EFFICIENT ALGORITHMS FOR PROTEIN TERTIARY STRUCTURE PREDICTION

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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 UALR, collaborators in ASU, 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 UALR, and collaborators in ASU, we will predict RNA/protein structures which can be used to provide insights information to improve biological studies. Combined with biological experimental analysis, the proposed research has the potential to enable important biological discoveries. The specific aims of the proposed research include the following: Aim 1: We design and implement efficient parameterized algorithms for protein tertiary structure prediction. Implementations will be made publicly available through a web services interface. Using sample techniques, from existing protein structure databases, the algorithms accuracy will be analyzed and compared to other available algorithms. Aim 2: Based on biological data provided by the mentor and other publicly-accessible sources, the parameterized algorithms will be improved to increase their accuracy with the goal of exceeding the current benchmark of an 80% predictive rate. Aim 3: Applying the implemented algorithms to the mentor's data sets, we will predict protein tertiary structures which can be used to improve mutant protein stability in mutagenesis studies. The proposed research could provide useful information to tremendously reduce the time and expenses on doing biological experiments on blind prediction. Combined with physico-chemical analysis of protein structures, 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.