Predicting overlapping protein complexes based on core-attachment and a local modularity structure

Abstract

BackgroundIn recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Therefore, these methods have three major limitations we should concern. Firstly, many methods have ignored the importance of selecting seed, especially without considering the impact of overlapping nodes as seed nodes. Thus, there may be false predictions. Secondly, PCs are generally supposed to be dense subgraphs. However, the subgraphs with high local modularity structure usually correspond to PCs. Thirdly, a number of available methods lack handling noise mechanism, and miss some peripheral proteins. In summary, all these challenging issues are very important for predicting more biological overlapping PCs.ResultsIn this paper, to overcome these weaknesses, we propose a clustering method by core-attachment and local modularity structure, named CALM, to detect overlapping PCs from weighted PPINs with noises. Firstly, we identify overlapping nodes and seed nodes. Secondly, for a node, we calculate the support function between a node and a cluster. In CALM, a cluster which initially consists of only a seed node, is extended by adding its direct neighboring nodes recursively according to the support function, until this cluster forms a locally optimal modularity subgraph. Thirdly, we repeat this process for the remaining seed nodes. Finally, merging and removing procedures are carried out to obtain final predicted clusters. The experimental results show that CALM outperforms other classical methods, and achieves ideal overall performance. Furthermore, CALM can match more complexes with a higher accuracy and provide a better one-to-one mapping with reference complexes in all test datasets. Additionally, CALM is robust against the high rate of noise PPIN.ConclusionsBy considering core-attachment and local modularity structure, CALM could detect PCs much more effectively than some representative methods. In short, CALM could potentially identify previous undiscovered overlapping PCs with various density and high modularity.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2309-9) contains supplementary material, which is available to authorized users.

A seed-extended algorithm for detecting protein complexes based on density and modularity with topological structure and GO annotations

“…We have experiments on six PPINs to compare our SE-DMTG algorithm with the following state-of-the-art protein complex detection algorithms, including MCODE [28 ], MCL [ 34 ], CFinder [ 26 ], DPClus [ 29 ], IPCA [ 30 ], CMC [ 27 ], COACH [ 32 ], HC-PIN [ 41 ], SPICi [ 31 ], ClusterONE [ 35 ], WPNCA [ 33 ], CALM [ 36 ], and ClusterEPs [ 43]. Here all parameters are set as their authors advised in Table 4.…”

“…Recent studies [30 , 35 , 36] have shown that the accuracy of protein complex detection can be significantly improved by taking network weights into account. In the following subsections, we introduce how to calculate the weight of the PPIN.…”

“…ClusterONE introduces a cohesiveness (also called graph modularity) to assess the quality of protein complexes for the first time. On the basis of ClusterONE, we introduce CALM [36], a improved method, to detect protein complexes. Firstly, we identify overlapping nodes and seed nodes by calculating node degree and betweenness, then uses a greedy local research approach based on core-attachment and local modularity structure to produce detected protein complexes.…”

MLQCC: an improved local search algorithm for the set k‐covering problem

“…Experimental results suggest that MLQCC + LI improves the performance of MLQCC on large-scale instances. In future work, we will continue to improve the effectiveness of our algorithms and design various heuristics (Brandão et al, 2017;Interian and Ribeiro, 2017;Wang et al, 2018a) to obtain a better solution quality. Also, we will try to use these proposed strategies in some other problems (Moraes et al, 2016;Wang et al, 2017a;Liu et al, 2018).…”

Integrating data and knowledge to identify functional modules of genes: a multilayer approach

“…Recently, evolutionary algorithm [15, 16] has been adopted to avoid poor local minimum; and node embedding [17] have been used to transform the graph clustering problem into a conventional clustering problem. In addition, algorithms [18, 19] combining two or more approaches described above has emerged. Unfortunately, the computational methods for functional module identification are clearly limited by the poor quality of the underlying PPI data, which is noisy with high rates of false positive and false negative [20, 21].…”