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Current Medicinal Chemistry


ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Author(s): Branislava Gemovic, Neven Sumonja, Radoslav Davidovic, Vladimir Perovic and Nevena Veljkovic*

Volume 26 , Issue 21 , 2019

Page: [3890 - 3910] Pages: 21

DOI: 10.2174/0929867325666180214113704

Price: $65


Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

Keywords: Protein-protein interactions, computational predictions, predictive performance, high-throughput experimental techniques, interactome datasets, databases.

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