scholarly journals Protein-Protein Interactions Inferred from Domain-Domain Interactions in Genogroup II Genotype 4 Norovirus Sequences

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Chuan-Ching Huang ◽  
Chuan Yi Tang
Keyword(s):  
Protein Interactions ◽  
Protein Domain ◽  
Protein Protein Interactions ◽  
Domain Interaction ◽  
Genotype 4 ◽  
Interaction Database ◽  
Domain Interactions ◽  
Severe Gastroenteritis ◽  
Worldwide Phenomenon ◽  
Infection Processes

Severe gastroenteritis and foodborne illness caused by Noroviruses (NoVs) during the winter are a worldwide phenomenon. Vulnerable populations including young children and elderly and immunocompromised people often require hospitalization and may die. However, no efficient vaccine for NoVs exists because of their variable genome sequences. This study investigates the infection processes in protein-protein interactions between hosts and NoVs. Protein-protein interactions were collected from related Pfam NoV domains. The related Pfam domains were accumulated incrementally from the protein domain interaction database. To examine the influence of domain intimacy, the 7 NoV domains were grouped by depth. The number of domain-domain interactions increased exponentially as the depth increased. Many protein-protein interactions were relevant; therefore, cloud techniques were used to analyze data because of their computational capacity. The infection relationship between hosts and NoVs should be used in clinical applications and drug design.

AN INTEGRATIVE DOMAIN-BASED APPROACH TO PREDICTING PROTEIN–PROTEIN INTERACTIONS

2008 ◽  
Vol 06 (06) ◽  
pp. 1115-1132 ◽  
Author(s):  
THANH-PHUONG NGUYEN ◽  
TU-BAO HO
Keyword(s):  
Computational Methods ◽  
Protein Interactions ◽  
Inductive Logic ◽  
High Sensitivity ◽  
Protein Domain ◽  
Protein Protein Interactions ◽  
Reciprocal Relationships ◽  
Multiple Data ◽  
Domain Interactions ◽  
Almost All

Protein–protein interactions (PPIs) are intrinsic to almost all cellular processes. Different computational methods offer new chances to study PPIs. To predict PPIs, while the integrative methods use multiple data sources instead of a single source, the domain-based methods often use only protein domain features. Integration of both protein domain features and genomic/proteomic features from multiple databases can more effectively predict PPIs. Moreover, it allows discovering the reciprocal relationships between PPIs and biological features of their interacting partners. We developed a novel integrative domain-based method for predicting PPIs using inductive logic programming (ILP). Two principal domain features used were domain fusions and domain–domain interactions (DDIs). Various relevant features of proteins were exploited from five popular genomic and proteomic databases. By integrating these features, we constructed biologically significant ILP background knowledge of more than 278,000 ground facts. The experimental results through multiple 10-fold cross-validations demonstrated that our method predicts PPIs better than other computational methods in terms of typical performance measures. The proposed ILP framework can be applied to predict DDIs with high sensitivity and specificity. The induced ILP rules gave us many interesting, biologically reciprocal relationships among PPIs, protein domains, and PPI-related genomic/proteomic features. Supplementary material is available at .

scholarly journals DISPOT: a simple knowledge-based protein domain interaction statistical potential

2019 ◽  
Vol 35 (24) ◽  
pp. 5374-5378 ◽  
Author(s):  
Oleksandr Narykov ◽  
Dmytro Bogatov ◽  
Dmitry Korkin
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Protein Domain ◽  
Supplementary Information ◽  
Protein Protein Interactions ◽  
Domain Interaction ◽  
Statistical Potential ◽  
Knowledge Based ◽  
Scop Family

Abstract Motivation The complexity of protein–protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, determining which domains from each protein mediate the corresponding PPI is a challenging task. Results Here, we present domain interaction statistical potential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their structural classification of protein (SCOP) family annotations. The statistical potential is derived based on the analysis of >352 000 structurally resolved PPIs obtained from DOMMINO, a comprehensive database of structurally resolved macromolecular interactions. Availability and implementation DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: https://github.com/korkinlab/dispot and standalone docker images on DockerHub: https://hub.docker.com/r/korkinlab/dispot. The web server is freely available at http://dispot.korkinlab.org/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DISPOT: A simple knowledge-based protein domain interaction statistical potential

2019 ◽  
Author(s):  
Oleksandr Narykov ◽  
Dmitry Korkin
Keyword(s):  
Protein Interactions ◽  
Source Code ◽  
Experimental Studies ◽  
Protein Domain ◽  
Protein Protein Interactions ◽  
Domain Interaction ◽  
Statistical Potential ◽  
Link Type ◽  
Knowledge Based ◽  
Scop Family

AbstractMotivationThe complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, finding which domains from each protein mediate the corresponding PPI is a challenging task.ResultsHere, we present Domain Interaction Statistical POTential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their SCOP family annotations. The statistical potential is derived based on the analysis of more than 352,000 structurally resolved protein-protein interactions obtained from DOMMINO, a comprehensive database on structurally resolved macromolecular interactionsAvailability and implementationDISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on Github: (https://github.com/KorkinLab/DISPOT) and standalone docker images on DockerHub: (https://cloud.docker.com/u/korkinlab/repository/docker/korkinlab/dispot).

scholarly journals Integrative Visual Analysis of the Effects of Alternative Splicing on Protein Domain Interaction Networks

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Dorothea Emig ◽  
Melissa S. Cline ◽  
Karsten Klein ◽  
Anne Kunert ◽  
Petra Mutzel ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Protein Interactions ◽  
Visual Analysis ◽  
Protein Isoforms ◽  
Interaction Networks ◽  
Protein Domain ◽  
Domain Interaction ◽  
Exon Arrays ◽  
Exon Expression ◽  
Splicing Patterns

SummaryProteins and their interactions are essential for the functioning of all organisms and for understanding biological processes. Alternative splicing is an important molecular mechanism for increasing the protein diversity in eukaryotic cells. Splicing events that alter the protein structure and the domain composition can be responsible for the regulation of protein interactions and the functional diversity of different tissues. Discovering the occurrence of splicing events and studying protein isoforms have become feasible using Affymetrix Exon Arrays. Therefore, we have developed the versatile Cytoscape plugin DomainGraph that allows for the visual analysis of protein domain interaction networks and their integration with exon expression data. Protein domains affected by alternative splicing are highlighted and splicing patterns can be compared.

scholarly journals The functional impact of alternative splicing in cancer

2016 ◽  
Author(s):  
Héctor Climente-González ◽  
Eduard Porta-Pardo ◽  
Adam Godzik ◽  
Eduardo Eyras
Keyword(s):  
Alternative Splicing ◽  
Negative Correlation ◽  
Protein Interactions ◽  
Somatic Mutations ◽  
Protein Domain ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Functional Impact ◽  
Functional Consequences ◽  
Functional Transformations

SummaryAlternative splicing changes are frequently observed in cancer and are starting to be recognized as important signatures for tumor progression and therapy. However, their functional impact and relevance to tumorigenesis remains mostly unknown. We carried out a systematic analysis to characterize the potential functional consequences of alternative splicing changes in thousands of tumor samples. This analysis revealed that a subset of alternative splicing changes affect protein domain families that are frequently mutated in tumors and potentially disrupt protein protein interactions in cancer-related pathways. Moreover, there was a negative correlation between the number of these alternative splicing changes in a sample and the number of somatic mutations in drivers. We propose that a subset of the alternative splicing changes observed in tumors may represent independent oncogenic processes that could be relevant to explain the functional transformations in cancer and some of them could potentially be considered alternative splicing drivers (AS-drivers).

scholarly journals HMNPPID—human malignant neoplasm protein–protein interaction database

2019 ◽  
Vol 13 (S1) ◽  
Author(s):  
Qingqing Li ◽  
Zhihao Yang ◽  
Zhehuan Zhao ◽  
Ling Luo ◽  
Zhiheng Li ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Malignant Neoplasm ◽  
Biomedical Literature ◽  
Malignant Neoplasms ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Database ◽  
Protein Interaction Database

Abstract Background Protein–protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available. Results In this work, a database of protein–protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm. Conclusions HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

scholarly journals Inferring domain-domain interactions from protein-protein interactions in the complex network conformation

2012 ◽  
Vol 6 (Suppl 1) ◽  
pp. S7 ◽  
Author(s):  
Chen Chen ◽  
Jun-Fei Zhao ◽  
Qiang Huang ◽  
Rui-Sheng Wang ◽  
Xiang-Sun Zhang
Keyword(s):  
Complex Network ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Domain Interactions

scholarly journals Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Mayumi Kamada ◽  
Yusuke Sakuma ◽  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interactions ◽  
Interaction Strength ◽  
Feature Space ◽  
Relevance Vector Machine ◽  
Protein Domain ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Living Organisms ◽  
Domain Information

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

scholarly journals Subcellular micropatterning for visual immunoprecipitation reveals differences in cytosolic protein complexes downstream the EGFR

2021 ◽  
Author(s):  
Roland Hager ◽  
Ulrike Mueller ◽  
Nicole Ollinger ◽  
Julian Weghuber ◽  
Peter Lanzerstorfer
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Growth Factor Receptor ◽  
Spatial Arrangement ◽  
Spatiotemporal Analysis ◽  
Protein Domain ◽  
Protein Protein Interactions ◽  
Cytosolic Protein ◽  
Bait Construct ◽  
Biochemical Analyses

Analysis of protein-protein interactions in living cells by protein micropatterning is currently limited to the spatial arrangement of transmembrane proteins and their corresponding downstream molecules. Here we present a robust method for visual immunoprecipitation of cytosolic protein complexes by use of an artificial transmembrane bait construct in combination with micropatterned antibody arrays on cyclic olefin polymer (COP) substrates. The method was used to characterize Grb2-mediated signalling pathways downstream the epidermal growth factor receptor (EGFR). Ternary protein complexes (Shc1:Grb2:SOS1 and Grb2:Gab1:PI3K) were identified and we found that EGFR downstream signalling is based on constitutively bound (Grb2:SOS1 and Grb2:Gab1) as well as on agonist-dependent protein associations with transient interaction properties (Grb2:Shc1 and Grb2:PI3K). Spatiotemporal analysis further revealed significant differences in stability and exchange kinetics of protein interactions. Furthermore, we could show that this approach is well suited to study the efficacy and specificity of SH2 and SH3 protein domain inhibitors in a live cell context. Altogether, this method represents a significant enhancement of quantitative subcellular micropatterning approaches as an alternative to standard biochemical analyses.

Domain-Based Approaches to Prediction and Analysis of Protein-Protein Interactions

2014 ◽  
Vol 4 (1) ◽  
pp. 24-41 ◽  
Author(s):  
Morihiro Hayashida ◽  
Tatsuya Akutsu
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Domains ◽  
Cellular Systems ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Scale Free ◽  
Free Distribution ◽  
Protein Protein Interaction ◽  
Domain Interactions

Protein-protein interactions play various essential roles in cellular systems. Many methods have been developed for inference of protein-protein interactions from protein sequence data. In this paper, the authors focus on methods based on domain-domain interactions, where a domain is defined as a region within a protein that either performs a specific function or constitutes a stable structural unit. In these methods, the probabilities of domain-domain interactions are inferred from known protein-protein interaction data and protein domain data, and then prediction of interactions is performed based on these probabilities and contents of domains of given proteins. This paper overviews several fundamental methods, which include association method, expectation maximization-based method, support vector machine-based method, linear programming-based method, and conditional random field-based method. This paper also reviews a simple evolutionary model of protein domains, which yields a scale-free distribution of protein domains. By combining with a domain-based protein interaction model, a scale-free distribution of protein-protein interaction networks is also derived.

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