scholarly journals Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

BMC Genomics ◽  
2009 ◽  
Vol 10 (1) ◽  
pp. 288 ◽  
Author(s):  
Stefanie De Bodt ◽  
Sebastian Proost ◽  
Klaas Vandepoele ◽  
Pierre Rouzé ◽  
Yves Van de Peer
Keyword(s):  
Arabidopsis Thaliana ◽  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Protein Interactions

Gene Ontology-driven inference of protein–protein interactions using inducers

2011 ◽  
Vol 28 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Stefan R. Maetschke ◽  
Martin Simonsen ◽  
Melissa J. Davis ◽  
Mark A. Ragan
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Protein Protein Interactions

scholarly journals BBX32 Interacts with AGL24 Involved in Flowering Time Control in Chinese Cabbage (Brassica rapa L. ssp. pekinensis)

2018 ◽  
Vol 47 (1) ◽  
pp. 34-45
Author(s):  
Guan-Peng MA ◽  
Da-Qin ZHAO ◽  
Tian-Wen WANG ◽  
Lin-Bi ZHOU ◽  
Gui-Lian LI
Keyword(s):  
Arabidopsis Thaliana ◽  
Circadian Clock ◽  
Flowering Time ◽  
Brassica Rapa ◽  
Chinese Cabbage ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Time Control ◽  
Flowering Time Control ◽  
Molecular Regulatory Mechanisms

B-box (BBX) zinc finger proteins play critical roles in both vegetative and reproductive development in plants. Many BBX proteins have been identified in Arabidopsis thaliana as floral transition regulatory factors, such as CO, BBX7 (COL9), BBX19, and BBX32. BBX32 is involved in flowering time control through repression of COL3 in Arabidopsis thaliana, but it is still elusive that whether and how BBX32 directly interacts with flowering signal integrators of AGAMOUS-LIKE 24 (AGL24) and SUPPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1) in Chinese cabbage (Brassica rapa L. ssp. pekinensis) or other plants. In this study, B-box-32(BBX32), a transcription factor in this family with one B-box motif was cloned from B. rapa, acted as a circadian clock protein, showing expression changes during the circadian period. Additional experiments using GST pull-down and yeast two-hybrid assays indicated that BrBBX32 interacts with BrAGL24 and does not interact with BrSOC1, while BrAGL24 does interact with BrSOC1. To investigate the domains involved in these protein-protein interactions, we tested three regions of BrBBX32. Only the N-terminus interacted with BrAGL24, indicating that the B-box domain may be the key region for protein interaction. Based on these data, we propose that BrBBX32 may act in the circadian clock pathway and relate to the mechanism of flowering time regulation by binding to BrAGL24 through the B-box domain. This study will provide valuable information for unraveling the molecular regulatory mechanisms of BrBBX32 in flowering time of B. rapa.

DCL and Associated Proteins of Arabidopsis thaliana - An Interaction Study

2017 ◽  
Vol 61 ◽  
pp. 85-94
Author(s):  
Paushali Roy ◽  
Abhijit Datta
Keyword(s):  
Arabidopsis Thaliana ◽  
Protein Interactions ◽  
Stress Responses ◽  
Interaction Study ◽  
Protein Protein Interactions ◽  
Protein Expression Level ◽  
Double Stranded Rna ◽  
Rna Molecules ◽  
Associated Proteins

During RNA interference in plants, Dicer-like/DCL proteins process longer double-stranded RNA (dsRNA) precursors into small RNA molecules. In Arabidopsis thaliana there are four DCLs (DCL1, DCL2, DCL3, and DCL4) that interact with various associated proteins to carry out this processing. The lack of complete structural-functional information and characterization of DCLs and their associated proteins leads to this study where we have generated the structures by modelling, analysed the structures and studied the interactions of Arabidopsisthaliana DCLs with their associated proteins with the homology-derived models to screen the interacting residues. Structural analyses indicate existence of significant conserved domains that may play imperative roles during protein-protein interactions. The interaction study shows some key domain-domain (including multi-domains and inter-residue interactions) interfaces and specific residue biases (like arginine and leucine) that may help in augmenting the protein expression level during stress responses. Results point towards plausible stable associations to carry out RNA processing in a synchronised pattern by elucidating the structural properties and protein-protein interactions of DCLs that may hold significance for RNAi researchers.

scholarly journals A Novel Network-Based Algorithm for Predicting Protein-Protein Interactions Using Gene Ontology

2021 ◽  
Vol 12 ◽  
Author(s):  
Lun Hu ◽  
Xiaojuan Wang ◽  
Yu-An Huang ◽  
Pengwei Hu ◽  
Zhu-Hong You
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Structural Information ◽  
Computational Prediction ◽  
Biological Information ◽  
Main Role ◽  
Protein Protein Interactions ◽  
Chronic Infections ◽  
Prediction Algorithms ◽  
Ppi Networks

Proteins are one of most significant components in living organism, and their main role in cells is to undertake various physiological functions by interacting with each other. Thus, the prediction of protein-protein interactions (PPIs) is crucial for understanding the molecular basis of biological processes, such as chronic infections. Given the fact that laboratory-based experiments are normally time-consuming and labor-intensive, computational prediction algorithms have become popular at present. However, few of them could simultaneously consider both the structural information of PPI networks and the biological information of proteins for an improved accuracy. To do so, we assume that the prior information of functional modules is known in advance and then simulate the generative process of a PPI network associated with the biological information of proteins, i.e., Gene Ontology, by using an established Bayesian model. In order to indicate to what extent two proteins are likely to interact with each other, we propose a novel scoring function by combining the membership distributions of proteins with network paths. Experimental results show that our algorithm has a promising performance in terms of several independent metrics when compared with state-of-the-art prediction algorithms, and also reveal that the consideration of modularity in PPI networks provides us an alternative, yet much more flexible, way to accurately predict PPIs.

Gene multifunctionality scoring using gene ontology

2018 ◽  
Vol 16 (05) ◽  
pp. 1840018 ◽  
Author(s):  
Hisham Al-Mubaid
Keyword(s):  
Gene Ontology ◽  
Protein Interactions ◽  
Evaluation Criteria ◽  
Computational Method ◽  
Protein Protein Interactions ◽  
Validity And Reliability ◽  
Functional Annotations ◽  
Gene Sets ◽  
Human Genes ◽  
Disease Associations

Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene multifunctionality based on functional annotations of the target gene from the Gene Ontology. The method is based on identifying pairs of GO annotations that represent semantically different biological functions and any gene annotated with two annotations from one pair is considered multifunctional. The proposed method can be employed to identify multifunctional genes in the entire human genome using solely the GO annotations. We evaluated the proposed method in scoring multifunctionality of all human genes using four criteria: gene-disease associations; protein–protein interactions; gene studies with PubMed publications; and published known multifunctional gene sets. The evaluation results confirm the validity and reliability of the proposed method for identifying multifunctional human genes. The results across all four evaluation criteria were statistically significant in determining multifunctionality. For example, the method confirmed that multifunctional genes tend to be associated with diseases more than other genes, with significance [Formula: see text]. Moreover, consistent with all previous studies, proteins encoded by multifunctional genes, based on our method, are involved in protein–protein interactions significantly more ([Formula: see text]) than other proteins.

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