Title:Identifying Native and Non-native Membrane Protein Loops by Using Stabilizing Energetic Terms of Three Popular Force Fields
Volume: 1
Issue: 1
Author(s): Konda Mani Saravanan, Haiping Zhang and Yanjie Wei*
Affiliation:
- Center for High Performance Computing, Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055,China
Keywords:
Loop conformation, membrane proteins, molecular force fields, structure prediction, molecular modeling, template modeling.
Abstract:
Background: Predicting the three-dimensional structure of globular proteins from their
amino acid sequence has reached a fair accuracy, but predicting the structure of membrane proteins,
especially loop regions, is still a difficult task in structural bioinformatics. The difficulty in predicting
membrane loops is due to various factors like length variation, position, flexibility, and they are
easily prone to mutation.
Objective: In the present work, we address the problem of identifying and ranking near-native loops
from a set of decoys generated by Monte-Carlo simulations.
Methods: We systematically analyzed native and generated non-native decoys to develop a scoring
function. The scoring function uses four important stabilizing energy terms from three popular force
fields, such as FOLDX, OPLS, and AMBER, to identify and rank near-native membrane loops.
Results: The results reveal better discrimination of native and non-natives and perform poor prediction
in binary classifying native and near-native defined based on Root Mean Square Deviation
(RMSD), Global Distance Test (GDT), and Template Modeling (TM) score, respectively.
Conclusion: From our observations, we conclude that the important energy features described here
may help to improve the loop prediction when the membrane protein database size increases.
