Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

2010 ◽  
Vol 1 (3) ◽  
pp. 20-35
Author(s):  
Young-Rae Cho ◽  
Aidong Zhang
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Characterization ◽  
Flow Simulation ◽  
Data Set ◽  
Systematic Analysis ◽  
Core Proteins ◽  
Interactome Network ◽  
Protein Interactome ◽  
Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

2013 ◽  
pp. 222-238
Author(s):  
Young-Rae Cho ◽  
Aidong Zhang
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Characterization ◽  
Flow Simulation ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Graph Theoretic ◽  
Interactome Network ◽  
Protein Interactome ◽  
Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

Revisiting the deformation transients before the 2011 Tohoku-Oki Megathrust Earthquake with GPS

2020 ◽  
Author(s):  
Anne Socquet ◽  
Lou Marill ◽  
David Marsan ◽  
Baptiste Rousset ◽  
Mathilde Radiguet ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Gravity Anomalies ◽  
Seismogenic Zone ◽  
Double Difference ◽  
Ocean Bottom ◽  
Slow Slip ◽  
Short Term ◽  
Data Set ◽  
Systematic Analysis

<p>The precursory activity leading up to the Tohoku-Oki earthquake of 2011 has been suggested to feature both long- and short-term episodes of decoupling and suggests a particularly complex slow slip history. The analysis of the F3 solution of the Japanese GPS network suggested that an accelerated slip occurred in the deeper part of the seismogenic zone during the 10 years preceding the earthquake (Heki & Mitsui, EPSL 2013; Mavrommatis et al., GRL 2014; Yokota & Koketsu, Nat. Com. 2015). During the two months preceding the earthquake, no anomaly in the GPS position time series has been revealed so far, although several anomalous geophysical signals have been reported (an extended foreshock crisis near the future hypocenter (Kato et al., Science 2012), a synchronized increase of intermediate-depth background seismicity (Bouchon et al., Nat Geosc. 2016), a signal in ocean-bottom pressure gauges and on-land strainmeter time series (Ito et al., Tectonoph. 2013), and large scale gravity anomalies that suggest deep-seated slab deformation processes (Panet et al., Nat. Geosc. 2018 ; Wang & Burgmann, GRL 2019)).</p><p>We present novel results based on an independent analysis of the Japanese GPS data set. We perform a full reprocessing of the raw data with a double-difference approach, a systematic analysis of the obtained time-series, including noise characterization and network filtering, and make a robust assessment of long- and short-term tectonic aseismic transients preceding the Tohoku-Oki earthquake. An accelerated slip on the lower part of the seismogenic zone over the last decade is confirmed, not only below the epicenter of Tohoku-Oki earthquake but also further south, offshore Boso peninsula, which is a worrying sign of an on-going slow decoupling east of Tokyo. At shorter time-scale, first results seem compatible with a slow slip close to the epicenter initiating ~ 2 months before the mainshock.</p>

scholarly journals Algorithms for protein interaction networks

2005 ◽  
Vol 33 (3) ◽  
pp. 530-534 ◽  
Author(s):  
M. Lappe ◽  
L. Holm
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Sequence Similarity ◽  
Functional Characterization ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Interaction Patterns ◽  
Main Challenge

The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.

scholarly journals Recent advances in large-scale protein interactome mapping

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 782 ◽  
Author(s):  
Virja Mehta ◽  
Laura Trinkle-Mulcahy
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Specific Protein ◽  
Quality Data ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Biological Studies ◽  
Protein Interactome ◽  
Literature Curation

Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives (where it can be combined with other ‘omics’ data to gain a better understanding of functional pathways and networks) and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation of low-throughput studies of specific protein complexes, but also an increase in the deposition of high-quality data from non-biased high-throughput experimental PPI mapping strategies into publicly available databases.

scholarly journals Can Bibliographic Pointers for Known Biological Data Be Found Automatically? Protein Interactions as a Case Study

2001 ◽  
Vol 2 (4) ◽  
pp. 196-206 ◽  
Author(s):  
Christian Blaschke ◽  
Alfonso Valencia
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Biological Data ◽  
Positive Finding ◽  
Data Set ◽  
Large Scale Assessment ◽  
Low Coverage ◽  
Standard Protein

The Dictionary of Interacting Proteins(DIP) (Xenarioset al., 2000) is a large repository of protein interactions: its March 2000 release included 2379 protein pairs whose interactions have been detected by experimental methods. Even if many of these correspond to poorly characterized proteins, the result of massive yeast two-hybrid screenings, as many as 851 correspond to interactions detected using direct biochemical methods.We used information retrieval technology to search automatically for sentences in Medline abstracts that support these 851 DIP interactions. Surprisingly, we found correspondence between DIP protein pairs and Medline sentences describing their interactions in only 30% of the cases. This low coverage has interesting consequences regarding the quality of annotations (references) introduced in the database and the limitations of the application of information extraction (IE) technology to Molecular Biology. It is clear that the limitation of analyzing abstracts rather than full papers and the lack of standard protein names are difficulties of considerably more importance than the limitations of the IE methodology employed. A positive finding is the capacity of the IE system to identify new relations between proteins, even in a set of proteins previously characterized by human experts. These identifications are made with a considerable degree of precision.This is, to our knowledge, the first large scale assessment of IE capacity to detect previously known interactions: we thus propose the use of the DIP data set as a biological reference to benchmark IE systems.

An empirical analysis of emoji usage on Twitter

2019 ◽  
Vol 119 (8) ◽  
pp. 1748-1763 ◽  
Author(s):  
Mengdi Li ◽  
Eugene Chng ◽  
Alain Yee Loong Chong ◽  
Simon See
Keyword(s):  
Empirical Analysis ◽  
Large Scale ◽  
Cultural Dimensions ◽  
Data Set ◽  
Systematic Analysis ◽  
Content Type ◽  
Textual Data ◽  
Research Questions ◽  
Twitter Users ◽  
Different Cultures

Purpose Emoji has become an essential component of any digital communication and its importance can be attested to by its sustained popularity and widespread use. However, research in Emojis is rarely to be seen due to the lack of data at a greater scale. The purpose of this paper is to systematically analyse and compare the usage of Emojis in a cross-cultural manner. Design/methodology/approach This research conducted an empirical analysis using a large-scale, cross-regional emoji usage data set from Twitter, a platform where the limited 140 characters allowance has made it essential for the inclusion of emojis within tweets. The extremely large textual data set covers a period of only two months, but the 673m tweets authored by more than 2,081,542 unique users is a sufficiently large sample for the authors to yield significant results. Findings This research discovered that the categories and frequencies of Emojis communicated by users can provide a rich source of data to understand cultural differences between Twitter users from a large range of demographics. This research subsequently demonstrated the preferential use of Emojis complies with Hofstede’s Cultural Dimensions Model, in which different representations of demographics and culture within countries present significantly different use of Emojis to communicate emotions. Originality/value This study provides a robust example of how to strategically conduct research using large-scale emoji data to pursue research questions previously difficult. To the best of authors’ knowledge, the present study pioneers the first systematic analysis and comparison of the usage of emojis on Twitter across different cultures; it is the largest, in terms of the scale study of emoji usage to-date.

scholarly journals Large-Scale Proteomic Analysis of Follicular Lymphoma Reveals Extensive Remodeling of Cell Adhesion Pathway and Identifies Hub Proteins Related to the Lymphomagenesis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 630
Author(s):  
Kamila Duś-Szachniewicz ◽  
Grzegorz Rymkiewicz ◽  
Anil Kumar Agrawal ◽  
Paweł Kołodziej ◽  
Jacek R. Wiśniewski
Keyword(s):  
Mass Spectrometry ◽  
B Cells ◽  
Follicular Lymphoma ◽  
B Cell ◽  
Protein Interactions ◽  
Large Scale ◽  
Cell Receptor ◽  
Cellular Adhesion ◽  
Proteomic Profiling ◽  
Hub Proteins

Follicular lymphoma (FL) represents the major subtype of indolent B-cell non-Hodgkin lymphomas (B-NHLs) and results from the malignant transformation of mature B-cells in lymphoid organs. Although gene expression and genomic studies have identified multiple disease driving gene aberrations, only a few proteomic studies focused on the protein level. The present work aimed to examine the proteomic profiles of follicular lymphoma vs. normal B-cells obtained by fine-needle aspiration biopsy (FNAB) to gain deep insight into the most perturbed pathway of FL. The cells of interest were purified by magnetic-activated cell sorting (MACS). High-throughput proteomic profiling was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allowed to identify of 6724 proteins in at least 75% of each group of samples. The ‘Total Protein Approach’ (TPA) was applied to the absolute quantification of proteins in this study. We identified 1186 differentially abundant proteins (DAPs) between FL and control samples, causing an extensive remodeling of several molecular pathways, including the B-cell receptor signaling pathway, cellular adhesion molecules, and PPAR pathway. Additionally, the construction of protein–protein interactions networks (PPINs) and identification of hub proteins allowed us to indicate the key player proteins for FL pathology. Finally, ICAM1, CD9, and CD79B protein expression was validated in an independent cohort by flow cytometry (FCM), and the results were consistent with the mass spectrometry (MS) data.

scholarly journals Yeast Two-Hybrid Systems and Protein Interaction Mapping Projects for Yeast and Worm

Yeast ◽  
2000 ◽  
Vol 1 (2) ◽  
pp. 88-94 ◽  
Author(s):  
Albertha J. M. Walhout ◽  
Simon J. Boulton ◽  
Marc Vidal
Keyword(s):  
Hybrid Systems ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Systematic Analysis ◽  
Technical Features ◽  
Two Hybrid

The availability of complete genome sequences necessitates the development of standardized functional assays to analyse the tens of thousands of predicted gene products in high-throughput experimental settings. Such approaches are collectively referred to as ‘functional genomics’. One approach to investigate the properties of a proteome of interest is by systematic analysis of protein–protein interactions. So far, the yeast two-hybrid system is the most commonly used method for large-scale, high-throughput identification of potential protein–protein interactions. Here, we discuss several technical features of variants of the two-hybrid systems in light of data recently obtained from different protein interaction mapping projects for the budding yeastSaccharomyces cerevisiaeand the nematodeCaenorhabditis elegans.

scholarly journals Yeast Two-Hybrid Systems and Protein Interaction Mapping Projects for Yeast and Worm

Yeast ◽  
2000 ◽  
Vol 1 (2) ◽  
pp. 88-94 ◽  
Author(s):  
Albertha J. M. Walhout ◽  
Simon J. Boulton ◽  
Marc Vidal
Keyword(s):  
Hybrid Systems ◽  
High Throughput ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Systematic Analysis ◽  
Technical Features ◽  
Two Hybrid

The availability of complete genome sequences necessitates the development of standardized functional assays to analyse the tens of thousands of predicted gene products in high-throughput experimental settings. Such approaches are collectively referred to as ‘functional genomics’. One approach to investigate the properties of a proteome of interest is by systematic analysis of protein–protein interactions. So far, the yeast two-hybrid system is the most commonly used method for large-scale, high-throughput identification of potential protein–protein interactions. Here, we discuss several technical features of variants of the two-hybrid systems in light of data recently obtained from different protein interaction mapping projects for the budding yeast Saccharomyces cerevisiae and the nematode Caenorhabditis elegans.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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