Background:
To carry out wide range of cellular functionalities, proteins often associate with
one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and
computational approaches were applied on PPIs in order to determine the interacting partners, and also
to understand how an abnormality in such interactions can become the principle cause of a disease.
Objective:
This review aims to elucidate the case studies where PPIs involved in various human diseases
have been proven or validated with computational techniques, and also to elucidate how small molecule
inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against
certain diseases.
Results:
Computational techniques to predict PPIs are emerging rapidly in the modern day. They not
only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined
results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting
the PPIs. Some of them are already being tested in the clinical trials.
Conclusion:
This review delineated the classification of computational tools that are essential to investigate
PPIs. Furthermore, the review shed light on how indispensable computational tools have become
in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently
against dreadful diseases in a shorter time span.
IP-FCM Measures Physiologic Protein-Protein Interactions Modulated by Signal Transduction and Small-Molecule Drug Inhibition