Computational Approaches for Predicting Protein–Protein Interactions: A Survey

2006 ◽  
Vol 30 (1) ◽  
pp. 39-44 ◽  
Author(s):  
Jingkai Yu ◽  
Farshad Fotouhi
2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri

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.


2020 ◽  
Vol 21 (2) ◽  
pp. 179-192
Author(s):  
Baichun Hu ◽  
Xiaoming Zheng ◽  
Ying Wang ◽  
Jian Wang ◽  
Fengjiao Zhang

Background: The lipid bilayer of the plasma membrane is impermeable to ions, yet changes in the flux of ions across the cell membrane are critical regulatory events in cells. Because of their regulatory roles in a range of physiological processes, such as electrical signaling in muscles and neurons, to name a few, these proteins are one of the most important drug targets. Objective: This review mainly focused on the computational approaches for elucidating proteinprotein interactions in cation channel signaling. Discussion: Due to continuously advanced facilities and technologies in computer sciences, the physical contacts of macromolecules of channel structures have been virtually visualized. Indeed, techniques like protein-protein docking, homology modeling, and molecular dynamics simulation are valuable tools for predicting the protein complex and refining channels with unreleased structures. Undoubtedly, these approaches will greatly expand the cation channel signaling research, thereby speeding up structure-based drug design and discovery. Conclusion: We introduced a series of valuable computational tools for elucidating protein-protein interactions in cation channel signaling, including molecular graphics, protein-protein docking, homology modeling, and molecular dynamics simulation.


2011 ◽  
Vol 108 (24) ◽  
pp. 9747-9752 ◽  
Author(s):  
C. J. Rogers ◽  
P. M. Clark ◽  
S. E. Tully ◽  
R. Abrol ◽  
K. C. Garcia ◽  
...  

2008 ◽  
Vol 36 (6) ◽  
pp. 1438-1441 ◽  
Author(s):  
Bostjan Kobe ◽  
Gregor Guncar ◽  
Rebecca Buchholz ◽  
Thomas Huber ◽  
Bohumil Maco ◽  
...  

Crystallography is commonly used for studying the structures of protein–protein complexes. However, a crystal structure does not define a unique protein–protein interface, and distinguishing a ‘biological interface’ from ‘crystal contacts’ is often not straightforward. A number of computational approaches exist for distinguishing them, but their error rate is high, emphasizing the need to obtain further data on the biological interface using complementary structural and functional approaches. In addition to reviewing the computational and experimental approaches for addressing this problem, we highlight two relevant examples. The first example from our laboratory involves the structure of acyl-CoA thioesterase 7, where each domain of this two-domain protein was crystallized separately, but both yielded a non-functional assembly. The structure of the full-length protein was uncovered using a combination of complementary approaches including chemical cross-linking, analytical ultracentrifugation and mutagenesis. The second example involves the platelet glycoprotein Ibα–thrombin complex. Two groups reported the crystal structures of this complex, but all the interacting interfaces differed between the two structures. Our computational analysis did not fully resolve the reasons for the discrepancies, but provided interesting insights into the system. This review highlights the need to complement crystallographic studies with complementary experimental and computational approaches.


2011 ◽  
Vol 33 (1) ◽  
pp. 8-11
Author(s):  
Hung Xuan Ta ◽  
Liisa Holm

A great number of cellular behaviours are mediated by proteins which always carry out their functions by interacting with each other. Unravelling protein–protein interactions (PPIs) is one of the central goals in proteomics, which will decipher the molecular mechanisms underlying the biological functions and thereby help to understand human diseases on a system-wide level. A number of experimental techniques, especially high-throughput approaches, have resulted in a large amount of PPI data that still suffer from incompleteness and contradiction. Moreover, these experimental techniques are expensive, time-consuming and labour-intensive. Computational methods have emerged as complementary tools to experimental approaches to discover PPIs. Promisingly, computational methods can guide, assess and validate experimental data and finally predict novel PPIs.


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