Structure‐Guided Identification of Flavonoid Inhibitors Targeting PE_PGRS17 in Mycobacterium tuberculosis : Insights From an Integrated In Silico Strategy

ABSTRACT PE_PGRS17, a surface‐associated antigen of Mycobacterium tuberculosis (Mtb), contributes to immune evasion and intracellular survival within macrophages and therefore represents a potentially relevant but underexplored therapeutic target. Here, a curated library of flavonoid compounds was screened against PE_PGRS17 using structure‐based computational methods. Among the candidates, compounds PubChem CID 452707 and 5280352 emerged as the most promising, with binding affinities of −11.5 and −8.471 kcal/mol, respectively. Detailed interaction profiling revealed multiple hydrogen bonds and extensive hydrophobic contacts with key residues, including Val223, Thr306, Thr320, His221 , and Val308 , anchoring the ligands within the predicted binding pocket. Molecular dynamics simulations over 200 ns confirmed that both ligands remained stably associated with PE_PGRS17, with protein RMSD values equilibrating within 1.5–2.0 Å and ligand fluctuations confined to rotatable bonds. Secondary structure analysis demonstrated that α‐helices and β‐strands were preserved throughout the trajectories, while MM‐GBSA binding free energy calculations supported sustained binding, with compound PubChem CID 452707 at −47.7 kcal/mol and compound PubChem CID 5280352 at ‐68.6 kcal/mol at 200 ns. Thus, these findings suggest that PE_PGRS17 warrants further investigation as a druggable target and that flavonoids may provide useful starting scaffolds for anti‐tubercular lead discovery.

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Journal
ChemistrySelect
Published
2026-10-29
DOI
https://doi.org/10.1002/slct.74995
Primary Topic
Computational Drug Discovery Methods
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article
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article

Structure‐Guided Identification of Flavonoid Inhibitors Targeting PE_PGRS17 in Mycobacterium tuberculosis : Insights From an Integrated In Silico Strategy

Mohammed H. AL Mughram, Fatmah M. Alqahtani, Yousef A. Bin Jardan, Amjad Ibraim Oraibi et al.
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ChemistrySelect
Computational Drug Discovery Methods
article

Structure‐Guided Identification of Flavonoid Inhibitors Targeting PE_PGRS17 in Mycobacterium tuberculosis : Insights From an Integrated In Silico Strategy

Mohammed H. AL Mughram, Fatmah M. Alqahtani, Yousef A. Bin Jardan, Amjad Ibraim Oraibi, Jannat ul Firdaus, Youssouf Ali Younous
article en

Abstract

ABSTRACT PE_PGRS17, a surface‐associated antigen of Mycobacterium tuberculosis (Mtb), contributes to immune evasion and intracellular survival within macrophages and therefore represents a potentially relevant but underexplored therapeutic target. Here, a curated library of flavonoid compounds was screened against PE_PGRS17 using structure‐based computational methods. Among the candidates, compounds PubChem CID 452707 and 5280352 emerged as the most promising, with binding affinities of −11.5 and −8.471 kcal/mol, respectively. Detailed interaction profiling revealed multiple hydrogen bonds and extensive hydrophobic contacts with key residues, including Val223, Thr306, Thr320, His221 , and Val308 , anchoring the ligands within the predicted binding pocket. Molecular dynamics simulations over 200 ns confirmed that both ligands remained stably associated with PE_PGRS17, with protein RMSD values equilibrating within 1.5–2.0 Å and ligand fluctuations confined to rotatable bonds. Secondary structure analysis demonstrated that α‐helices and β‐strands were preserved throughout the trajectories, while MM‐GBSA binding free energy calculations supported sustained binding, with compound PubChem CID 452707 at −47.7 kcal/mol and compound PubChem CID 5280352 at ‐68.6 kcal/mol at 200 ns. Thus, these findings suggest that PE_PGRS17 warrants further investigation as a druggable target and that flavonoids may provide useful starting scaffolds for anti‐tubercular lead discovery.

ChemistrySelectVol. 11(41)
King Saud University (SA), University of Manara (IQ), Sharda University (IN), King Khalid University (SA)
King Khalid University
Good health and well-being
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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