A novel framework for protein structure prediction

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file (viewed on March 23, 2009)Vita.Thesis (Ph.D.) University of Missouri-Columbia 2007.Proteins are one of the most important molecules in the life processes. The structure of a protein is essential in understanding the function of a protein at the molecular level. Due to rapid progress in sequencing technologies, the gap between the proteins whose structure is known and the proteins whose structure needs to be characterized is rapidly increasing. To address this problem, we are developing a novel framework to computationally predict many aspects of proteins like secondary structure, solvent accessibility, contact map and finally, the tertiary structure itself. We have applied various computational techniques including the fuzzy k-nearest neighbor algorithm, the multi-dimensional scaling method, and the least-squares minimization, in the structure predictions. Our framework uses the evolutionary information more effectively than traditional template based methods, while it has a better potential to utilize the information in PDB than the other evolutionary information based methods. Our methods show better performance in prediction accuracy and computational time than many other tools.Includes bibliographical reference

Protein secondary structure prediction: Creating a meta-tool

Abstract only availableProtein 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).NSF-REU Program in Biosystems Modeling and Analysi