scholarly journals Applications of Bioinformatics and Experimental Methods to Intrinsic Disorder-Based Protein-Protein Interactions

10.5772/29246 ◽  
2012 ◽  
Author(s):  
Xiaolin Sun ◽  
William T. ◽  
Vladimir N.
Keyword(s):  
Protein Interactions ◽  
Intrinsic Disorder ◽  
Experimental Methods ◽  
Protein Protein Interactions

scholarly journals Intrinsic Disorder in Tetratricopeptide Repeat Proteins

2020 ◽  
Vol 21 (10) ◽  
pp. 3709 ◽  
Author(s):  
Nathan W. Van Bibber ◽  
Cornelia Haerle ◽  
Roy Khalife ◽  
Bin Xue ◽  
Vladimir N. Uversky
Keyword(s):  
Protein Interactions ◽  
Posttranslational Modifications ◽  
Tandem Repeats ◽  
Intrinsically Disordered Protein ◽  
Intrinsic Disorder ◽  
Protein Protein Interactions ◽  
Systematic Analysis ◽  
Tetratricopeptide Repeats ◽  
Intrinsically Disordered ◽  
Tetratricopeptide Repeat Proteins

Among the realm of repeat containing proteins that commonly serve as “scaffolds” promoting protein-protein interactions, there is a family of proteins containing between 2 and 20 tetratricopeptide repeats (TPRs), which are functional motifs consisting of 34 amino acids. The most distinguishing feature of TPR domains is their ability to stack continuously one upon the other, with these stacked repeats being able to affect interaction with binding partners either sequentially or in combination. It is known that many repeat-containing proteins are characterized by high levels of intrinsic disorder, and that many protein tandem repeats can be intrinsically disordered. Furthermore, it seems that TPR-containing proteins share many characteristics with hybrid proteins containing ordered domains and intrinsically disordered protein regions. However, there has not been a systematic analysis of the intrinsic disorder status of TPR proteins. To fill this gap, we analyzed 166 human TPR proteins to determine the degree to which proteins containing TPR motifs are affected by intrinsic disorder. Our analysis revealed that these proteins are characterized by different levels of intrinsic disorder and contain functional disordered regions that are utilized for protein-protein interactions and often serve as targets of various posttranslational modifications.

DISCOVERING PROTEIN–PROTEIN INTERACTIONS

2004 ◽  
Vol 01 (04) ◽  
pp. 711-741 ◽  
Author(s):  
SEE-KIONG NG ◽  
SOON-HENG TAN
Keyword(s):  
Protein Interactions ◽  
Experimental Methods ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Comprehensive Description ◽  
New Genes ◽  
Genomics And Proteomics ◽  
Living Organisms

The ongoing genomics and proteomics efforts have helped identify many new genes and proteins in living organisms. However, simply knowing the existence of genes and proteins does not tell us much about the biological processes in which they participate. Many major biological processes are controlled by protein interaction networks. A comprehensive description of protein–protein interactions is therefore necessary to understand the genetic program of life. In this tutorial, we provide an overview of the various current high-throughput methods for discovering protein–protein interactions, covering both the conventional experimental methods and new computational approaches.

scholarly journals Interactome Data and Databases: Different Types of Protein Interaction

2004 ◽  
Vol 5 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Javier De Las Rivas ◽  
Alberto de Luis
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Interaction Network ◽  
Short Review ◽  
Experimental Methods ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Bioinformatic Tool ◽  
Different Types

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

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.

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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