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

Jingsi Ji,Yanrui Ding

doi:10.1109/access.2020.2991307

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-Q w has been proposed to detect communities from dynamic RINN. T-DYNMOGA-Q w sets a threshold during the initialization process and optimizes weighted modularity Q w 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 Q w . 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.

Highlights

Protein function is closely related with its conformation
The values of Q, community number, community size, inter-cluster density, intra-cluster density are shown in table 1: In table 1, the community modularity of the networks of xyna_strli, xyna_theau, wild type of lipase (WTL) and 6B at 300K, 350K, and 400K are all around 0.6, and the communities detected by the T-DYNMOGA-Qw algorithm have higher modularity
In this study, we proposed an evolutionary multiobjective approach T-DYNMOGA-Qw based on DRINN for community discovery in dynamic networks

Summary

Introduction

Protein function is closely related with its conformation. It is a powerful method to study the relationship between protein structure and function using complex network theory. A. EVOLUTIONARY CLUSTERING Evolutionary clustering is a method of processing time series data to generate clustering sequences. EVOLUTIONARY CLUSTERING Evolutionary clustering is a method of processing time series data to generate clustering sequences It needs to consider two conflicting indicators at the same time: snapshot quality (ST) and temporal cost (TC) proposed by Chakrabarti et al [22]. The snapshot quality is used to represent the clustering result of the network Gt (the network at time t) at current time. The optimal clustering result at each time is to maximize snapshot quality and minimize the temporal cost.

Methods

Results

Conclusion