Abstract
Plants are a rich source of chemical compounds which serve as food, colors, fragrances’, flavors, medicines, etc. Plant secondary metabolites are widely used in food technology, industry, and medicinal preparations and play a vital role in plant-environment interactions. These metabolites have unique characteristics which make them as important candidates for discovery of new drugs and “lead” molecules. So far the major lacuna in the area of plant metabolite research is the identification and characterization of the secondary metabolites and their biosynthetic mechanisms. With an upsurge in the demand for plant metabolites, the advanced “omics technologies” are most sought after for a faster research and better characterization of the natural products. With the advent of the advanced bioinformatics, genomics, and proteomics and the synergy between combinatorial chemistry and structure-based drug design, the process of characterizing secondary metabolites as lead molecules for drug design has been revolutionized. The scientific community is now witnessing a newer, faster, and sophisticated approach to drug discovery with the aid of in silico characterization methods. This chapter, thus, focuses on the general steps to be followed in the in silico characterization of plant secondary metabolites, starting from literature mining, virtual screening, structural characterization, ADMET screening, and structure-based drug designing.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agostini-Costa TS, Vieira RF, Bizzo HR, Silveira D, Gimenes MA (2012) Secondary metabolites. In: Dhanarasu DS (ed) Chromatography and its applications. In Tech, Brazil
Chen CY (2011) TCM Database@Taiwan: the world’s largest traditional Chinese medicine database for drug screening in silico, 2011. PLoS One 6:e15939. https://doi.org/10.1371/journal.pone.0015939
Cheng F, Li W, Liu G, Tang Y (2013) In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem 13(11):1273–1289., 1568-0266/1873-4294. https://doi.org/10.2174/15680266113139990033
Dar AM, Mir S (2017) Molecular docking: approaches, types, applications and basic challenges. J Anal Bioanal Tech. 2017 8:2. https://doi.org/10.4172/2155-9872.1000356
Feixiong C, Weihua L, Guixia L, Yun T (2013) In Silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem 13:1273–1289
Goossens A, Hakkinen ST, Laakso I, Seppanen-Laakso T, Biondi S, De Sutter V (2003) A functional genomics approach toward the understanding of secondary metabolism in plant cells. Proc Natl Acad Sci U S A 100:8595–8600
Haggarty SJ (2005) The principle of complementarity: chemical versus biological space. Curr Opin Chem Biol 9(3):296–303
Irwin JJ, Shoichet BK (2016) Docking screens for novel ligands conferring new biology. J Med Chem 59:4103–4120
Kabera JN, Seman1 E, Mussa AR, He X (2014) Plant secondary metabolites: biosynthesis, classification, function and pharmacological classification, function and pharmacological properties. J Pharm Pharmacol 2(7):377–392
Kar S, Roy K (2013) How far can virtual screening take us in drug discovery? Expert Opin Drug Discovery 8(3):245–261. https://doi.org/10.1517/17460441.2013.761204
Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25:197–206
Kogan SB, Kaliya M, Froumin N (2006) Liquid phase isomerization of isoprenol into prenol in hydrogen environment. Appl Catal A Gen 297(2):231–236
Kristensen TG, Nielsen J, Pedersen CNS (2013) Methods for similarity-based virtual screening. Comput Struct Biotechnol J 5:e201302009
Kruger DM, Evers A (2010) Comparison of structure- and ligand-based virtual screening protocols considering hit list complementarity and enrichment factors. ChemMedChem 5:148–158
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2012) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 64:4–17
Morrissey JP, Osbourn AE (1999) Fungal resistance to plant antibiotics as a mechanism of pathogenesis. Microbiol Mol Biol Rev 63:708–724
Newman DJ, Cragg GM (2012) Natural products as sources of new drugs over the 30 years from 1981 to 2010. J Nat Prod 75:311–335
Osbourn AE, lanzotti V (2009) Plant-derived natural products- synthesis, function and application. Springer, LLC, New York
Park ES, Moon WS, Song MJ, Kim MN, Chung KH, Yoon JS (2001) Antimicrobial activity of phenol and benzoic acid derivatives. Int Biodeterior Biodegrad 47(4):209–214
Pascolutti M, Quinn RJ (2014) Natural products as lead structures: chemical transformations to create lead-like libraries
Poulton JE (1990) Cyanogenesis in plants. Plant Physiol 94(2):401–405
Reymond J-L, Awale M (2012) Exploring chemical space for drug discovery using the chemical universe database. ACS Chem Neurosci 3(9):649–657
Ruby T, Rana CS (2015) Plant secondary metabolites: a review. Int J Eng Res Gen Sci 3(5):661–670
Samuelsson G, Bohlin L (2009) Drugs of natural origin, 6th edn. Apotekarsocieteten, Sweden
Schmidt TJ, Khalid SA, Romanha AJ, Alves TM, Biavatti MW, Brun R, Da Costa FB, de Castro SL, Ferreira VF, de Lacerda MV (2012) The potential of secondary metabolites from plants as drugs or leads against protozoan neglected diseases – part I. Curr Med Chem 19(14):2128–2175
Shoichet BK, McGovern SL, Wei B, Irwin JJ (2002) Lead discovery using molecular docking. Curr Opin Chem Biol 6:439–446
Sumner LW, Mendes P, Dixon RA (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62:817–836
Sweetlove LJ, Fell DA, Fernie AR (2008) Getting to grips with the plant metabolic network. Biochem J 409:27–41
Tian S, Wang J, Li Y, Li D, Xu L, Hou T (2015) The application of in silico drug-likeness predictions in pharmaceutical research. Adv Drug Deliv Rev 86:2–10
Wadood A, Ahmed N, Shah L, Ahmad A, Hassan H, Shams S (2013) In-silico drug design: an approach which revolutionarised the drug discovery process. OA Drug Des Deliv. 2013 Sep 01 1(1):3
Weber T, Kim HU (2016) The secondary metabolite bioinformatics portal: computational tools to facilitate synthetic biology of secondary metabolite production. Synth Syst Biotechnol 1:69–79. https://doi.org/10.1016/j.synbio.2015.1012.100
Wink MT, Schmeller TB, Latz Bruning B (1998) Modes of action of allelochemical alkaloids: interaction with neuroreceptors, DNA and other molecular targets. J Chem Ecol 24(11):1881–1937
Ziegler S, Pries V, Hedberg C, Waldmann H (2013) Target identification for small bioactive molecules: finding the needle in the haystack. Angew Chem Int Ed Eng 52:2744
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sabitha Rani, A., Neelima, G., Mukhopadhyay, R., Jyothi, K.S.N., Sulakshana, G. (2018). In Silico Characterization of Plant Secondary Metabolites. In: Choudhary, D., Kumar, M., Prasad, R., Kumar, V. (eds) In Silico Approach for Sustainable Agriculture. Springer, Singapore. https://doi.org/10.1007/978-981-13-0347-0_15
Download citation
DOI: https://doi.org/10.1007/978-981-13-0347-0_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0346-3
Online ISBN: 978-981-13-0347-0
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)