Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

Yang, Lei; Tang, Xianglong

doi:10.1155/2014/523634

Cited by 9 publications

(12 citation statements)

References 14 publications

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“…Indeed, proteins are recruited to form a complex to perform a set of specific biological functions and loops may act as the basic unit to build more complex assemblies54. Additionally, a high degree of functional consensus may be exploited to predict biological processes of partially annotated protein complexes5657. More interestingly, loops of different lengths show a slightly different enrichment for some terms, but strong differences in functional annotation when compared with the remaining proteins in the network.…”

Section: Discussionmentioning

confidence: 99%

Bridging topological and functional information in protein interaction networks by short loops profiling

Chung

Pandini

Annibale

et al. 2015

Sci Rep

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Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.

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Section: Discussionmentioning

confidence: 99%

Bridging topological and functional information in protein interaction networks by short loops profiling

Chung

Pandini

Annibale

et al. 2015

Sci Rep

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“…We considered comprehensive hierarchical dictionary (ie, ontology) of the GO‐terms provided by the Gene Ontology Consortium . Many bioinformatics resources contain and use Gene Ontology Annotations (GOAs) terms in protein and gene function prediction, prediction and validation of protein‐protein interactions, gene expression, pathway regulatio,n and homology analysis . All GO‐terms (ie, annotations) are subdivided into three domains: molecular function, biological process, and cellular component.…”

Section: Introductionmentioning

confidence: 99%

“…of protein-protein interactions, 13,14 gene expression, 15 pathway regulatio,n 16 and homology analysis. 17 All GO-terms (ie, annotations) are subdivided into three domains: molecular function, biological process, and cellular component.…”

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confidence: 99%

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Gene ontology improves template selection in comparative protein docking

et al. 2018

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Structural characterization of protein‐protein interactions is essential for our ability to study life processes at the molecular level. Computational modeling of protein complexes (protein docking) is important as the source of their structure and as a way to understand the principles of protein interaction. Rapidly evolving comparative docking approaches utilize target/template similarity metrics, which are often based on the protein structure. Although the structural similarity, generally, yields good performance, other characteristics of the interacting proteins (eg, function, biological process, and localization) may improve the prediction quality, especially in the case of weak target/template structural similarity. For the ranking of a pool of models for each target, we tested scoring functions that quantify similarity of Gene Ontology (GO) terms assigned to target and template proteins in three ontology domains—biological process, molecular function, and cellular component (GO‐score). The scoring functions were tested in docking of bound, unbound, and modeled proteins. The results indicate that the combined structural and GO‐terms functions improve the scoring, especially in the twilight zone of structural similarity, typical for protein models of limited accuracy.

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“…Currently, a number of computational methods have been widely exploited for the prediction of PPIs. These computational methods [6] can be roughly divided into sequencebased [7][8][9], structure-based [10][11][12], and function annotation-based [13][14][15] methods. The advantage of sequencebased methods is not requiring expensive and time-consuming processes to determine protein structures.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

Zhang

et al. 2015

Mathematical Problems in Engineering

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Protein-protein interactions (PPIs) play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC) for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the 1 -regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM) and much higher than naive Bayes (NB), neural networks (NN), and k-nearest neighbor (KNN). The result shows that the MSRC is efficient for PPIs prediction.

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Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

Cited by 9 publications

References 14 publications

Bridging topological and functional information in protein interaction networks by short loops profiling

Bridging topological and functional information in protein interaction networks by short loops profiling

Gene ontology improves template selection in comparative protein docking

Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

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