Local helix parameters fitting of proteins based on dual quaternions registration method.
10.7507/1001-5515.201610020
- Author:
Yonghong XU
1
;
Shaowei ZHANG
1
;
Jun JING
2
;
Yong ZHAO
1
;
Feixiang HOU
1
Author Information
1. Department of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China.
2. Department of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China.ydjingjun@ysu.edu.cn.
- Publication Type:Journal Article
- Keywords:
dual quaternions;
helix parameters fitting;
registration;
secondary protein structure;
α helix detection
- From:
Journal of Biomedical Engineering
2018;35(1):131-138
- CountryChina
- Language:Chinese
-
Abstract:
A fitting method of calculating local helix parameters of proteins based on dual quaternions registration fitting (DQRFit) is proposed in this paper. First, the C and N atom coordinates of each residue in the protein structure data are extracted. Then the unregistered data and reference data are constructed using the sliding windows. The square sum of the distance of the data points before and after registration is regarded as an optimization goal. We calculate the optimal rotation matrix and the translation vector using the dual quaternion registration algorithm, and get the helix parameters of the secondary structure which contain the number of residues per turn( ), helix radius( )and helix pitch( ). Furthermore, we can achieve the fitting of three-helix parameters of , , simultaneously with the dual quaternion registration, and can adjust the sliding windows to adapt to different error levels. Compared with the traditional helix fitting method, DQRFit has some advantages such as low computational complexity, strong anti-interference, and high fitting accuracy. It is proven that the precision of proposed DQRFit for α helix detection is comparable to that of the dictionary of secondary structure of proteins (DSSP), and is better than that of other traditional methods. This is of great significance for the protein structure classification and functional prediction, drug design, protein structure visualization and other fields in the future.
