Detection of Covalent DNA-Protein Complexes: The Adenovirus DNA-Terminal Protein Complex and HeLa DNA-Protein Complexes

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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Evolutionary Conservation Levels of Subunits of Histone-Modifying Protein Complexes in Fungi

Comparative and Functional Genomics ◽

10.1155/2009/379317 ◽

2009 ◽

Vol 2009 ◽

pp. 1-6 ◽

Cited By ~ 5

Author(s):

Hiromi Nishida

Keyword(s):

Saccharomyces Cerevisiae ◽

Histone Acetylation ◽

Histone Methylation ◽

Protein Complex ◽

Histone Acetyltransferase ◽

Protein Complexes ◽

Evolutionary Conservation ◽

Catalytic Subunits ◽

Evolutionary Lineage ◽

Conservation Level

Eukaryotes possess a variety of histone-modifying protein complexes. Generally, a histone-modifying protein complex consists of multiple subunits, that is, a catalytic subunit and the associated subunits. In this study, I analyzed 62 and 48 subunits of the histone-modifying protein complexes ofSaccharomyces cerevisiaeandSchizosaccharomyces pombe, respectively. The evolutionary conservation levels of the 110 subunits were measured. The measurements revealed that the conservation levels of the catalytic subunits are significantly higher than those of the associated subunits of the histone acetyltransferase and deacetylase complexes; however, the conservation level of the catalytic subunits is similar to that of the associated subunits of the histone methyltransferase complexes. Thus, in the fungal histone acetylation and deacetylation systems, the catalytic subunits of histone-modifying protein complexes are conserved and the associated subunits are evolutionary lineage-specific. In contrast, in the fungal histone methylation system, both the catalytic and the associated subunits are evolutionary lineage-specific.

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

Algorithms for Molecular Biology ◽

10.1186/s13015-015-0044-6 ◽

2015 ◽

Vol 10 (1) ◽

Cited By ~ 13

Author(s):

Duc-Hau Le

Keyword(s):

Complex Networks ◽

Protein Complex ◽

Protein Complexes ◽

Functional Similarity ◽

Novel Method

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The 5S RNA – protein complex from yeast: a model for the evolution and structure of the eukaryotic ribosome

Canadian Journal of Biochemistry ◽

10.1139/o82-058 ◽

1982 ◽

Vol 60 (4) ◽

pp. 490-496 ◽

Cited By ~ 13

Author(s):

Ross N. Nazar ◽

Makoto Yaguchi ◽

Gordon E. Willick

Keyword(s):

Binding Site ◽

Protein Complex ◽

Rna Binding ◽

Rna Binding Proteins ◽

Protein Complexes ◽

Basic Structure ◽

5S Rna ◽

Chemical Studies ◽

Physical And Chemical ◽

Rna Protein Complexes

The ribosomal 5S RNA – protein complex appears to be an excellent model for studies on the evolution and structure of ribosomes. In eukaryotes this complex is composed of two components, the 5S rRNA and a single ribosomal protein which in yeast has a molecular weight of about 38 000. The primary protein-binding site is located in the 3′-end region of the 5S RNA together with a small portion of the 5′ end. The primary RNA-binding site appears to be situated in the C-terminal end of the protein (YL3 in yeast) but the binding specificity requires other structural elements in the N-terminal half of the molecule. When compared with prokaryotic 5S RNA – protein complexes, various physical and chemical studies suggest that the basic structure and interactions have been conserved in the course of evolution, but that the single larger eukaryotic 5S RNA binding protein has evolved through a fusion of genes for the multiple 5S RNA binding proteins in prokaryotes.

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