Chapter 9 - Application of Computation in the Biosynthesis of Phytochemicals

Abstract

Secondary metabolites from plant origin are a useful source of diverse drug molecules, such as antibiotics, anticancer, immunomodulators, antimalarials, antihypertensives, lipid-lowering agents, and many more. These diverse chemical entities belong to different classes, namely alkaloids, iridoids, limonoids, peptides, phenolics, terpenoids, and others. Without altering the biodiversity, it is possible to improve the production of important plant metabolites through interacting with the subsequent biotechnological fields of genomics, metabolomics, and transcriptomics. A variety of computational strategies with different algorithms and designing software are utilized to reduce the time and cost-effective production of secondary plant metabolites. Biosynthetic gene clusters (BGCs) encode different metabolic pathways and help to enhance the production of different secondary metabolites of particular interest. Metabolomics and genomics data are compiled by using different computational tools or databases for identification, prediction, analysis, and biosynthesis process development of secondary metabolites. Commonly used tools or databases for identification and analysis are antiSMASH, SMURF, MultiGeneBlast, and ClustScan. The NRPQuset and RiPPQuest are useful tools to connect the gene clusters and mass spectrometric observations as a part of glycogenomics and peptidogenomics. Prediction of substrate specificity may be conducted for non-ribosomal peptides (NPRS) with the help of tools, such as NRPSPredictor and NRPS/PKS substrate predictor. Different gene cluster tools and databases (such as DoBISCUIT and ClusterMine360) as well as chemical library databases (namely ChEMBL, PubChem, ChemSpider, and Dictionary of Natural Products) are used for compiling these gene clusters with small molecular structures through networking approaches for predicting new molecules. Moreover, designing software, algorithms, and computational methodologies may also be effective in the identification of possible metabolic pathways and integrating this to transcription. These computational methodologies may be helpful in the de novo prediction of biosynthetic pathways to enhance the production of secondary metabolites. Therefore, computational methodologies may significantly contribute to the processing of phytochemicals in the field of synthetic biology.