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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_6035_Библиотеки_им_академика_М_И_Перельмана.pdf
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Through MS-based peptidomics analysis, seven peptides belonging to three different
classes of antibacterial peptides, including a defensin, lipid transfer proteins, and snakins,
were discovered in the aerial tissue part of amaranth (Amaranthus tricolor). Furthermore,
the two bioactive peptides extracted from Acacia catechu, an Asian medicinal plant, have
been found to exhibit strong inhibitory effects against dengue viruses, indicating their
potential as potent antiviral agents (Moyer et al., 2021).
Using a rapid 1H NMR-based metabolomic approach, researchers analyzed a blend of
fourteen Fabaceae species commonly found in Mediterranean vegetation for its primary
metabolites. The study identied 31 primary metabolites in the blend that have the ability
to kill colon cancer cells. Among these metabolites, “Compound A from Astragalus
boeticus L.” and “Compound B from Trigonella esculenta Willd” were identied as the
most potent compounds. MS-based proteomics has been used to investigate the trichome
machinery and its role in artemisinin production in Artemisia annua L. (Bryant et al.,
2015; Chen et al., 2020). Kim et al. (2017) conducted a comprehensive proteomics
review of Panax species, which possess medicinal properties including circulatory shock
protection, anticancer, and antiaging, and also conducted a comparative proteomics
analysis of the root and leaf tissues of Indian, Oriental, and American ginseng. The
application of proteomics-based techniques has provided valuable insights into the
biology of ginseng, particularly in the case of Withania sominifera (L.) Dunal (Indian
ginseng) which is renowned for its withanolide secondary metabolites. Upon 2D-gel
separation, the root tissue of Indian ginseng revealed 56 unique spots, and matrix-assisted
laser desorption/ionization time-of-ight/time-of-ight analysis was used to identify
22 proteins (Nagappan et al., 2012). The use of NMR spectroscopy in metabolomics
research provides valuable data for detecting changes in two different dimensions.
Aloe vera (L.) Burm. f., a plant that has anticancer properties, was studied using NMR
spectroscopy and multivariate analysis to evaluate its metabolite prole and inhibitory
effects. The metabolome was found to affect the expression of the Bcl-2 and p53 genes
in hepatocellular carcinoma cells.
Thus, proteomics research on medicinal plants has provided important knowledge
regarding the structure, function, changes, and drug interactions of proteins. This information offers a better understanding of how plant-based medicines work in tumor cells or
any other cells and sheds light on the physiological status and hereditary characteristics
of plants. Such insights can potentially aid in the development of more effective natural
products based drugs for various disease treatments. Although proteomics data can provide
valuable information about protein structure, function, modications, and drug interactions, it may not always be consistent with transcriptomics data due to post-transcriptional
and post-translational modications. Therefore, it is essential to adopt integrated omics
approaches in future research to gain a comprehensive understanding of medicinal plant
biology. Metabolomics research conducted on medicinal plants has yielded valuable
information about identifying and isolating metabolites and NCCs, which can be used
to develop new drugs. Furthermore, these studies provide a better understanding of the
chemical composition of plant metabolites and their phytochemistry.
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10.3.2 LIMITATIONS AND CHALLENGES OF USING PROTEOMICS AND
METABOLOMICS IN NATURAL PRODUCT DISCOVERY
There are several limitations and challenges associated with using proteomics and
metabolomics in natural product discovery. These include the complexity of the data, the
lack of comprehensive databases, and the difficulty of identifying novel natural products.
Challenges in proteomics and metabolomics for natural product discovery include the
complexity of biological samples, the lack of standardized protocols for sample preparation
and analysis, and the need for extensive data analysis and interpretation. Despite the challenges, proteomics and metabolomics have the potential to make a significant impact on
natural product-based drug discovery. The integration of proteomic and metabolomic data
with other omics data, such as genomics and transcriptomics, is expected to provide a more
comprehensive understanding of natural product biosynthesis and regulation. The development of more comprehensive databases and software tools is also expected to facilitate the
identification and annotation of metabolites. Future directions in proteomics and metabolomics for natural product discovery include the development of more advanced analytical
techniques, improved data analysis and interpretation, and the integration of multiple omics
approaches. The potential impact of these advances includes the discovery of new natural
compounds with the potential for therapeutic use and the optimization of natural product
biosynthesis for improved yields and reduced environmental impact (Ong and Mann, 2005).
10.4 BIOINFORMATICS IN NATURAL PRODUCT-BASED DRUG DISCOVERY
Biological data, such as DNA, RNA, protein sequences, and other biological information,
are managed and analyzed by the interdisciplinary area of bioinformatics, which merges
biology, computer science, mathematics, and statistics (Bayat, 2022). Bioinformatics has
become increasingly important in the field of natural product-based drug discovery, which
involves identifying and characterizing new natural compounds from living organisms for
use in medicine, agriculture, and industry. Natural substances called phytochemicals are
present in plants and have been linked to several health advantages, such as anticancer,
anti-inflammatory, and antioxidant effectsClick or tap here to enter text.. Bioinformatics
plays a critical role in phytochemical discovery by providing researchers with tools and
resources to analyze and identify these compounds.
10.4.1 ROLE OF BIOINFORMATICS IN NATURAL PRODUCT-BASED DRUG DISCOVERY
Bioinformatics plays an important role in the following areas for drug discovery.
Bioinformatics tools can be used to analyze the genome sequences of various organisms to
identify the BGCs responsible for producing natural products. By identifying these gene
225
clusters, researchers can better understand the biosynthesis of natural products and develop
strategies for their efficient production.
Genome mining involves the computational analysis of genome sequences to identify
gene clusters that are responsible for the biosynthesis of natural products. This approach
involves the use of tools such as antiSMASH, which can predict BGCs and provide insights
into the biosynthetic pathways of natural products (Chavali and Rhee, 2018).
Bioinformatics tools can also be used to analyze the complex mixtures of small molecules that
are produced by living organisms. By analyzing the MS data generated from these mixtures,
researchers can identify novel natural products and determine their chemical structures.
Bioinformatics tools can be used to analyze metabolomics data from plants to identify and
characterize phytochemicals. This involves the use of MS and other analytical techniques to
detect and quantify the presence of phytochemicals in plant tissues. Metabolomics involves
the comprehensive analysis of small molecules, such as natural products, in biological
samples. This approach can be used to identify novel natural products and characterize their
chemical properties. Metabolomics data can be analyzed using bioinformatics tools such
as Mass Spectrometry Data Analysis Tool to identify and quantify the presence of natural
products in samples (Marco-Ramell et al., 2018).
Bioinformatics tools can also be used to predict the biological activities of natural
products. By analyzing the chemical structures of natural products, researchers can predict
their potential targets and modes of action, which can help guide drug discovery efforts
(Thomford et al., 2018).
Bioinformatics tools are also used to manage the large amounts of data generated in natural
product discovery. It is used to manage large databases of plant metabolites, including phytochemicals. By developing and maintaining databases of natural products and their associated
data, researchers can more efficiently analyze and compare the properties of different compounds.
These databases provide researchers with access to the chemical structures, physical properties,
and biological activities of these compounds (Bayat, 2022; Chavali and Rhee, 2018).
Network analysis involves the construction and analysis of biological networks, such as
metabolic pathways and protein–protein interaction networks. This approach can be used
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to identify novel natural products and potential targets for drug discovery (Atanasov et al.,
2021). For example, network analysis can be used to identify enzymes involved in the
biosynthesis of natural products and predict their potential substrates.
It involves the development of algorithms that can learn from data and make predictions.
This approach can be used to predict the properties of natural products, such as their
biological activities and toxicity . For example, machine learning algorithms can be trained
on a dataset of known natural products and their activities to predict the activities of novel
compounds (Biswas et al., 2023).
Bioinformatics tools can be used to predict the biological activities of phytochemicals.
This involves analyzing the chemical structures of these compounds and predicting their
potential targets and modes of action (Wright and Sieber, 2016; Chen et al., 2020).
10.4.2 THE USE OF BIOINFORMATICS TO PREDICT AND ANNOTATE NATURAL PRODUCT BIOSYNTHETIC PATHWAYS, GENE CLUSTERS, AND METABOLOMICS
Genome mining involves the analysis of genomic data to identify BGCs that are responsible for the production of natural products (Chavali and Rhee, 2018; Walker and Clardy,
2021). Bioinformatics tools such as antiSMASH, Deep-BGC, SMURF, and PRISM, are
geared toward identifying specific phytochemicals from natural products. AntiSMASH has
identified more than l lakh BGC sequences with a unique structure, which have given many
new drug entities (Walker and Clardy, 2021).
PRISM 4 is a method that was just recently published for predicting natural product
activity from the sequence of BGCs, by using chemical ngerprints (Skinnider et al., 2020).
Once BGCs have been identied, bioinformatics tools are utilized in identifying specic
pathways of natural products. This involves the identication of biosynthetic enzymes,
transporters, and regulatory genes involved in the biosynthesis of various phytochemicals.
Pathway prediction and annotation are essential for understanding the biosynthesis and
regulation of natural products and for the identication of potential drug targets.
Similar to other “omics” elds, metabolomics calls for specic bioinformatics tools.
Analyzing metabolomics data involves multiple key phases, including intensive raw data
preparation, multivariate statistical analysis and bioinformatics tools, data mining, incorporation with other omics data, and mathematical network modeling.
Metabolite proling involves the analysis of the metabolites produced by an organism.
Bioinformatics tools, such as metabolite proling and pathway analysis, can be used to
identify and quantify the metabolites produced by an organism. Metabolite proling can
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also be used to identify key regulatory metabolites and metabolic pathways that inuence
natural product biosynthesis. To determine the chemical structure of a natural product,
several tools, such as molecular docking and virtual screening, can be used to predict the
chemical structure of a natural product based on its biosynthetic pathway and metabolite
proling data. Structural elucidation is essential for identifying potential drug candidates
and for understanding the mechanisms of action of natural products.
Gene expression analysis involves the measurement of the amount of mRNA produced
by genes under different conditions. This approach can be used to identify genes that are
upregulated or downregulated during natural product biosynthesis (W alker and Clardy , 2021).
By analyzing gene expression data, researchers can predict the functions of uncharacterized
genes and identify potential biosynthetic intermediates.
Once potential natural product candidates have been identied, they can be screened for
their biological activity using bioassays. Bioinformatics approaches have revolutionized
natural product discovery by enabling researchers to analyze large datasets and extract
meaningful insights (Xia, 2017). Bioinformatics tools, such as network analysis and machine
learning, can be used to predict the potential biological activity of natural products based
on their chemical structure and biosynthetic pathway data (Chang et al., 2016). Screening
and validation are essential for identifying potential drug candidates and for understanding
the therapeutic potential of natural products.
Overall, bioinformatics plays a critical role in phytochemical discovery by providing
researchers with the tools and resources needed to identify , characterize, and develop these
compounds for use in medicine, agriculture, and other industries (Fang et al., 2018). By
combining bioinformatics with other experimental techniques, researchers can accelerate
the discovery and development of new phytochemicals with potential therapeutic benets.
Bioinformatics plays a critical role in natural product discovery by providing researchers
with the tools and resources needed to efciently identify, characterize, and develop novel
natural products for a wide range of applications.
10.5 INTEGRATION OF OMICS DATA USING BIOINFORMATICS TOOLS AND
PIPELINES
Integration of omics data is a crucial step in the analysis of biological systems, including
natural product biosynthesis. Bioinformatics tools and pipelines are used to integrate
different types of omics data, such as genomics, proteomics, transcriptomics, and metabolomics, to gain an in depth understanding of biological systemsClick or tap here to enter
text.. Here are some of the key bioinformatics tools and pipelines used for the integration
of OMICs data:
• Pathway analysis tools: Pathway analysis tools, such as Kyoto Encyclopedia of
Genes and Genomes and Reactome, provide a comprehensive overview of metabolic
and signaling pathways. These tools allow researchers to integrate omics data and
identify pathways that are altered under different conditions, such as natural product
biosynthesis. By analyzing the expression of genes and metabolites involved in
228
particular pathways, researchers can identify key regulators and potential targets for
drug discovery (dos Santos et al., 2016b; Park et al., 2016).
• Network analysis tools: Network analysis tools, such as Cytoscape and STRING,
enable the visualization and analysis of complex biological networks. These tools
allow researchers to integrate different types of omics data and identify nodes that
are highly connected within the network. By analyzing the interactions between
genes, proteins, and metabolites, researchers can identify key regulators and potential targets for drug discovery (Brohée et al., 2008; Laukens et al., 2015).
• Machine learning pipelines: Machine learning pipelines, such as Random Forest
and Support Vector Machines, enable the prediction of biological properties, such
as natural product activities, based on omics data. By integrating genomics, transcriptomics, proteomics, and metabolomics data, researchers can develop machinelearning models to predict the properties of novel natural products. These models
can be used to prioritize natural products for further experimental validation (Brohée
et al., 2008; Arjmand et al., 2022).
• Metabolic flux analysis: Metabolic flux analysis or fluxomics is the physiological
analog of its siblings’ transcriptomics, proteomics, and metabolomics in the modern
“omics age.” Fluxomics blends in vivo observations of metabolic fluxes with
various networks. By integrating transcriptomics, proteomics, and metabolomics
data, researchers can estimate the fluxes of metabolites and identify key enzymes
involved in natural product biosynthesis. This approach can be used to identify
potential targets for metabolic engineering to increase the yield of natural products
(Winter and Krömer, 2013).
Overall, the integration of omics data using bioinformatics tools and pipelines is crucial
for gaining a comprehensive understanding of biological systems, including natural product
biosynthesis. By combining these approaches with traditional experimental techniques,
researchers can accelerate the discovery and development of novel natural products with
potential therapeutic applications.
10.6 LIMITATIONS AND CHALLENGES OF USING BIOINFORMATICS IN NATURAL
PRODUCT DISCOVERY
While bioinformatics has greatly advanced natural product discovery, there are still some
limitations and challenges associated with this approach. It is crucial to keep in mind that
omics-based approach may only be able to see the proverbial tip of the iceberg, which
may not be sufficient to pinpoint precise molecular targets within intricate biochemical and
physiological networks (Hirota et al., 2012; Y in and Kassner , 2016). As a result, it is essential
to integrate several strategies into the system in a complementary or synergistic way . A range
of potential natural product targets and action mechanisms can also be clarified, described,
and evaluated at the same time using methods utilizing integrated multiomics methodology
for the successful development of a drug candidate (Hirota et al., 2012). Since target
proteins and biochemical, physiological, or other processes are not genetically conserved
across humans, target identification techniques have some limitations, and further validation
229
procedures must be carried out in human or other mammalian systems (Chang et al., 2016;
Rodrigues et al., 2016).
10.7 FUTURE PERSPECTIVES AND POTENTIAL IMPACT OF OMICS IN NATURAL PRODUCT-BASED DRUG DISCOVERY
The application of various omics technologies has revolutionized natural product-based drug
discovery. These approaches have allowed researchers to identify new natural products,
characterize biosynthetic pathways, and understand the mode of action of natural products.
As technology continues to advance, it is important to consider the potential future directions and impact of omics in natural product-based drug discovery (Guo et al., 2020).
10.8 POTENTIAL FUTURE DIRECTIONS FOR OMICS IN NATURAL PRODUCT
DISCOVERY
• Integration of omics data: There is a need to integrate data from multiple omics
approaches to gain a comprehensive understanding of natural products and their
mechanisms of action. Integration of data from genomics, transcriptomics,
proteomics, and metabolomics can provide a more complete picture of natural
products, their biosynthetic pathways, and their interactions with biological systems
(Luo et al., 2019; Hu et al., 2020).
• Application of machine learning: The use of machine learning algorithms can
help in analyzing complex omics data sets and identifying potential targets and lead
compounds. Machine learning can also be used to predict the properties of natural
products, such as their bioactivity and toxicity (Saldívar-González et al., 2021).
• Integration of synthetic biology: Synthetic biology approaches can be used to
manipulate natural product biosynthetic pathways and create new natural products
with improved properties (Jamieson et al., 2021).
10.9 POTENTIAL IMPACT ON DRUG DISCOVERY AND DEVELOPMENT
• Discovery of novel natural products: Omics approaches can help in discovering
novel natural products with potential therapeutic applications. These approaches
can also help in identifying natural products that can be used as lead compounds for
drug development (Thomford et al., 2018).
• Understanding the mode of action: Omics approaches can provide insights into the
mechanisms of action of natural products. This knowledge can be used to develop more
effective drugs and optimize dosing (Katz and Baltz, 2016; Thomford et al., 2018).
• Development of personalized medicine: Omics approaches can be used to identify
biomarkers that can be used to develop personalized medicine. This can improve
patient outcomes and reduce the risk of adverse effects (Beyoğlu and Idle, 2020).
230
• Optimization of drug development: Omics approaches can be used to optimize
drug development by identifying potential targets and lead compounds early in the
drug discovery process. This can reduce the time and cost required for drug development (Paananen and Fortino, 2020).
10.10 CONCLUSION
It can be concluded that Omics approaches have transformed natural product-based drug
discovery and have the potential to continue to do so in the future. Integration of data from
multiple omics approaches, application of machine learning algorithms, advancements in
imaging techniques, and integration of synthetic biology are some of the potential future
directions for omics in natural product discovery. The impact of omics on drug discovery
and development includes the discovery of novel natural products, understanding their mode
of action, development of personalized medicine, and optimization of drug development.
The continued development of omics technologies and their integration into drug discovery
pipelines holds great promise for the future of natural product-based drug discovery .
KEYWORDS
• natural products
• omics platforms
• genomics
• transcriptomics
• mass spectrometry
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