A Bayesian approach for estimating protein–protein interactions by integrating structural and non-structural biological data

2017 ◽  
Vol 13 (12) ◽  
pp. 2592-2602
Author(s):  
Hafeez Ur Rehman ◽  
Inam Bari ◽  
Anwar Ali ◽  
Haroon Mahmood
Keyword(s):  
Bayesian Approach ◽  
Protein Interactions ◽  
Biological Data ◽  
Protein Protein Interactions ◽  
Regulatory Processes ◽  
Genome Wide

Accurate elucidation of genome wide protein–protein interactions is crucial for understanding the regulatory processes of the cell.

scholarly journals Roles of the 14-3-3 gene family in cotton flowering

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Na Sang ◽  
Hui Liu ◽  
Bin Ma ◽  
Xianzhong Huang ◽  
Lu Zhuo ◽  
...  
Keyword(s):  
Gene Family ◽  
Protein Interactions ◽  
Leucine Zipper ◽  
Expression Patterns ◽  
Bimolecular Fluorescence Complementation ◽  
Structural Motif ◽  
Virus Induced Gene Silencing ◽  
Protein Protein Interactions ◽  
Basic Leucine Zipper ◽  
Genome Wide

Abstract Background In plants, 14-3-3 proteins, also called GENERAL REGULATORY FACTORs (GRFs), encoded by a large multigene family, are involved in protein–protein interactions and play crucial roles in various physiological processes. No genome-wide analysis of the GRF gene family has been performed in cotton, and their functions in flowering are largely unknown. Results In this study, 17, 17, 31, and 17 GRF genes were identified in Gossypium herbaceum, G. arboreum, G. hirsutum, and G. raimondii, respectively, by genome-wide analyses and were designated as GheGRFs, GaGRFs, GhGRFs, and GrGRFs, respectively. A phylogenetic analysis revealed that these proteins were divided into ε and non-ε groups. Gene structural, motif composition, synteny, and duplicated gene analyses of the identified GRF genes provided insights into the evolution of this family in cotton. GhGRF genes exhibited diverse expression patterns in different tissues. Yeast two-hybrid and bimolecular fluorescence complementation assays showed that the GhGRFs interacted with the cotton FLOWERING LOCUS T homologue GhFT in the cytoplasm and nucleus, while they interacted with the basic leucine zipper transcription factor GhFD only in the nucleus. Virus-induced gene silencing in G. hirsutum and transgenic studies in Arabidopsis demonstrated that GhGRF3/6/9/15 repressed flowering and that GhGRF14 promoted flowering. Conclusions Here, 82 GRF genes were identified in cotton, and their gene and protein features, classification, evolution, and expression patterns were comprehensively and systematically investigated. The GhGRF3/6/9/15 interacted with GhFT and GhFD to form florigen activation complexs that inhibited flowering. However, GhGRF14 interacted with GhFT and GhFD to form florigen activation complex that promoted flowering. The results provide a foundation for further studies on the regulatory mechanisms of flowering.

scholarly journals Genome-wide analysis of vaccinia virus protein-protein interactions

2000 ◽  
Vol 97 (9) ◽  
pp. 4879-4884 ◽  
Author(s):  
S. McCraith ◽  
T. Holtzman ◽  
B. Moss ◽  
S. Fields
Keyword(s):  
Vaccinia Virus ◽  
Protein Interactions ◽  
Virus Protein ◽  
Protein Protein Interactions ◽  
Genome Wide Analysis ◽  
Genome Wide

scholarly journals Coiled-coil networking shapes cell molecular machinery

2012 ◽  
Vol 23 (19) ◽  
pp. 3911-3922 ◽  
Author(s):  
Yongqiang Wang ◽  
Xinlei Zhang ◽  
Hong Zhang ◽  
Yi Lu ◽  
Haolong Huang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Critical Role ◽  
Coiled Coil ◽  
Coiled Coils ◽  
Protein Protein Interactions ◽  
Full Spectrum ◽  
Kinetochore Assembly ◽  
Genome Wide ◽  
Molecular Machinery ◽  
Systematic Understanding

The highly abundant α-helical coiled-coil motif not only mediates crucial protein–protein interactions in the cell but is also an attractive scaffold in synthetic biology and material science and a potential target for disease intervention. Therefore a systematic understanding of the coiled-coil interactions (CCIs) at the organismal level would help unravel the full spectrum of the biological function of this interaction motif and facilitate its application in therapeutics. We report the first identified genome-wide CCI network in Saccharomyces cerevisiae, which consists of 3495 pair-wise interactions among 598 predicted coiled-coil regions. Computational analysis revealed that the CCI network is specifically and functionally organized and extensively involved in the organization of cell machinery. We further show that CCIs play a critical role in the assembly of the kinetochore, and disruption of the CCI network leads to defects in kinetochore assembly and cell division. The CCI network identified in this study is a valuable resource for systematic characterization of coiled coils in the shaping and regulation of a host of cellular machineries and provides a basis for the utilization of coiled coils as domain-based probes for network perturbation and pharmacological applications.

scholarly journals Prioritization of genes associated with the pathogenesis of leukosis in cattle

2019 ◽  
Vol 22 (8) ◽  
pp. 1063-1069 ◽  
Author(s):  
N. S. Yudin ◽  
N. L. Podkolodnyy ◽  
T. A. Agarkova ◽  
E. V. Ignatieva
Keyword(s):  
Protein Interactions ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Mammalian Species ◽  
Genome Wide Association ◽  
Farm Animals ◽  
Genome Wide Association Studies ◽  
Protein Protein Interactions ◽  
Genome Wide ◽  
A Genome

Selection by means of genetic markers is a promising approach to the eradication of infectious diseases in farm animals, especially in the absence of effective methods of treatment and prevention. Bovine leukemia virus (BLV) is spread throughout the world and represents one of the biggest problems for the livestock production and food security in Russia. However, recent genome-wide association studies have shown that sensitivity/resistance to BLV is polygenic. The aim of this study was to create a catalog of cattle genes and genes of other mammalian species involved in the pathogenesis of BLV-induced infection and to perform gene prioritization using bioinformatics methods. Based on manually collected information from a range of open sources, a total of 446 genes were included in the catalog of cattle genes and genes of other mammals involved in the pathogenesis of BLV-induced infection. The following criteria were used to prioritize 446 genes from the catalog: (1) the gene is associated with leukemia according to a genome-wide association study; (2) the gene is associated with leukemia according to a case-control study; (3) the role of the gene in leukemia development has been studied using knockout mice; (4) protein-protein interactions exist between the gene-encoded protein and either viral particles or individual viral proteins; (5) the gene is annotated with Gene Ontology terms that are overrepresented for a given list of genes; (6) the gene participates in biological pathways from the KEGG or REACTOME databases, which are over-represented for a given list of genes; (7) the protein encoded by the gene has a high number of protein-protein interactions with proteins encoded by other genes from the catalog. Based on each criterion, a rank was assigned to each gene. Then the ranks were summarized and an overall rank was determined. Prioritization of 446 candidate genes allowed us to identify 5 genes of interest (TNF,LTB,BOLA-DQA1,BOLA-DRB3,ATF2), which can affect the sensitivity/resistance of cattle to leukemia.

scholarly journals Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Qi Lv ◽  
Weimin Ma ◽  
Hui Liu ◽  
Jiang Li ◽  
Huan Wang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Protein Protein Interactions ◽  
Genome Wide

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

2019 ◽  
Vol 20 (3) ◽  
pp. 177-184 ◽  
Author(s):  
Nantao Zheng ◽  
Kairou Wang ◽  
Weihua Zhan ◽  
Lei Deng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Protein Interactions ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Protein Motifs

Background:Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.Methods:In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.Results:We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.Conclusion:The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

scholarly journals Chasing coevolutionary signals in intrinsically disordered proteins complexes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Javier A. Iserte ◽  
Tamas Lazar ◽  
Silvio C. E. Tosatto ◽  
Peter Tompa ◽  
Cristina Marino-Buslje
Keyword(s):  
Protein Interactions ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Protein Protein Interactions ◽  
Contact Prediction ◽  
Intrinsically Disordered ◽  
Regulatory Processes ◽  
Wide Range ◽  
Predict Protein Structure ◽  
Biological Interpretation

Abstract Intrinsically disordered proteins/regions (IDPs/IDRs) are crucial components of the cell, they are highly abundant and participate ubiquitously in a wide range of biological functions, such as regulatory processes and cell signaling. Many of their important functions rely on protein interactions, by which they trigger or modulate different pathways. Sequence covariation, a powerful tool for protein contact prediction, has been applied successfully to predict protein structure and to identify protein–protein interactions mostly of globular proteins. IDPs/IDRs also mediate a plethora of protein–protein interactions, highlighting the importance of addressing sequence covariation-based inter-protein contact prediction of this class of proteins. Despite their importance, a systematic approach to analyze the covariation phenomena of intrinsically disordered proteins and their complexes is still missing. Here we carry out a comprehensive critical assessment of coevolution-based contact prediction in IDP/IDR complexes and detail the challenges and possible limitations that emerge from their analysis. We found that the coevolutionary signal is faint in most of the complexes of disordered proteins but positively correlates with the interface size and binding affinity between partners. In addition, we discuss the state-of-art methodology by biological interpretation of the results, formulate evaluation guidelines and suggest future directions of development to the field.

scholarly journals GWIDD: a comprehensive resource for genome-wide structural modeling of protein-protein interactions

2012 ◽  
Vol 6 (1) ◽  
pp. 7 ◽  
Author(s):  
Petras J Kundrotas ◽  
Zhengwei Zhu ◽  
Ilya A Vakser
Keyword(s):  
Protein Interactions ◽  
Structural Modeling ◽  
Protein Protein Interactions ◽  
Genome Wide
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