Deriving a scoring matrix for mapping protein local structure and sequence

• Main Authors: Chia-Chuan Liu, 劉家銓
• Other Authors: Ming-Jing Hwang
• Format: Others
• Language: en_US
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/83868168879023365235

碩士 === 國立陽明大學 === 生物資訊研究所 === 94 === The correlation between protein local structure and sequence was low (r ~ -0.12) when one matches them using existing scoring matrices for amino acid sequence similarity. Here we improve the correlation by a new amino acid substitution scoring matrix. We created fragment pairs chosen randomly from PDBselect 25 (a set of protein structures with sequence identity less than 25%) and used Genetic Algorithm (GA) to optimize the correlation. In our results, the GA-optimized scoring matrix for fragment length of 5, 7, and 9 amino acids achieved a much better correlation (r ~ -0.5). The same approach was then applied for local structure classification using the I-sites library as a test set, which is a set of sequence patterns that strongly correlate with protein structure at the local level. The GA-optimized scoring matrix again achieved better results. Thus, in this work we have developed a GA-based approach that can produce amino acid substitution matrices suitable for mapping protein local structure and sequence.