-REU Program in Biosystems Modeling and Analysiseng
dc.identifier.urihttp://hdl.handle.net/10355/2172eng
dc.languageen_USeng
dc.publisherUniversity of Missouri--Columbia. Office of Undergraduate Researcheng
dc.relation.ispartof2005 Summer Undergraduate Research and Creative Achievements Forum (MU)eng
dc.relation.ispartofcommunityUniversity of Missouri-Columbia. Office of Undergraduate Research. Undergraduate Research and Creative Achievements Forumeng
dc.source.urihttp://undergradresearch.missouri.edu/forums-conferences/abstracts/abstract-detail.php?abstractid=eng
dc.subjectprotein structure predictioneng
dc.subjectoptimal protein structureeng
dc.subjectk-nearest neighbor methodseng
dc.subjectneural network methodseng
dc.subjecthidden markov modelseng
dc.subjectsecondary structure of proteinseng
dc.titleProtein secondary structure prediction: Creating a meta-tooleng
dc.typePresentationeng


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