The Prediction of the Backbone Conformation from Protein Sequences

• Main Authors: Chien-Hua Shih, 施建華
• Other Authors: Jenn-Kang Hwang
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/53498359389855414996

碩士 === 國立交通大學 === 生物資訊研究所 === 93 === The knowledge of protein structure conformation is useful in understanding the functions and mechanism of proteins. Nowadays, we can use the X-ray or NMR technique to construct the 3-dimensional structure of proteins. But these methods cost lots of time and money. Fortunately, we now have an easier alternative to the knowledge of protein structures. That is, from the increasing amount of data in the protein structure database, such as PDB database. Up to the present, there are 30 thousand records stored in the PDB database. If we use the PDB database as our knowledge and use the machine learning technique to predict the 3-dimensional structure from the sequence of an unknown protein structure. That can let us easier understand the protein’s function and mechanism. This research is to find a suitable definition to describe protein backbone conformation, and to predict backbone conformation from sequence information. We adapt two different definitions of Ramachandran Plot and Protein Blocks. Our results by Support Vector Machines (SVM) get a relatively good performance. Finally, we will study on the relationship between the conservation of protein backbone conformation and structural entropy. We've discovered that there is a consistency in structural entropy by different definitions of protein backbone conformation.