T-DYNMOGA-Q_w: Detecting Community From Dynamic Residue Interaction Energy Network and Its Application in Analyzing Lipase Thermostability

Abstract

A new type of residue interaction network named residue interaction energy network (RINN) is built. Then, a multi-objective optimization dynamic network community discovery algorithm T-DYNMOGA-Qw has been proposed to detect communities from dynamic RINN. T-DYNMOGA-Qw sets a threshold during the initialization process and optimizes weighted modularity Qw as the objective function. Setting the threshold can better find the stable structure in the dynamic RINN. The resolution limit of modularization has been broken by using objective function Qw. After Community detection from dynamic RINN of wild type of lipase (WTL) and its mutant 6B from 300K to 400K, it is found that the communities in 6B network can still maintain a tight structure even at higher temperature. Stable community is benefit to the heat resistance of lipase 6B. The hydrogen bonds between mutated Ser15 and Ser17, and the Glu20 with other residues improved the structure stability. The mutated L114P, M134E, M137P, and S163P enhance the rigidity of the flexible region and tighten the secondary structure, which stabilize the protein structure.

Keywords

• Residue interaction energy network
• community detection
• lipase thermostability