Abstract
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.
Highlights
The links between proteins in a protein set can make a proteinprotein interaction network (PIN) and all proteins that are involved in PINs are spatially or temporally engaged to interact with other proteins within the process as well as functioning as indirect interacting members of the same pathway [1]
The discovery of protein connections has been assisted by developments in both biochemical and computational methods, which have produced precious awareness into the fundamental building of protein interactions in cellular networks [2]
The computational approaches may be utilised for comprehensive examination or perform a wide scale analysis across large datasets (Figure 1)
Summary
The links between proteins in a protein set can make a proteinprotein interaction network (PIN) and all proteins that are involved in PINs are spatially or temporally engaged to interact with other proteins within the process as well as functioning as indirect interacting members of the same pathway [1]. The computational approaches may be utilised for comprehensive examination or perform a wide scale analysis across large datasets (Figure 1) This approach signifies the multifaceted association of proteins with PPI links in a protein interaction network and would help to comprehend how signalling pathways linked with a disease are connected [5]. The approaches using protein sequence and genomic data contain a study of the absence or presence of genes in associated species, gene fusion events, preservation of gene neighborhood, interconnected mutations on surfaces of protein, the resemblance of phylogenetic trees, co-occurrence of sequence domains, functional and co-expression features [17] Sometimes, integration of these features is used to predict new interactions or to approximate the validity of PPIs, which are evaluated experimentally [18]. All interaction data are freely provided via search index and available by downloading in a wide variety of standardized format
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