A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks

Numerous gene sets have been used as molecular signatures for exploring the genetic basis of complex disorders. These gene sets are distinct but related to each other in many cases; therefore, efforts have been made to compare gene sets for studies such as those evaluating the reproducibility of different experiments. Comparison in terms of biological function has been demonstrated to be helpful to biologists. We improved the measurement of semantic similarity to quantify the functional association between gene sets in the context of gene ontology and developed a web toolkit named Gene Set Functional Similarity (GSFS; http://bioinfo.hrbmu.edu.cn/GSFS ). Validation based on protein complexes for which the functional associations are known demonstrated that the GSFS scores tend to be correlated with sequence similarity scores and that complexes with high GSFS scores tend to be involved in the same functional catalogue. Compared with the pairwise method and the annotation method, the GSFS shows better discrimination and more accurately reflects the known functional catalogues shared between complexes. Case studies comparing differentially expressed genes of prostate tumour samples from different microarray platforms and identifying coronary heart disease susceptibility pathways revealed that the method could contribute to future studies exploring the molecular basis of complex disorders.

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Protein Complexes Detection Based on Node Local Properties and Gene Expression On PPI Weighted Networks

10.21203/rs.3.rs-287016/v1 ◽

2021 ◽

Author(s):

Yang Yu ◽

Dezhou Kong

Keyword(s):

Gene Expression ◽

Resource Allocation ◽

Protein Complex ◽

Protein Complexes ◽

Second Order ◽

Topological Properties ◽

Ppi Network ◽

Weighted Networks ◽

Gene Expression Information ◽

Local Properties

Abstract Background Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed to solve this issue. These algorithms usually consider a node’s direct neighbors and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection.Results To overcome this deficiency, this paper proposes a new protein complex identification method based on node-local topological properties and gene expression information on a new weighted PPI network, named NLPGE-WPN (joint node-local topological properties and gene expression information on weighted PPI network). First, based on the resource allocation of the PPI network and gene expression, a new weight metric is designed to describe the interaction between proteins. Second, our method constructs a series of dense complex cores based on density and network diameter constraints; the final complexes are recognized by expanding the second-order neighbor nodes of core complexes. Experimental results demonstrate that this algorithm has improved the performances of precision and f-measure, which is more valid in identifying protein complexes．Conclusions This identification method is simple and can accurately identify more complexes by integrating node-local properties and gene expression on PPI weighted networks.

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Computational Prediction of Heteromeric Protein Complex Disassembly Order with Hybrid Monte Carlo/Molecular Dynamics Simulation

10.1101/2022.01.12.476000 ◽

2022 ◽

Author(s):

Ikuo Kurisaki ◽

Shigenori Tanaka

Keyword(s):

Molecular Dynamics ◽

Monte Carlo ◽

Protein Complex ◽

Protein Complexes ◽

Structural Bioinformatics ◽

Dynamics Simulation ◽

Tryptophan Synthase ◽

Selection Scheme ◽

Hybrid Monte Carlo ◽

Multimeric Protein

The physicochemical entity of biological phenomenon in the cell is a network of biochemical reactions and the activity of such a network is regulated by multimeric protein complexes. Mass spectroscopy (MS) experiments and multimeric protein docking simulations based on structural bioinformatics techniques have revealed the molecular-level stoichiometry and static configuration of subcomplexes in their bound forms, then revealing the subcomplex populations and formation orders. Meanwhile, these methodologies are not designed to straightforwardly examine temporal dynamics of multimeric protein assembly and disassembly, essential physicochemical properties to understand functional expression mechanisms of proteins in the biological environment. To address the problem, we had developed an atomistic simulation in the framework of the hybrid Monte Carlo/Molecular Dynamics (hMC/MD) method and succeeded in observing disassembly of homomeric pentamer of the serum amyloid P component protein in experimentally consistent order. In this study, we improved the hMC/MD method to examine disassembly processes of the tryptophan synthase tetramer, a paradigmatic heteromeric protein complex in MS studies. We employed the likelihood-based selection scheme to determine a dissociation-prone subunit pair at each hMC/MD simulation cycle and achieved highly reliable predictions of the disassembly orders with the success rate over 0.9 without a priori knowledge of the MS experiments and structural bioinformatics simulations. We similarly succeeded in reliable predictions for the other three tetrameric protein complexes. These achievements indicate the potential availability of our hMC/MD approach as the general purpose methodology to obtain microscopic and physicochemical insights into multimeric protein complex formation.

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Identification of Potential Plants Producing Tannin-protein Complex for a-amylase as Botanical Pesticide

Pelita Perkebunan (a Coffee and Cocoa Research Journal) ◽

10.22302/iccri.jur.pelitaperkebunan.v29i1.189 ◽

2013 ◽

Vol 29 (1) ◽

Author(s):

Asriyah Firdausi ◽

Tri Agus Siswoyo ◽

Soekadar Wiryadiputra

Keyword(s):

Protein Complex ◽

Protein Complexes ◽

Gliricidia Sepium ◽

In Vitro Test ◽

Betel Nut ◽

Botanical Pesticides ◽

Botanical Pesticide ◽

Areca Catechu ◽

Gnetum Gnemon

Research on the development of botanical pesticides should be developed through new methods, such as by inhibiting the activity of digestive enzymes by secondary metabolites. The aim of this study was to identify some of potential plants as a source of tannin-protein complexes to inhibitthe activity of - amylase. The study of identification of potential plants producing the active ingredient tannin-protein complex was divided into three stages, 1) identification of potential plants producing tannin, 2) isolation of tannin-protein complexes, and 3) in vitro test of tannin-protein complexes effect of the -amylase activity. Some of the observed plants were sidaguri leaf (Sida rhombifolia), melinjo leaf (Gnetum gnemon), gamal leaf (Gliricidia sepium),lamtoro leaf (Leucaena leucocephala) , betel nut (Areca catechu) , and crude gambier (Uncaria gambir) a s a source of tannins and melinjo seed was used asprotein source. Betel nut and melinjo seed were the best source of tannin-protein complex, tannin content 1.77 mg TAE/mL with antioxidant activity of 90%,the ability to inhibit the activity of -amylase by 95% with IC 50 values of 10 mg/mL.Key words: Tannin, protein, -amylase, botanical pesticides,Areca catechu, Gnetum gnemon.

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ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

10.1101/573774 ◽

2019 ◽

Author(s):

Wojciech Michalak ◽

Vasileios Tsiamis ◽

Veit Schwämmle ◽

Adelina Rogowska-Wrzesińska

Keyword(s):

T Cell Activation ◽

Protein Complex ◽

Quantitative Proteomics ◽

Large Scale ◽

Protein Complexes ◽

Quantitative Measure ◽

Exploratory Analysis ◽

List Type ◽

Link Type ◽

Complex Components

AbstractWe have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.A fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/The source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowserHighlightsAutomated analysis of protein complexes in proteomics experimentsQuantitative measure of the coordinated changes in protein complex componentsInteractive visualisations for exploratory analysis of proteomics resultsIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

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Robustness of Protein Complex Networks

2012 International Conference on Computer Science and Service System ◽

10.1109/csss.2012.215 ◽

2012 ◽

Author(s):

Chien-Hung Huang ◽

Szu-Yu Chou ◽

Ka-Lok Ng

Keyword(s):

Complex Networks ◽

Protein Complex

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Differential Evolution Dynamic Analysis in the Form of Complex Networks

Advanced Methods for Complex Network Analysis - Advances in Wireless Technologies and Telecommunication ◽

10.4018/978-1-4666-9964-9.ch012 ◽

2016 ◽

pp. 285-318 ◽

Cited By ~ 3

Author(s):

Lenka Skanderova ◽

Ivan Zelinka

Keyword(s):

Evolutionary Algorithms ◽

Differential Evolution ◽

Complex Networks ◽

Dynamic Analysis ◽

Evolutionary Algorithm ◽

Complex Network ◽

Novel Method

In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.

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