scholarly journals Binary Interactome Models of Inner- Versus Outer-Complexome Organization

2021 ◽  
Author(s):  
Luke Lambourne ◽  
Anupama Yadav ◽  
Yang Wang ◽  
Alice Desbuleux ◽  
Dae-Kyum Kim ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Small Proportion ◽  
Cellular Systems ◽  
Functional Similarity ◽  
Functional Heterogeneity ◽  
Protein Protein Interactions ◽  
Functional Plasticity ◽  
Similarity Network ◽  
Transient Interactions ◽  
Similarity Relationships

The interactome is often conceived of as a collection of hundreds of multimeric machines, collectively the "complexome". However, the large proportion of the proteome that exists outside of the complexome, (the "outer-complexome") is expected to account for most of the functional plasticity exhibited by cellular systems. To compare features of inner- versus outer-complexome organization, we generated an all-by-all yeast systematic binary interactome map, integrated it with previous binary maps, and compared the resulting binary interactome "atlas" with systematic co-complex association and functional similarity network maps. Direct binary protein-protein interactions in the inner-complexome tend to be readily detected in different assays and exhibit high levels of coherence with functional similarity relationships. In contrast, pairs of proteins connected by relatively transient, harder to detect binary interactions in the outer-complexome appear to exhibit higher levels of functional heterogeneity. Thus a small proportion of the interactome corresponds to a stable, functionally homogeneous inner-complexome, while a much greater proportion consists of mostly transient interactions between pairs of functionally heterogeneous proteins in the outer-complexome.

scholarly journals GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Xiaoshi Zhong ◽  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Information Content ◽  
Semantic Similarity ◽  
Protein Interactions ◽  
Large Scale ◽  
Functional Similarity ◽  
Experimental Results ◽  
Graph Embeddings ◽  
Protein Protein Interactions ◽  
Vector Representations ◽  
Go Terms

Abstract Background Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content to estimate the semantic similarity between GO terms; recently some research exploited word embeddings to learn vector representations for GO terms from a large-scale corpus. In this paper, we proposed a novel method, named GO2Vec, that exploits graph embeddings to learn vector representations for GO terms from GO graph. GO2Vec combines the information from both GO graph and GO annotations, and its learned vectors can be applied to a variety of bioinformatics applications, such as calculating functional similarity between proteins and predicting protein-protein interactions. Results We conducted two kinds of experiments to evaluate the quality of GO2Vec: (1) functional similarity between proteins on the Collaborative Evaluation of GO-based Semantic Similarity Measures (CESSM) dataset and (2) prediction of protein-protein interactions on the Yeast and Human datasets from the STRING database. Experimental results demonstrate the effectiveness of GO2Vec over the information content-based measures and the word embedding-based measures. Conclusion Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GO and GOA graphs. Our results also demonstrate that GO annotations provide useful information for computing the similarity between GO terms and between proteins.

scholarly journals Colloidal stability of the living cell

2020 ◽  
Vol 117 (19) ◽  
pp. 10113-10121 ◽  
Author(s):  
Håkan Wennerström ◽  
Eloy Vallina Estrada ◽  
Jens Danielsson ◽  
Mikael Oliveberg
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Colloidal Stability ◽  
Surface Heterogeneity ◽  
Specific Protein ◽  
Cellular Systems ◽  
Dispersion Force ◽  
Protein Protein Interactions ◽  
Protein Surfaces ◽  
Dense Crowd

Cellular function is generally depicted at the level of functional pathways and detailed structural mechanisms, based on the identification of specific protein–protein interactions. For an individual protein searching for its partner, however, the perspective is quite different: The functional task is challenged by a dense crowd of nonpartners obstructing the way. Adding to the challenge, there is little information about how to navigate the search, since the encountered surrounding is composed of protein surfaces that are predominantly “nonconserved” or, at least, highly variable across organisms. In this study, we demonstrate from a colloidal standpoint that such a blindfolded intracellular search is indeed favored and has more fundamental impact on the cellular organization than previously anticipated. Basically, the unique polyion composition of cellular systems renders the electrostatic interactions different from those in physiological buffer, leading to a situation where the protein net-charge density balances the attractive dispersion force and surface heterogeneity at close range. Inspection of naturally occurring proteomes and in-cell NMR data show further that the “nonconserved” protein surfaces are by no means passive but chemically biased to varying degree of net-negative repulsion across organisms. Finally, this electrostatic control explains how protein crowding is spontaneously maintained at a constant level through the intracellular osmotic pressure and leads to the prediction that the “extreme” in halophilic adaptation is not the ionic-liquid conditions per se but the evolutionary barrier of crossing its physicochemical boundaries.

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.

Evolutionary selection for protein aggregation

2012 ◽  
Vol 40 (5) ◽  
pp. 1032-1037 ◽  
Author(s):  
Natalia Sanchez de Groot ◽  
Marc Torrent ◽  
Anna Villar-Piqué ◽  
Benjamin Lang ◽  
Salvador Ventura ◽  
...  
Keyword(s):  
Protein Aggregation ◽  
Protein Interactions ◽  
Current Knowledge ◽  
Protein Aggregates ◽  
Cellular Systems ◽  
Aggregation Process ◽  
Loss Of Function ◽  
Protein Protein Interactions ◽  
Wide Range ◽  
Range Of Functions

Protein aggregation is being found to be associated with an increasing number of human diseases. Aggregation can lead to a loss of function (lack of active protein) or to a toxic gain of function (cytotoxicity associated with protein aggregates). Although potentially harmful, protein sequences predisposed to aggregation seem to be ubiquitous in all kingdoms of life, which suggests an evolutionary advantage to having such segments in polypeptide sequences. In fact, aggregation-prone segments are essential for protein folding and for mediating certain protein–protein interactions. Moreover, cells use protein aggregates for a wide range of functions. Against this background, life has adapted to tolerate the presence of potentially dangerous aggregation-prone sequences by constraining and counteracting the aggregation process. In the present review, we summarize the current knowledge of the advantages associated with aggregation-prone stretches in proteomes and the strategies that cellular systems have developed to control the aggregation process.

scholarly journals Combining Semantic Similarity and GO Enrichment for Computation of Functional Similarity

2017 ◽  
Author(s):  
Wenting Liu ◽  
Jianjun Liu ◽  
Jagath C. Rajapakse
Keyword(s):  
Semantic Similarity ◽  
Protein Interactions ◽  
Similarity Measures ◽  
Functional Similarity ◽  
Protein Protein Interactions ◽  
Go Enrichment ◽  
Sequence Homologies ◽  
Benchmark Datasets ◽  
Novel Method ◽  
Go Terms

AbstractFunctional similarity between genes is widely used in many bioinformatics applications including detecting molecular pathways, finding co-expressed genes, predicting protein-protein interactions, and prioritization of candidate genes. Methods evaluating functional similarity of genes are mostly based on semantic similarity of gene ontology (GO) terms. Though there are hundreds of functional similarity measures available in the literature, none of them considers the enrichment of the GO terms by the querying gene pair. We propose a novel method to incorporate GO enrichment into the existing functional similarity measures. Our experiments show that the inclusion of gene enrichment significantly improves the performance of 44 widely used functional similarity measures, especially in the prediction of sequence homologies, gene expression correlations, and protein-protein interactions.Software availabilityThe software (python code) and all the benchmark datasets evaluation (R script) are available at https://gitlab.com/liuwt/EnrichFunSim.

Computational Methods for Prediction of Protein-Protein Interactions

2017 ◽  
pp. 309-340
Author(s):  
Sneha Rai ◽  
Sonika Bhatnagar
Keyword(s):  
Network Topology ◽  
Protein Interactions ◽  
Biological Significance ◽  
Experimental Methods ◽  
Ease Of Use ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Large Numbers ◽  
Transient Interactions ◽  
Ppi Prediction

The key signaling pathways in cellular processes involve protein-protein interactions (PPIs). A perturbation in the balance of PPIs occurs in various pathophysiological processes. There are a large numbers of experimental methods for detection of PPIs. However, experimental PPI determination is time consuming, expensive, error prone and does not effectively cover transient interactions. Therefore, overlaying and integration of predictive methods with experimental results provides statistical robustness and biological significance to the PPI data. In this chapter, the authors describe PPIs in terms of types, importance, and biological consequences. This chapter also provides a comprehensive description on various computational approaches for PPI prediction. Prediction of PPI can be done through: 1) Genomic information based methods 2) Structure based methods 3) Network topology based methods: 4) Literature and data mining based methods 5) Machine learning methods. For ease of use and convenience, a summary of various databases and software for PPI prediction has been provided.

scholarly journals In Vivo Methods to Study Protein–Protein Interactions as Key Players in Mycobacterium Tuberculosis Virulence

Pathogens ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
Veyron-Churlet ◽  
Locht
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Unknown Protein ◽  
Protein Functions ◽  
Transient Interactions ◽  
Protein Complementation

Studies on protein–protein interactions (PPI) can be helpful for the annotation of unknown protein functions and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher tuberculosis (TB) pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

scholarly journals Large-scale prediction of key dynamic interacting proteins in multiple cancers

2020 ◽  
Author(s):  
Jifeng Zhang ◽  
Xiao Wang ◽  
Zhicheng Ji ◽  
Weidong Tian
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
General Characteristic ◽  
Interacting Proteins ◽  
Cancer Genes ◽  
Protein Protein Interactions ◽  
Dynamic Interactions ◽  
Network Characteristics ◽  
Edge Betweenness ◽  
Transient Interactions

AbstractTracking cancer dynamic protein-protein interactions(PPIs) and deciphering their pathogenesis remain a challenge. Here, we presented a dynamic PPIs’ hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted more than 1,400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. Two prominent functional characteristics, “Cell cycle-related” and “Immune-related”, were presented, suggesting that it might be a general characteristic of KCG. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.

scholarly journals In Vivo Methods to Study Protein-Protein Interactions as Key Players in Mycobacterium tuberculosis Virulence

2019 ◽  
Author(s):  
Romain Veyron-Churlet ◽  
Camille Locht
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Protein Interactions ◽  
Protein Function ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Unknown Protein ◽  
Transient Interactions ◽  
Protein Complementation

Studies on Protein-Protein interactions (PPI) can be helpful for the annotation of unknown protein function and for the understanding of cellular processes, such as specific virulence mechanisms developed by bacterial pathogens. In that context, several methods have been extensively used in recent years for the characterization of Mycobacterium tuberculosis PPI to further decipher TB pathogenesis. This review aims at compiling the most striking results based on in vivo methods (yeast and bacterial two-hybrid systems, protein complementation assays) for the specific study of PPI in mycobacteria. Moreover, newly developed methods, such as in-cell native mass resonance and proximity-dependent biotinylation identification, will have a deep impact on future mycobacterial research, as they are able to perform dynamic (transient interactions) and integrative (multiprotein complexes) analyses.

Computational Methods for Prediction of Protein-Protein Interactions

2016 ◽  
pp. 184-215
Author(s):  
Sneha Rai ◽  
Sonika Bhatnagar
Keyword(s):  
Network Topology ◽  
Protein Interactions ◽  
Biological Significance ◽  
Experimental Methods ◽  
Ease Of Use ◽  
Protein Protein Interactions ◽  
Cellular Processes ◽  
Large Numbers ◽  
Transient Interactions ◽  
Ppi Prediction

The key signaling pathways in cellular processes involve protein-protein interactions (PPIs). A perturbation in the balance of PPIs occurs in various pathophysiological processes. There are a large numbers of experimental methods for detection of PPIs. However, experimental PPI determination is time consuming, expensive, error prone and does not effectively cover transient interactions. Therefore, overlaying and integration of predictive methods with experimental results provides statistical robustness and biological significance to the PPI data. In this chapter, the authors describe PPIs in terms of types, importance, and biological consequences. This chapter also provides a comprehensive description on various computational approaches for PPI prediction. Prediction of PPI can be done through: 1) Genomic information based methods 2) Structure based methods 3) Network topology based methods: 4) Literature and data mining based methods 5) Machine learning methods. For ease of use and convenience, a summary of various databases and software for PPI prediction has been provided.

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