Protein Complex Detection in PPI Network by Identifying Mutually Exclusive Protein-protein Interactions

Hossain, Sk Md Mosaddek; Mahboob, Zeba; Chowdhury, Rejaul; Sohel, Arif; Ray, Sumanta

doi:10.1016/j.procs.2016.07.309

Cited by 7 publications

(6 citation statements)

References 11 publications

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“…It has been observed that proteins within a complex are functionally similar 14 , 32 , 33 . For understanding the biological roles and functions of proteins, functional similarity is a more informative measure compared to the structural and sequence similarity.…”

Section: Resultsmentioning

confidence: 99%

Identifying protein complexes in PPI network using non-cooperative sequential game

Maulik

Basu

Ray

2017

Sci Rep

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Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.

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Section: Resultsmentioning

confidence: 99%

Identifying protein complexes in PPI network using non-cooperative sequential game

Maulik

Basu

Ray

2017

Sci Rep

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“…The densely connected regions in the core network were predicted using module analysis using Molecular Cluster Detection (MCODE) 4.1, a Cytoscape plug-in. The local neighbourhood density of weighted nodes was used to identify the network's heavily linked areas [22]. All of the parameters were kept constant, including the degree threshold (2), node score threshold (0.2), k-core threshold (2) and network max depth (100).…”

Section: Network Cluster Analysismentioning

confidence: 99%

Exploring key molecular signatures of immune responses and pathways associated with tuberculosis in comorbid diabetes mellitus: a systems biology approach

Selvan

Gollapalli

Shetty

et al. 2022

Beni-Suef Univ J Basic Appl Sci

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Background Comorbid type 2 diabetes mellitus (T2DM) increases the risk for tuberculosis (TB) and its associated complications, although the pathological connections between T2DM and TB are unknown. The current research aims to identify shared molecular gene signatures and pathways that affirm the epidemiological association of T2DM and TB and afford clues on mechanistic basis of their association through integrative systems biology and bioinformatics approaches. Earlier research has found specific molecular markers linked to T2DM and TB, but, despite their importance, only offered a limited understanding of the genesis of this comorbidity. Our investigation used a network medicine method to find possible T2DM-TB molecular mediators. Results Functional annotation clustering, interaction networks, network cluster analysis, and network topology were part of our systematic investigation of T2DM-TB linked with 1603 differentially expressed genes (DEGs). The functional enrichment and gene interaction network analysis emphasized the importance of cytokine/chemokine signalling, T cell receptor signalling route, NF-kappa B signalling pathway and Jak-STAT signalling system. Furthermore, network analysis revealed significant DEGs such as ITGAM and STAT1, which may be necessary for T2DM-TB immune responses. Furthermore, these two genes are modulators in clusters C4 and C5, abundant in cytokine/chemokine signalling and Jak-STAT signalling pathways. Conclusions Our analyses highlight the role of ITGAM and STAT1 in T2DM-TB-associated pathways and advances our knowledge of the genetic processes driving this comorbidity.

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“…1) A local neighborhood density search approach, focusing on the discovery of dense subgraphs inside the input network, including MCODE [7], DPClus [6], ProRank [8], [9], ProRank+ [10], CMC [11], PROCOMOSS [12] and PEWCC [13], NCMine [14], Core&Peel [15], SPICi [16] and non-cooperative sequential game [17]. 2) Local search approaches based on cost, focusing on the extraction of modules from interaction graphs through the partition of the graphs into linked subgraphs employing cost functions for the guidance of searches towards the optimal partition, including RNSC [18], ModuLand [19], and STM [20].…”

Section: Introductionmentioning

confidence: 99%

Identifying Protein Complexes in Protein-Protein Interaction Data Using Graph Convolutional Network

Zaki¹,

Singh

Mohamed

2021

IEEE Access

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Protein complexes are groups of two or more polypeptide chains that bind to form noncovalent networks of protein interactions. Over the past decade, researchers have created a number of means of computing the ways in which protein complexes and their members can be identified through these interaction networks. Although most of the existing methods identify protein functional complexes from the protein-protein interaction networks (PPIs) at a fairly decent level, the applicability of advanced graph network methods has not yet been adequately investigated. This paper proposes various graph convolutional network (GCN) methods to improve the detection of protein complexes. We first formulate the protein complex detection problem as a node classification problem. Then, we developed a Neural Overlapping Community Detection (NOCD) model to cluster the nodes (proteins) using a complex affiliation matrix. A representation learning approach, that combines a multi-class GCN feature extractor (to obtain the nodes' features) and a mean shift clustering algorithm (to perform the clustering), is also utilized. We convert the dense-dense matrix operations into dense-sparse or sparse-sparse matrix operations to improve the efficiency of the multi-class GCN network by reducing space and time complexities. The proposed solution significantly improves the scalability of the existing GCN. Finally, we apply clustering aggregation to find the best protein complexes. A grid search is then performed on various detected complexes obtained via three well-known protein detection methods, namely ClusterONE, CMC, and PEWCC, with the help of the Meta-Clustering Algorithm (MCLA) and the Hybrid Bipartite Graph Formulation (HBGF). We test the proposed GCN-based methods on various publicly available datasets and find that they perform significantly better than previous state-of-the-art methods. The code/data are available for free download from https://github.com/Analystharsh/GCN_complex_detection.

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Protein Complex Detection in PPI Network by Identifying Mutually Exclusive Protein-protein Interactions

Cited by 7 publications

References 11 publications

Identifying protein complexes in PPI network using non-cooperative sequential game

Identifying protein complexes in PPI network using non-cooperative sequential game

Exploring key molecular signatures of immune responses and pathways associated with tuberculosis in comorbid diabetes mellitus: a systems biology approach

Identifying Protein Complexes in Protein-Protein Interaction Data Using Graph Convolutional Network

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