Subtractive Proteomics for Identification of Drug Targets in Bacterial Pathogens: A Review

Maurya, Shalini Singh; Akhtar, Salman; Siddiqui, Mohammed Haris; Khan, Mohammed Kalim Ahmad

doi:10.17577/ijertv9is010169

Cited by 4 publications

(1 citation statement)

References 35 publications

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“…Essential proteins are preferred targets for vaccine and antibacterial drug development. Thus, 171 essential proteins were identified [ 51 ]. Notably, not all essential genes can be exploited as therapeutic drug targets because some of them may be linked to the host metabolic pathways.…”

Section: Discussionmentioning

confidence: 99%

In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39

Shami

Alharbi

Al-Saeed

et al. 2023

Life

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Streptococcus pneumoniae is a notorious Gram-positive pathogen present asymptomatically in the nasophayrnx of humans. According to the World Health Organization (W.H.O), pneumococcus causes approximately one million deaths yearly. Antibiotic resistance in S. pneumoniae is raising considerable concern around the world. There is an immediate need to address the major issues that have arisen as a result of persistent infections caused by S. pneumoniae. In the present study, subtractive proteomics was used in which the entire proteome of the pathogen consisting of 1947 proteins is effectively decreased to a finite number of possible targets. Various kinds of bioinformatics tools and software were applied for the discovery of novel inhibitors. The CD-HIT analysis revealed 1887 non-redundant sequences from the entire proteome. These non-redundant proteins were submitted to the BLASTp against the human proteome and 1423 proteins were screened as non-homologous. Further, databases of essential genes (DEGG) and J browser identified almost 171 essential proteins. Moreover, non-homologous, essential proteins were subjected in KEGG Pathway Database which shortlisted six unique proteins. In addition, the subcellular localization of these unique proteins was checked and cytoplasmic proteins were chosen for the druggability analysis, which resulted in three proteins, namely DNA binding response regulator (SPD_1085), UDP-N-acetylmuramate—L-alanine Ligase (SPD_1349) and RNA polymerase sigma factor (SPD_0958), which can act as a promising potent drug candidate to limit the toxicity caused by S. pneumoniae. The 3D structures of these proteins were predicted by Swiss Model, utilizing the homology modeling approach. Later, molecular docking by PyRx software 0.8 version was used to screen a library of phytochemicals retrieved from PubChem and ZINC databases and already approved drugs from DrugBank database against novel druggable targets to check their binding affinity with receptor proteins. The top two molecules from each receptor protein were selected based on the binding affinity, RMSD value, and the highest conformation. Finally, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) analyses were carried out by utilizing the SWISS ADME and Protox tools. This research supported the discovery of cost-effective drugs against S. pneumoniae. However, more in vivo/in vitro research should be conducted on these targets to investigate their pharmacological efficacy and their function as efficient inhibitors.

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Section: Discussionmentioning

confidence: 99%

In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39

Shami

Alharbi

Al-Saeed

et al. 2023

Life

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Genome-Wide Subtraction Analysis and Reverse Vaccinology to Detect Novel Drug Targets and Potential Vaccine Candidates Against Ehrlichia chaffeensis

Sabzi

Shahbazi

Goodarzi

et al. 2022

Appl Biochem Biotechnol

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In silico discovery of druggable targets in Citrobacter koseri using echinoderm metabolites and molecular dynamics simulation

Alhaidhal,

Alsulais,

Mothana

et al. 2024

Sci Rep

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Citrobacter koseri causes infection in people who are immunocompromised. Without effective antibiotics, these infections can become severe and life-threatening, so effective drugs are essential to treat these infections. Utilizing subtractive genomics, 2699 ORFs were predicted and translated into amino acid sequences. Metabolic pathway analysis and subcellular localization helped define the roles of key bacterial proteins. Two druggable proteins, WP_012000829.1 and WP_275157394.1, were discovered as promising targets. Alpha Fold provided 3D structures, and a library of 1600 echinoderm metabolites was docked against these proteins, with Ampicillin, Levofloxacin, and Doxycycline as controls. Notably, CMNPD13085 and CMNPD15632 exhibited the highest binding affinities for WP_012000829.1 and WP_275157394.1, respectively. Molecular dynamics simulations and MM-GBSA binding free energy complemented docking results. However, acknowledging the reliance on computational validations, the study emphasizes the need for essential in-vitro research to transform these potential inhibitors into therapeutic drugs.

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Subtractive Proteomics for Identification of Drug Targets in Bacterial Pathogens: A Review

Cited by 4 publications

References 35 publications

In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39

In Silico Subtractive Proteomics and Molecular Docking Approaches for the Identification of Novel Inhibitors against Streptococcus pneumoniae Strain D39

Genome-Wide Subtraction Analysis and Reverse Vaccinology to Detect Novel Drug Targets and Potential Vaccine Candidates Against Ehrlichia chaffeensis

In silico discovery of druggable targets in Citrobacter koseri using echinoderm metabolites and molecular dynamics simulation

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