A survey on Protein Protein Interactions (PPI) methods, databases, challenges and future directions

Protein Proteins Interactions (PPIs) is a process of interacting protein with proteins in order to produce some organic procedures. To make better understanding of recognizing protein work it is necessary to create high throughput strategies for distinguishing PPI. Protein protein interaction is used for different cells also it defines the 3D structure of proteins. It facilitates different cell capacities resulting from 3D structure of cell that is three dimensional structures of protein protein complexes interactions and it binds affinity information together. The purpose of PPIs is to make interactions between bacterial, viral, and parasitic pathogens of human host's harbors which have great medicinal making potential in order to reduce the causes of dieses that occur due to the PPIs in humans. PPIs are used to discover target specific disease with related interfaces that underlines human interaction network. The structure of ligand binding proteins has to face several challenges including protein sampling of the huge possible orientations for ligand, the protein pocket, sequence space of large data and estimating the binding free energies accurately during the design process. Computational methods are used for successful ligand binding protein design their pros and cons and the potential future directions of the field are discussed. In this paper we have analyzed different methods and techniques of PPIs identification, management, interactions and bindings also to gather different analysis and results based on big databases. In future, we will use Spark's distributed machine Learning library for PPIs prediction, data modeling and machine learning.