Abstract Dr. Huang

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Xiuzhen Huang, PhD

Computational alignment of a biopolymer sequence (e.g., an RNA or a protein) to a structure is an effective approach to predict and search for the structure of new sequences. To identify the structure of remote homologs, the structure-sequence alignment has to consider not only sequence similarity but also spatially conserved conformations caused by residue interactions, and consequently is computationally intractable. It is difficult to cope with the inefficiency without compromising alignment accuracy, especially for structure search in genomes or large databases.

 The goal of this proposed research in bioinformatics is to introduce novel methods and develop efficient parameterized algorithms for RNA/protein structure prediction. By identifying small parameters from the analysis of RNA/protein sequence and structure properties, parameterized approaches have the advantage of being very efficient, i.e., having low computational cost compared to the other traditional approaches such as approximation algorithms and statistical approaches. 

 The specific aims of the proposed research include the following: Aim 1: We introduce novel approaches and design efficient parameterized algorithms for RNA/protein structure prediction. The efficiency and accuracy of our algorithm will be analyzed and compared to other available approaches. Our preliminary experimental results demonstrate our algorithm is very efficient for RNA structural search. Aim 2: Based on biological data provided by the mentor in University of Arkansas at Little Rock, collaborators in Arkansas State University, and other publicly-accessible sources, the parameterized algorithms will be improved to increase their accuracy. Aim 3: Applying the implemented algorithms to the biological data sets provided by the mentor in University of Arkansas at Little Rock, and collaborators in Arkansas State University, we will predict RNA/protein structures which can be used to provide insights information to improve biological studies. The proposed research could provide useful information to tremendously reduce the time and expenses on doing biological experiments on blind prediction using X-ray crystallography and NMR spectroscopy. Combined with biological experimental analysis, the proposed research has the potential to enable important biological discoveries, which could positively impact scientific discovery in the areas of biological science such as agricultural plant genetics, new pharmaceuticals design, and new protein production related to human health and disease.

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Updated 05/29/2007

The Arkansas INBRE is Supported by a grant  from the National Institutes of Health
and the National Center for Research Resources (P20 RR-16460).