Based On Statistical Modeling Of Protein Structure Prediction

From 1980's, bioinformatics began to appear and developed with very high speed. It mainly deals with biological data by means of storing, searching and performing analysis with the power of modern computers. Bioinformatics is the cutting edge of life and natural sciences nowadays and will be one of the most important research areas in the 21^st century. The advance of bioinformatics will be an evolutionary power to current life sciences: not only basic research fields, but also agriculture, medicine and public health, food industry, and so on, will benefit from its merits. One urgent work for current bioinformatics researchers is to investigate efficient methodologies based on statistical modeling, and to predict or analyses the mountainous data deposited in current public databases. Comparing to traditional bench-experiments, advantages of these approaches from statistical modeling (Hidden Markov Model, Support Vector Machine, k-Nearest Neighbor, etc) are apparent: fast, automatic and efficient in time and labor resources, especially in high throughput large-scale sequence analysis. In this paper we mainly carry through deeply research on these prediction methods based on statistic modeling on the background of the prediction of proteins' structure and function, and aims to improve their sensitivity and efficiency in application.In this dissertation, some original research works by the author can be formulated as follow:1. Transmemebrane proteins are very important and contribute a lot to the living cells and signal transduction. Lots of proteins encoded by humane genome have α-helices transmembrane segments in their structures. Prediction of transmembrane helices in proteins by statistical modeling is one of the most urgent research works in bioinformatics. A novel segment-training algorithm for Hidden Markov Modeling based on the biological characters of transmembrane proteins has been introduced, for training and predicting the topological characters of transmembrane helices, such as location, orientation, and so on. Results and...