Fig 1.
Flow diagram of the pan-cancer study of SIRT7 comprising the relevance of SIRT7 in cancers, analysis of networks, gene expression, alterations, and regulations of SIRT7, along with identification of therapeutic opportunities.
Fig 2.
Quality assessment of the 3D structure model of SIRT7.
(A) Model protein structure (Designed using Discovery Studio 2021), (B) Ramachandran Plot (Source: SAVES v6.1), (C) Normalized QMEAN plot (Source: Swiss QMEAN), (D) Overall model quality plot (Source: ProSA).
Fig 3.
Cancer relevance analysis of SIRT7.
(A) Subcellular localization of SIRT7. (B) immunohistochemistry of SIRT7. Green indicates nuclear regions. (C), (D), (E) Expression profile of SIRT7 across tissues, cell lines, and cancer types, respectively. (Source: Human Protein Atlas).
Fig 4.
SIRT7 interacting patterns and partners.
(A) Physical interactions and (B) co-expression between SIRT7 and neighboring genes (Source: GeneMania). (C) Protein-protein interaction network of SIRT7 (Source: STRING 12).
Table 1.
Performance of the deep neural network model in calculating hub proteins.
Table 2.
The most important feature extraction process using the automated deep neural network-based hub proteins prediction model.
Fig 5.
(A) SIRT7 expression levels (log2 TPM) across multiple cancer types, comparing tumor and normal tissues. * indicates significant expression, and *** means highly significant expression (Source: TIMER2.0). (B-I) Kaplan-Meier overall survival analyses depicting survival differences between high and low SIRT7 expression groups in (B) BLCA, (C) KIRC, (D) KIRP, (E) LGG, (F) LIHC, (G) PRAD, (H) SARC, and (I) UCEC (Source: GEPIA2).
Fig 6.
(A) Mutations that alter SIRT7 expression, and (B) SIRT7 survival effects on altered and unaltered groups (Source: cBioPortal). (C) Changes in Gene expression of the SIRT7 protein cluster network due to the alteration of SIRT7 (Source: TIMER2).
Fig 7.
Survival analysis of SIRT7 expression and immune cell infiltration impact on patient outcomes in (A) Glioblastoma Multiforme (GBM) CD8 + , (B) Kidney Renal Papillary Cell Carcinoma (KIRP) CD8 + , (C) Uveal Melanoma (UVM) CD8 + , (D) Lower Grade Glioma (LGG) CD4 + , and (E) Lower Grade Glioma (LGG) B-Cell Populations (Source: TIMER2).
Table 3.
Types of cancer promoted by SIRT7.
Fig 8.
Localization of active site residues within the SIRT7 domain, highlighting the inhibitory site (residues: 116–128) and the catalytic site (residues: 169, 187, 237, 239–243, 240, 272, 273, 277).
(A) Schematic representation of the SIRT7 domain indicating key residue positions. (B) Ribbon diagram of SIRT7 with the inhibitory site surface highlighted in red. (C) Surface representation of SIRT7 emphasizing the catalytic site residues in red within the active pocket (Designed using MOE 2019 release).
Fig 9.
Mapping pharmacophore features.
(A) Superimposed view of pharmacophore features mapping within the active site of the target protein. (B) ZINC000150641215, (C) ZINC000150487575, (D) MCULE-8582664468, and (E) CID: 155513088 (Control). (Designed using Schrodinger 2024.2 release).
Table 4.
Molecular docking analysis to capture interacting amino acids with compounds in the most suitable binding pose.
Fig 10.
Molecular docking analysis of candidate inhibitors reveals key hydrogen bonding and hydrophobic interactions within the SIRT7 inhibitory domain.
(A) ZINC000150487575, (B) ZINC000150641215, (C) MCULE-8582664468, and (D) PubChem-155513088 (Control) (Designed using MOE 2019 release).
Table 5.
Toxicity profiling of selected compounds: molecular weight, toxicophores, hepatotoxicity, and carcinogenicity assessment.
Fig 11.
Molecular dynamics simulations.
(A) RMSD, (B) RMSF, (C), (D), (E), and (F) Hydrogen bonding occupancy of amino acid residues for compound S1, compound S2, compound S3, and control inhibitor, respectively (Designed using Matplotlib 3.10.0).
Fig 12.
Molecular mechanics (MM) of SIRT7-Compounds Complex.
(A) Molecular Mechanics Generalized Born Surface Area (MMGBSA). (B) Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) (Designed using Matplotlib 3.10.0).