Rediscovering and repurposing natural microbial macromolecules through computational approaches

• Main Authors: Abdella, M. *, Abdella, B. *, Lahiri, Chandrajit *
• Other Authors: Surajit, D.
• Format: Book Section
• Published: Academic Press 2020
• Subjects: QH301 Biology
• Online Access: http://eprints.sunway.edu.my/1391/; http://doi.org/10.1016/B978-0-12-820084-1.00016-8
• Institution: Sunway University

Microorganisms are known for their superabundance in their secondary bioactive metabolites and macromolecules. Some of these microbial metabolites and macromolecules have been commercialized as drugs for ages because of their biological activities. Research work on microbial macromolecules is exceedingly demanding for discoveries of new molecules to cure the debilitating and emerging diseases. De novo discovery of new drug candidates usually takes a long time to get approval for human use, though sometimes the compound finally fails to get the approval. For the acceleration or the discovery processes, computational approaches are more convenient and considered as the best alternative to overcome the drawbacks of the traditional methods. Computational approaches might reduce the time and resources required and increase the potential for new discoveries in silico, prior to moving on preclinical or clinical trial stages. For instance, virtual screening, data mining computational chemistry, genetic association, and retrospective clinical analysis are different approaches of computational methods in repurposing and repositioning of a known drug. On another note, the structure of new microbial metabolites with potential activity and their biosynthetic gene clusters could be predicted using a variety of tools and databases. Using approaches of computational chemistry and biology has been found to be very crucial for rediscovery and repurposing of microbial macromolecules. This chapter aims at describing the advancement made in the application of computational approaches in rediscovering and repurposing with different strategies incorporating the available sources of knowledge.