Applications of in Silico Methods for Design and Development of Drugs Targeting Protein-Protein Interactions

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Author(s): Vittoria Cicaloni, Alfonso Trezza, Francesco Pettini, Ottavia Spiga*.

Journal Name: Current Topics in Medicinal Chemistry

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Background: Identification of protein-protein interactions (PPIs) is a major challenge in modern molecular biology and biochemistry research, due to the unquestionable role of proteins in cells, biological process and pathological states. Over the past decade, the PPIs have evolved from being considered a highly challenging field of research to being investigated and examined as targets for pharmacological intervention.

Objective: Comprehension of protein interactions is crucial to known how proteins come together to build signalling pathways, to carry out their functions, or to cause diseases, when deregulated. Multiplicity and great amount of PPIs structures offer a huge number of new and potential targets for the treatment of different diseases.

Method: Computational techniques are becoming predominant in PPIs studies for their effectiveness, flexibility, accuracy and cost. As a matter of fact, there are effective in silico approaches which are able to identify PPIs and PPI site. Such methods for computational target prediction have been developed through molecular descriptors and data-mining procedures.

Results: In this review, we present different types of interactions between protein-protein and the application of in silico methods for design and development of drugs targeting PPIs. We described computational approaches for the identification of possible targets on protein surface and to detect of stimulator/ inhibitor molecules.

Conclusion: A deeper study of the most recent bioinformatics methodologies for PPIs studies is vital for a better understanding of protein complexes and for discover new potential PPI modulators in therapeutic intervention.

Keywords: Protein-protein interaction, Molecular dynamics simulation, Drug repositioning, Protein docking, Interaction network, Structural bioinformatics, Computational screening

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(E-pub Ahead of Print)
DOI: 10.2174/1568026619666190304153901
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