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 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 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.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

2019 ◽  
Vol 26 (21) ◽  
pp. 3890-3910 ◽  
Author(s):  
Branislava Gemovic ◽  
Neven Sumonja ◽  
Radoslav Davidovic ◽  
Vladimir Perovic ◽  
Nevena Veljkovic
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Experimental Studies ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Prediction Tools ◽  
Physiological Processes ◽  
Modern Drug ◽  
Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

2019 ◽  
Vol 19 (4) ◽  
pp. 232-241 ◽  
Author(s):  
Xuegong Chen ◽  
Wanwan Shi ◽  
Lei Deng
Keyword(s):  
Protein Interactions ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Computational Method ◽  
Biological Information ◽  
Support Vector ◽  
Protein Protein Interactions ◽  
Efficient Treatment ◽  
Disease Associations ◽  
Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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 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.

scholarly journals Computational and experimental studies of plasmodium falciparum protein PfAMA1 domain-II loop dynamics: implications in PfAMA1-PfRON2 binding event

2021 ◽  
Author(s):  
Suman Sinha ◽  
Anamika Biswas ◽  
Jagannath Mondal ◽  
Kalyaneswar Mandal
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Experimental Studies ◽  
Alpha Helix ◽  
Dynamics Simulation ◽  
Malarial Parasite ◽  
Titration Calorimetry ◽  
Protein Protein Interactions ◽  
Human Red Blood Cells ◽  
Developed Nations

Protein-protein interactions are interesting targets for various drug discovery campaigns. One such promising and therapeutically pertinent protein-protein complex is PfAMA1-PfRON2, which is involved in malarial parasite invasion into human red blood cells. A thorough understanding of the interactions between these macromolecular binding partners is absolutely necessary to design better therapeutics to fight against the age-old disease affecting mostly under-developed nations. Although crystal structures of several PfAMA1-PfRON2 complexes have been solved to understand the molecular interactions between these two proteins, the mechanistic aspects of the domain II loop-PfRON2 association is far from clear. The current work investigates a crucial part of the recognition event; i.e., how the domain II loop of PfAMA1 exerts its effect on the alpha helix of the PfRON2, thus influencing the overall kinetics of this intricate recognition phenomenon. To this end, we have conducted thorough computational investigation of the dynamics and free energetics of domain II loop closing processes using molecular dynamics simulation. The computational results are validated by systematic alanine substitutions of the PfRON2 peptide helix. The subsequent evaluation of the binding affinity of Ala-substituted PfRON2 peptide ligands by surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) provides a rank of the relative importance of the residues in context. Our combined (computational and experimental) investigation has revealed that the domain II loop of PfAMA1 is in fact responsible for arresting the PfRON2 molecule from egress, K2027 and D2028 of PfRON2 being the determinant residues for the capturing event. Our study provides a comprehensive understanding of the molecular recognition event between PfAMA1 and PfRON2, specifically in the post binding stage, which potentially can be utilized for drug discovery against malaria.

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