A genetic algorithm for simplifying the amino acid alphabet and predicting protein interactions.
Date of Award
Master of Science (MS)
Dr. Hesham H. Ali
A central problem in creating simplified amino acid alphabets is narrowing down the massive number of possible simplifications. Since considering all possible simplifications is intractable, effectively creating simplified alphabets is essential. Genetic algorithms have been effective in providing near-optimal solutions for similar combinatorial problems with large solution spaces. This makes them a good candidate for creating simplified alphabets. Simplified amino acid alphabets could uncover hidden relationships in protein sequences, and in turn provide a valuable first step in solving protein-related microbiological problems. The project demonstrates the impact of reducing the alphabet in addressing an important open problem in microbiology, which is predicting protein-protein interactions. Various techniques for predicting protein-protein interactions exist, but are incomplete. No single method can effectively predict more than a small subset of interactions. Hence, a comprehensive listing all of a cells protein-protein interactions may require many complimentary approaches. The projects results indicate that genetic algorithms effectively. create simplified amino acid alphabets, and these alphabets are a useful tool in predicting protein interactions.
Palensky, Matthew, "A genetic algorithm for simplifying the amino acid alphabet and predicting protein interactions." (2005). Student Work. 3561.
A Thesis Presented to the Department of Computer Science and the Faculty of the Graduate College University of Nebraska In Partial Fulfillment of the Requirements for the Degree Master of Science University of Nebraska at Omaha Copyright 2005 Matthew Palensky