Methods to investigate PPIâ€™s:
A wide variety of methods are present to investigate protein-protein interactions. These methods not only vary upon the proteins involved but also on the specificity. Based on the sensitivity, each method has its pros and cons. Yeast two-hybrid assay, affinity electrophoresis, light scattering, fluorescent spectroscopy, protein microarrays, analytical ultracentrifugation, affinity purification coupled with mass spectroscopy, are the examples of methods used to investigate different protein-protein interactions. Yeast two-hybrid assay and affinity purification coupled to mass spectrometry are the most prominent and commercially significant methods. These measure the entire interactomes as a whole.
Yeast two-hybrid assay obtained interactomes. Machine learning and computational methods such as genomic context methods and text mining methods also exist for the activity of investigation of PPI’s. The Rosetta Stone or Domain Fusion method, The Conserved Neighborhood method, and The phylogenetic Profile method are the genomic context methods. Text mining methods use publicly available information and convert it into binary relations. These binary inputs can be understood with machine learning approaches. Random Forest is the most effective of all machine learning methods.
The construction of more complex interactomes is possible due to the successful screening methods in detecting the PPIâ€™s. IntAct is a molecular interaction database, which has a lot of information on the protein-protein interactions. The available databases are applied to the protein-protein interaction prediction algorithms for further information. The biological databases containing information already obtained from the experimental analysis PPI’s are updated constantly. The integration of two or more primary databases leads to the establishment of meta-databases. The other type of databases includes prediction databases, which can predict several techniques or interactions.
PPI networks are extremely complicated and difficult to represent on paper. To simplify this task, the bioinformatics tool has been constructed. Cytoscape and Pajek software are examples of the said tools and make the work easy for researchers to visualize complex interaction networks. Specific to PPI networks, the functional module identification is of principal concern. In a protein-protein interaction, the area where the proteins are highly connected is termed as the functional nodule. MoBaS and Jactive modules help in identifying the functional modules. Signal network is the term given for PPI networks since their networks are indicated by either their repression or activation. The major limitation of the PPI networks that are existing today is that they are incomplete and very complicated. Due to their complexity, there are chances of false positives or false negatives to occur. Therefore, knowing the exact physical relationships that are present between proteins, is problematic.
The knowledge of protein-protein interaction networks is essential to control the occurrence of diseases in organisms, by knowing the cause of the occurrence, that is, their physiological mechanisms. Cancer, autoimmune disorders, among others, occur when the protein networks are disrupted. Hence, by understanding the PPI networks, a suitable therapy or respective treatment can be performed. PPI networks can also be applied to the development of new drugs.
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