Detecting protein complexes and functional modules from protein interaction networks: A graph entropy approach

Abstract

Recent high‐throughput experiments have generated protein–protein interaction data on a genomic scale, yielding the complete interactome for several organisms. Various graph clustering algorithms have been applied to protein interaction networks for identifying protein complexes and functional modules. Although the previous algorithms are scalable and robust, their accuracy is still limited because of the complex connectivity found in protein interaction networks. In this study, we propose a novel information‐theoretic definition, graph entropy, as a measure of the structural complexity of a graph. Loss of graph entropy represents an increase in modularity of the graph. Based on this concept, we present a graph clustering algorithm which searches for the local optimum in modularity. The algorithm detects each optimal cluster by growing a seed in a manner that minimizes graph entropy. In the experiments with the yeast interactome, the results show that the graph entropy approach has higher accuracy in predicting protein complexes and functional modules than the best competing method. We statistically compared output clusters to both known protein complexes and Gene Ontology annotations in the biological process and molecular function categories in order to measure f‐scores and p‐scores as clustering accuracy. Because this algorithm is also scalable, it can be applied to the larger scale human protein interaction network.

Network‐Wide Screen Identifies Variation of Novel Precise On‐Module Targets Using Conformational Modudaoism

“…With the integration of network entropy index, the MDv value used for on‐module identification was considered a more stringent criterion, so fewer on‐modules were identified by MDv, but the reduced entropy represented better modular structure . According to the MDv value, the on‐modules and characteristic UAMs of different drugs may help to elucidate their specific or unique actions.…”

Alignment of PPI Networks Using Semantic Similarity for Conserved Protein Complex Prediction

“…As expected, most of the prominent graph clustering algorithms, such as MCODE [44] and CFinder [20], failed in running with the genome-wide human PPI network that we use. We were thus able to receive the clustering results from two algorithms, Markov Clustering (MCL) [45] and Graph Entropy algorithm [46]. When the clustering results from MCL and Graph Entropy were compared to the integrated reference data set of human protein complexes, we had recall in the range between 0.4 and 0.45 and precision in the range between 0.4 and 0.52 as shown in Table III, which are lower than our network alignment results in Table II.…”

Assessment of Cluster Overlaps to Improve Accuracy of Module Detection from PPI Networks

“…It has been observed that PPI networks are typically modular [3]. Consequently, various graph clustering algorithms [4], [5] have been applied to the networks for the purpose of identifying protein complexes and functional modules. A protein complex consists of proteins that interact at the same time and same place, building a larger molecular machine.…”