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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Arnaud Droit,
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Auer F, Mayer S, Kramer F. Data-dependent visualization of biological networks in the web-browser with NDExEdit. PLoS Comput Biol 2022; 18:e1010205. [PMID: 35675360 PMCID: PMC9212158 DOI: 10.1371/journal.pcbi.1010205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 06/21/2022] [Accepted: 05/15/2022] [Indexed: 12/02/2022] Open
Abstract
Networks are a common methodology used to capture increasingly complex associations between biological entities. They serve as a resource of biological knowledge for bioinformatics analyses, and also comprise the subsequent results. However, the interpretation of biological networks is challenging and requires suitable visualizations dependent on the contained information. The most prominent software in the field for the visualization of biological networks is Cytoscape, a desktop modeling environment also including many features for analysis. A further challenge when working with networks is their distribution. Within a typical collaborative workflow, even slight changes of the network data force one to repeat the visualization step as well. Also, just minor adjustments to the visual representation not only need the networks to be transferred back and forth. Collaboration on the same resources requires specific infrastructure to avoid redundancies, or worse, the corruption of the data. A well-established solution is provided by the NDEx platform where users can upload a network, share it with selected colleagues or make it publicly available. NDExEdit is a web-based application where simple changes can be made to biological networks within the browser, and which does not require installation. With our tool, plain networks can be enhanced easily for further usage in presentations and publications. Since the network data is only stored locally within the web browser, users can edit their private networks without concerns of unintentional publication. The web tool is designed to conform to the Cytoscape Exchange (CX) format as a data model, which is used for the data transmission by both tools, Cytoscape and NDEx. Therefore the modified network can be directly exported to the NDEx platform or saved as a compatible CX file, additionally to standard image formats like PNG and JPEG. Relations in biological research are often visualized as networks. For instance, if two proteins interact with each other during a certain process, the corresponding network would show two nodes connected by one edge. But the fact that the interaction between the two exists, may not be enough. With established software solutions like Cytoscape we can add all the information we have about our nodes and their interaction to our data foundation. Furthermore, we can change the visual appearance of our nodes and their interaction based on this information. For example, if our network contains 20 nodes, that all interact with each other, but the strength of these interactions each range between 0 and 1, we can illustrate that by making the edges wider for strong interactions and slimmer for weak interactions. Thus, our visualization is enriched with valuable information. As of now these data-dependent modifications can only be made with a desktop client. We introduce NDExEdit, a web-based solution for visualization changes to networks that conform to the CX data format. It allows us to import networks directly from the NDEx platform and apply changes to the visualization—including all types of mappings, one of which was briefly described above.
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Affiliation(s)
- Florian Auer
- Department of IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, University of Augsburg, Augsburg, Germany
- * E-mail:
| | - Simone Mayer
- Department of IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, University of Augsburg, Augsburg, Germany
| | - Frank Kramer
- Department of IT-Infrastructure for Translational Medical Research, Faculty of Applied Computer Science, University of Augsburg, Augsburg, Germany
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Chung SS, Ng JCF, Laddach A, Thomas NSB, Fraternali F. Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks. NAR Genom Bioinform 2021; 3:lqab010. [PMID: 33709075 PMCID: PMC7936661 DOI: 10.1093/nargab/lqab010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/19/2020] [Accepted: 01/26/2021] [Indexed: 11/13/2022] Open
Abstract
Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein-protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein-Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named 'short loop commonality' to measure indirect PPIs occurring via common SLM interactions. This detects 'modules' of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR-Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.
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Affiliation(s)
- Sun Sook Chung
- Department of Haematological Medicine, King's College London, London, SE5 9NU, UK
| | - Joseph C F Ng
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, SE1 1UL, UK
| | - Anna Laddach
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, SE1 1UL, UK
| | - N Shaun B Thomas
- Department of Haematological Medicine, King's College London, London, SE5 9NU, UK
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King's College London, London, SE1 1UL, UK
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Kaalia R, Rajapakse JC. Functional homogeneity and specificity of topological modules in human proteome. BMC Bioinformatics 2019; 19:553. [PMID: 30717667 PMCID: PMC7394330 DOI: 10.1186/s12859-018-2549-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 11/30/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome. RESULTS We investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome. CONCLUSIONS The topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.
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Affiliation(s)
- Rama Kaalia
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Jagath C. Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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Jiang M, Pei Z, Fan X, Jiang J, Wang Q, Zhang Z. Function Analysis of Human Protein Interactions Based on a Novel Minimal Loop Algorithm. Curr Bioinform 2019. [DOI: 10.2174/1574893613666180906103946] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Background:
Various properties of Protein-Protein Interaction (PPI) network have been
widely exploited to discover the topological organizing principle and the crucial function motifs
involving specific biological pathway or disease process. The current motifs of PPI network are
either detected by the topology-based coarse grain algorithms, i.e. community discovering, or
depended on the limited-accessible protein annotation data derived precise algorithms. However,
the identified network motifs are hardly compatible with the well-defined biological functions
according to those two types of methods.
Method:
In this paper, we proposed a minimal protein loop finding method to explore the
elementary structural motifs of human PPI network. Initially, an improved article exchange model
was designed to search all the independent shortest protein loops of PPI network. Furthermore,
Gene Ontology (GO) based function clustering analysis was implemented to identify the biological
functions of the shortest protein loops. Additionally, the disease process associated shortest protein
loops were considered as the potential drug targets.
</P><P>
Result: Our proposed method presents the lowest computational complexity and the highest
functional consistency, compared to the three other methods. The functional enrichment and
clustering analysis for the identified minimal protein loops revealed the high correlation between
the protein loops and the corresponding biological functions, particularly, statistical analysis
presenting the protein loops with the length less than 4 is closely connected with some disease
process, suggesting the potential drug target.
Conclusion:
Our minimal protein loop method provides a novel manner to precisely define the
functional motif of PPI network, which extends the current knowledge about the cooperating
mechanisms and topological properties of protein modules composed of the short loops.
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Affiliation(s)
- Mingyang Jiang
- Inner Mongolia Engineering Research Center of Personalized Medicine, College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao, China
| | - Zhili Pei
- Inner Mongolia Engineering Research Center of Personalized Medicine, College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao, China
| | - Xiaojing Fan
- College of Mechanical Engineering, Inner Mongolia University for the Nationalities, Tongliao, China
| | - Jingqing Jiang
- Inner Mongolia Engineering Research Center of Personalized Medicine, College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao, China
| | - Qinghu Wang
- Inner Mongolia Engineering Research Center of Personalized Medicine, College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao, China
| | - Zhifeng Zhang
- Inner Mongolia Engineering Research Center of Personalized Medicine, College of Computer Science and Technology, Inner Mongolia University for the Nationalities, Tongliao, China
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Ignatius Pang CN, Goel A, Wilkins MR. Investigating the Network Basis of Negative Genetic Interactions in Saccharomyces cerevisiae with Integrated Biological Networks and Triplet Motif Analysis. J Proteome Res 2018; 17:1014-1030. [DOI: 10.1021/acs.jproteome.7b00649] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Chi Nam Ignatius Pang
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Apurv Goel
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
| | - Marc R. Wilkins
- Systems
Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, New South Wales 2052, Australia
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7
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Fraternali F. [Protein-protein interacting networks, their structures and disease-related mutations]. Biol Aujourdhui 2018; 211:223-228. [PMID: 29412132 DOI: 10.1051/jbio/2017031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Indexed: 11/14/2022]
Abstract
In recent years, the comparison of protein interactomes has identified conserved modules, that could represent functional nuclei with a common ancestry. Within this context, recent analyses of protein-protein interacting networks have led to a debate on the influence of the experimental method on the quality and biological pertinence of these data. It is crucial to understand the measure in which divergence between networks of different species reflect sampling biases in respective experimental methods, as opposed to topological features dictated by biological functionality. This aspect requires novel, precise and practical mathematical tools, to quantify and compare high resolution networks. To this end, we have studied the relationship between pools of aleatory graphs and real biological signalization networks, while stressing the number of graph cycles in the networks, which represent complexes in experimental protein interactomes. By combining methods for graph and algorithm dynamics to count the loops, we evaluate the relative importance of the loops in biological networks in comparison with network analyses.
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Affiliation(s)
- Franca Fraternali
- Randall Division of Cellular and Molecular Biology, King's College, London, UK
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Ding D, Sun X. Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1. Genes (Basel) 2018; 9:E41. [PMID: 29337910 PMCID: PMC5793192 DOI: 10.3390/genes9010041] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 01/07/2018] [Accepted: 01/12/2018] [Indexed: 12/22/2022] Open
Abstract
Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c-type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.
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Affiliation(s)
- Dewu Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
- Department of Mathematics and Computer Science, Chizhou College, Chizhou 247000, China.
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
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Laddach A, Ng JCF, Chung SS, Fraternali F. Genetic variants and protein-protein interactions: a multidimensional network-centric view. Curr Opin Struct Biol 2018; 50:82-90. [PMID: 29306755 DOI: 10.1016/j.sbi.2017.12.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 12/19/2017] [Accepted: 12/20/2017] [Indexed: 01/18/2023]
Abstract
We review recent progress in the mapping of genetic variants to proteins, in the context of their interactions, as measured from experiments and/or computational predictions. Such variants can impact on the molecular mechanisms underlying an interaction and its stability. We highlight recent work which relies on the effective use of protein-protein interaction networks (PPINs), integrated with 3D structural information, for evaluating disease-associated variants. Furthermore, we discuss how the integration of multiple layers of biological information, in the context of PPINs, can improve the interpretation of genetic variants and inspire new therapeutic strategies.
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Affiliation(s)
- Anna Laddach
- Randall Division of Cell and Molecular Biophysics, King's College London, UK
| | - Joseph Chi-Fung Ng
- Randall Division of Cell and Molecular Biophysics, King's College London, UK
| | - Sun Sook Chung
- Randall Division of Cell and Molecular Biophysics, King's College London, UK; Department of Haematological Medicine, King's College London, UK
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, UK.
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Mohammadi S, Gleich DF, Kolda TG, Grama A. Triangular Alignment (TAME): A Tensor-Based Approach for Higher-Order Network Alignment. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017; 14:1446-1458. [PMID: 27483461 DOI: 10.1109/tcbb.2016.2595583] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Network alignment has extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provides a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we identify a closely related surrogate function whose maximization results in a tensor eigenvector problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. Using a case study on the NAPAbench dataset, we show that triangular alignment is capable of producing mappings with high node correctness. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods in terms of conserved triangles. In addition, we show that the number of conserved triangles is more significantly correlated, compared to the conserved edge, with node correctness and co-expression of edges. Our formulation and resulting algorithms can be easily extended to arbitrary motifs.
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Abstract
Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of big data. Research organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx can also facilitate the integration of networks as data in electronic publications, thus making a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow seamlessly between scientists.
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12
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Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions. J Biol Phys 2016; 42:339-50. [PMID: 27012959 DOI: 10.1007/s10867-016-9410-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Accepted: 01/27/2016] [Indexed: 01/21/2023] Open
Abstract
Thermodynamics is an important driving factor for chemical processes and for life. Earlier work has shown that each cancer has its own molecular signaling network that supports its life cycle and that different cancers have different thermodynamic entropies characterizing their signaling networks. The respective thermodynamic entropies correlate with 5-year survival for each cancer. We now show that by overlaying mRNA transcription data from a specific tumor type onto a human protein-protein interaction network, we can derive the Gibbs free energy for the specific cancer. The Gibbs free energy correlates with 5-year survival (Pearson correlation of -0.7181, p value of 0.0294). Using an expression relating entropy and Gibbs free energy to enthalpy, we derive an empirical relation for cancer network enthalpy. Combining this with previously published results, we now show a complete set of extensive thermodynamic properties and cancer type with 5-year survival.
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Lu HC, Herrera Braga J, Fraternali F. PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks. ACTA ACUST UNITED AC 2016; 32:2534-6. [PMID: 27153707 PMCID: PMC4978923 DOI: 10.1093/bioinformatics/btw153] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 03/15/2016] [Indexed: 12/24/2022]
Abstract
Summary: We present a practical computational pipeline to readily perform data analyses of protein–protein interaction networks by using genetic and functional information mapped onto protein structures. We provide a 3D representation of the available protein structure and its regions (surface, interface, core and disordered) for the selected genetic variants and/or SNPs, and a prediction of the mutants’ impact on the protein as measured by a range of methods. We have mapped in total 2587 genetic disorder-related SNPs from OMIM, 587 873 cancer-related variants from COSMIC, and 1 484 045 SNPs from dbSNP. All result data can be downloaded by the user together with an R-script to compute the enrichment of SNPs/variants in selected structural regions. Availability and Implementation: PinSnps is available as open-access service at http://fraternalilab.kcl.ac.uk/PinSnps/ Contact:franca.fraternali@kcl.ac.uk Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hui-Chun Lu
- Randall Division of Cell and Molecular Biophysics, King's College London, London SE1 1UL, UK
| | - Julián Herrera Braga
- Randall Division of Cell and Molecular Biophysics, King's College London, London SE1 1UL, UK
| | - Franca Fraternali
- Randall Division of Cell and Molecular Biophysics, King's College London, London SE1 1UL, UK
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14
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Peguero-Sanchez E, Pardo-Lopez L, Merino E. IRES-dependent translated genes in fungi: computational prediction, phylogenetic conservation and functional association. BMC Genomics 2015; 16:1059. [PMID: 26666532 PMCID: PMC4678720 DOI: 10.1186/s12864-015-2266-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2015] [Accepted: 12/01/2015] [Indexed: 01/17/2023] Open
Abstract
Background The initiation of translation via cellular internal ribosome entry sites plays an important role in the stress response and certain physiological conditions in which canonical cap-dependent translation initiation is compromised. Currently, only a limited number of these regulatory elements have been experimentally identified. Notably, cellular internal ribosome entry sites lack conservation of both the primary sequence and mRNA secondary structure, rendering their identification difficult. Despite their biological importance, the currently available computational strategies to predict them have had limited success. We developed a bioinformatic method based on a support vector machine for the prediction of internal ribosome entry sites in fungi using the 5’-UTR sequences of 20 non-redundant fungal organisms. Additionally, we performed a comparative analysis and characterization of the functional relationships among the gene products predicted to be translated by this cap-independent mechanism. Results Using our method, we predicted 6,532 internal ribosome entry sites in 20 non-redundant fungal organisms. Some orthologous groups were enriched with our positive predictions. This is the case of the HSP70 chaperone family, which remarkably has two verified internal ribosome entry sites, one in humans and the other in flies. A second example is the orthologous group of the eIF4G repression protein Sbp1p, which has two homologous genes known to be translated by this cap-independent mechanism, one in mice and the other in yeast. These examples emphasize the wide conservation of these regulatory elements as a result of selective pressure. In addition, we performed a protein-protein interaction network characterization of the gene products of our positive predictions using Saccharomyces cerevisiae as a model, which revealed a highly connected and modular topology, suggesting a functional association. A remarkable example of this functional association is our prediction of internal ribosome entry sites elements in three components of the RNA polymerase II mediator complex. Conclusions We developed a method for the prediction of cellular internal ribosome entry sites that may guide experimental and bioinformatic analyses to increase our understanding of protein translation regulation. Our analysis suggests that fungi show evolutionary conservation and functional association of proteins translated by this cap-independent mechanism. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2266-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Esteban Peguero-Sanchez
- Departamento de Microbiología Molecular, Instituto de Biotecnología, UNAM, Av. Universidad 2001, Cuernavaca, Morelos, CP 62210, Mexico.
| | - Liliana Pardo-Lopez
- Departamento de Microbiología Molecular, Instituto de Biotecnología, UNAM, Av. Universidad 2001, Cuernavaca, Morelos, CP 62210, Mexico.
| | - Enrique Merino
- Departamento de Microbiología Molecular, Instituto de Biotecnología, UNAM, Av. Universidad 2001, Cuernavaca, Morelos, CP 62210, Mexico.
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