Identifying the Sulfate Ion Binding Residues in Proteins
- Shao-bo LI, Xiu-zhen HU, Li-xia SUN, Xiao-jin ZHANG
- Corresponding Author
- Shao-bo LI
Available Online May 2017.
- https://doi.org/10.2991/bbe-17.2017.34How to use a DOI?
- Sulfate Ion Ligand, Binding Residue, Support Vector Machine.
- Many proteins function execution depends on the process of protein and ligand interact with each other. The identification of ligand binding residues is important for the research of the protein function. The 4442 protein chains with <25% sequence identity and resolution <3.0 were analyzed using Ligand Protein Contact database. Our final dataset contained 8112 sulfate ion binding residues (SIBR). We did a statistical analysis on window size as 7 amino acids. Using the amino acid composition, hydropathy information, correlation information and predicted structure information as the characteristic parameter, a Support Vector Machine algorithm for identifying sulphate ion binding residues was proposed. The overall accuracy and Matthew's correlation coefficient achieved 78.5% and 0.571 using the 5 fold cross validation. The Acc and MCC achieved 72.7% and 0.455 by using independent test. In addition, an online web server was established. http://22.214.171.124:7321/
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shao-bo LI AU - Xiu-zhen HU AU - Li-xia SUN AU - Xiao-jin ZHANG PY - 2017/05 DA - 2017/05 TI - Identifying the Sulfate Ion Binding Residues in Proteins BT - 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017) PB - Atlantis Press SN - 2468-5747 UR - https://doi.org/10.2991/bbe-17.2017.34 DO - https://doi.org/10.2991/bbe-17.2017.34 ID - LI2017/05 ER -