94
filters selected. Recently, in vitro assays information, molecular docking and molecular dynamic simulation were associated to screen potential phototherapy molecules against already prioritized targets of multi drug resistant Acinetobacter
baumannii (Skariyachan et al. 2019). Another example of multi drug resistant bacteria, Streptococcus pneumoniae is the leading cause of bacterial pneumonia. In a
successful choice of filters (virulence analysis, drugability analysis, metabolic pathway enrichment, functional annotation and interactome network) resulting in just
two chokepoint hub enzymes (Nayak et al. 2019).
4.7.2 Metabolic Network Analysis
One useful application to prioritize drug targets in pathogens is the prediction of
molecules interdependency in biochemical reactions. These biochemical interactions may involve hundreds to thousands of metabolites and enzymatic reactions,
which participate in different subsets at microbial metabolism. Although different
types of biomolecules (nucleotide, carbohydrate, lipid, and amino acid) constitute
the complexity of microbial metabolism, protein-protein or protein-DNA interactions are currently the focus of study.
Metabolites interactions prediction can provide key information about putative
targets implicated in pathogenic and virulence process, adaption and response to
stress. Biological networks follow a standard architecture, where genes, proteins,
and compounds are represented by nodes, which are connected by edges represented through protein-compound, protein-gene, protein-protein interactions and
metabolic reactions (Nikolsky et al. 2005).
After sequencing or data retrieval from online databases, a whole genome or
partial metabolic network can be applied (Fig. 4.2). The filtering process can consider literature, size and cellular location of the protein, quality of tertiary structure,
drug ability of modelled proteins and homology with the host. Diverse online tools
can be used to generate networks.
Table 4.3 The list of some available target identification and prioritization databases, web tools,
and software
Name
Standalone/Web tool
References
AntibacTR
Database
Panjkovich et al.
(2014)
PDTD (Potential Drug Target Database) Database
Gao et al. (2008)
UniDrug-Target
Standalone & Web-Based
tool
Chanumolu et al.
(2012)
Target TB
Database
Raman et al. (2008)
Target-Pathogen
Database
Sosa et al. (2018)
TiD (Target iDentification)
Standalone
Gupta et al. (2017)
T-iDT (Tool for identification of drug
target)
Standalone
Singh et al. (2006)
TarFisDock (Target Fishing Dock)
Web-based tool
Li et al. (2006)
M. Santana et al.
filters selected. Recently, in vitro assays information, molecular docking and molecular dynamic simulation were associated to screen potential phototherapy molecules against already prioritized targets of multi drug resistant Acinetobacter
baumannii (Skariyachan et al. 2019). Another example of multi drug resistant bacteria, Streptococcus pneumoniae is the leading cause of bacterial pneumonia. In a
successful choice of filters (virulence analysis, drugability analysis, metabolic pathway enrichment, functional annotation and interactome network) resulting in just
two chokepoint hub enzymes (Nayak et al. 2019).
4.7.2 Metabolic Network Analysis
One useful application to prioritize drug targets in pathogens is the prediction of
molecules interdependency in biochemical reactions. These biochemical interactions may involve hundreds to thousands of metabolites and enzymatic reactions,
which participate in different subsets at microbial metabolism. Although different
types of biomolecules (nucleotide, carbohydrate, lipid, and amino acid) constitute
the complexity of microbial metabolism, protein-protein or protein-DNA interactions are currently the focus of study.
Metabolites interactions prediction can provide key information about putative
targets implicated in pathogenic and virulence process, adaption and response to
stress. Biological networks follow a standard architecture, where genes, proteins,
and compounds are represented by nodes, which are connected by edges represented through protein-compound, protein-gene, protein-protein interactions and
metabolic reactions (Nikolsky et al. 2005).
After sequencing or data retrieval from online databases, a whole genome or
partial metabolic network can be applied (Fig. 4.2). The filtering process can consider literature, size and cellular location of the protein, quality of tertiary structure,
drug ability of modelled proteins and homology with the host. Diverse online tools
can be used to generate networks.
Table 4.3 The list of some available target identification and prioritization databases, web tools,
and software
Name
Standalone/Web tool
References
AntibacTR
Database
Panjkovich et al.
(2014)
PDTD (Potential Drug Target Database) Database
Gao et al. (2008)
UniDrug-Target
Standalone & Web-Based
tool
Chanumolu et al.
(2012)
Target TB
Database
Raman et al. (2008)
Target-Pathogen
Database
Sosa et al. (2018)
TiD (Target iDentification)
Standalone
Gupta et al. (2017)
T-iDT (Tool for identification of drug
target)
Standalone
Singh et al. (2006)
TarFisDock (Target Fishing Dock)
Web-based tool
Li et al. (2006)
M. Santana et al.