3D-QSAR, Molecular Docking, and MD Simulations of Anthraquinone Derivatives as PGAM1 Inhibitors

3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors

Isocitrate dehydrogenase 1 (IDH1) is a necessary enzyme for cellular respiration in the tricarboxylic acid cycle. Mutant isocitrate dehydrogenase 1 (mIDH1) has been detected overexpressed in a variety of cancers. mIDH1 inhibitor ivosidenib (AG-120) was only approved by the Food and Drug Administration (FDA) for marketing, nevertheless, a range of resistance has been frequently reported. In this study, several mIDH1 inhibitors with the common backbone pyridin-2-one were explored using the three-dimensional structure-activity relationship (3D-QSAR), scaffold hopping, absorption, distribution, metabolism, excretion (ADME) prediction, and molecular dynamics (MD) simulations. Comparative molecular field analysis (CoMFA, R2 = 0.980, Q2 = 0.765) and comparative molecular similarity index analysis (CoMSIA, R2 = 0.997, Q2 = 0.770) were used to build 3D-QSAR models, which yielded notably decent predictive ability. A series of novel structures was designed through scaffold hopping. The predicted pIC50 values of C3, C6, and C9 were higher in the model of 3D-QSAR. Additionally, MD simulations culminated in the identification of potent mIDH1 inhibitors, exhibiting strong binding interactions, while the analyzed parameters were free energy landscape (FEL), radius of gyration (Rg), solvent accessible surface area (SASA), and polar surface area (PSA). Binding free energy demonstrated that C2 exhibited the highest binding free energy with IDH1, which was -93.25 ± 5.20 kcal/mol. This research offers theoretical guidance for the rational design of novel mIDH1 inhibitors.

2-Amino Thiazole Derivatives as Prospective Aurora Kinase Inhibitors against Breast Cancer: QSAR, ADMET Prediction, Molecular Docking, and Molecular Dynamic Simulation Studies

The aurora kinase is a key enzyme that is implicated in tumor growth. Research revealed that small molecules that target aurora kinase have beneficial effects as anticancer agents. In the present study, in order to identify potential antibreast cancer agents with aurora kinase inhibitory activity, we employed QSARINS software to perform the quantitative structure-activity relationship (QSAR). The statistical values resulted from the study include R2 = 0.8902, CCCtr = 0.7580, Q2 LOO = 0.7875, Q2LMO = 0.7624, CCCcv = 0.7535, R2ext = 0.8735, and CCCext = 0.8783. Among the four generated models, the two best models encompass five important variables, including PSA, EstateVSA5, MoRSEP3, MATSp5, and RDFC24. The parameters including the atomic volume, atomic charges, and Sanderson's electronegativity played an important role in designing newer lead compounds. Based on the above data, we have designed six series of compounds including 1a-e, 2a-e, 3a-e, 4a-e, 5a-e, and 6a-e. All these compounds were subjected to molecular docking studies by using AutoDock v4.2.6 against the aurora kinase protein (1MQ4). Among the above 30 compounds, the 2-amino thiazole derivatives 1a, 2a, 3e, 4d, 5d, and 6d have excellent binding interactions with the active site of 1MQ4. Compound 1a had the highest docking score (-9.67) and hence was additionally subjected to molecular dynamic simulation investigations for 100 ns. The stable binding of compound 1a with 1MQ4 was verified by RMSD, RMSF, RoG, H-bond, molecular mechanics-generalized Born surface area (MM-GBSA), free binding energy calculations, and solvent-accessible surface area (SASA) analyses. Furthermore, newly designed compound 1a exhibited excellent ADMET properties. Based on the above findings, we propose that the designed compound 1a may be utilized as the best theoretical lead for future experimental research of selective inhibition of aurora kinase, therefore assisting in the creation of new antibreast cancer drugs.

3D-QSAR, molecular docking, and molecular dynamics analysis of dihydrodiazaindolone derivatives as PARP-1 inhibitors

CONTEXT

PARP-1 plays an important role in DNA repair and apoptosis, and PARP-1 inhibitors have shown to be effective in the treatment of several malignancies. To evaluate the function of new PARP-1 inhibitors as anticancer adjuvant medicines, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations of a sequence of dihydrodiazepinoindolone derivatives PARP-1 inhibitors were undertaken in this study.

METHODS

In this paper, 43 PARP-1 inhibitors were studied in a three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). CoMFA with q2 of 0.675 and r2 of 0.981 was achieved, as was CoMSIA with q2 of 0.755 and r2 of 0.992. The changed areas of these compounds are shown by steric, electrostatic, hydrophobic, and hydrogen-bonded acceptor field contour maps. Subsequently, molecular docking, and molecular dynamics simulations further confirmed that key residues Gly863 and Ser904 of PARP-1 are vital residues for protein interactions and their binding affinity. The effects of 3D-QSAR, molecular docking and molecular dynamics simulations supply a new route for the search of new PARP-1 inhibitors. Finally, we designed eight new compounds with exact activity and ADME/T properties.

Phosphoglycerate mutase 1 that is essential for glycolysis may act as a novel metabolic target for predicating poor prognosis for patients with gastric cancer

BACKGROUND

To identify a novel marker for gastric cancer, we examined the usefulness of phosphoglycerate mutase 1 (PGAM1) as a potential diagnostic marker using isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics and evaluated its clinical significance.

METHODS

Proteins from a discovery group of four paired gastric cancer tissues and adjacent gastric tissues were labeled with iTRAQ reagents and then identified and quantified using LC-MS/MS. The expression of PGAM1 was further validated in 139 gastric cancer patients using immunohistochemistry. Furthermore, the correlation of PGAM1 expression with clinical parameters was analyzed. Gene set enrichment analysis (GSEA) was performed to identify gene sets that were activated in PGAM1-overexpressing patients with gastric cancer.

RESULTS

PGAM1 was significantly overexpressed in most cancers but particularly so in gastric cancer, with a sensitivity of 82.01% (95% confidence interval [CI]: 75.5%-88.5%) and specificity of 79.13% (95% CI: 72.3%-86%). Its expression was significantly associated with histological grade II and III tumors (p = 0.033), lymph node metastasis (p = 0.031), and TNM III-IV staging (p = 0.025). The area under the receiver operating characteristic (ROC) curve for the detection of PGAM1 overexpression in gastric cancer was 0.718 (p < 0.01). Furthermore, GSEA revealed that several important pathways such as glycolysis pathway and immune pathways were significantly enriched in patients with gastric cancer with PGAM1 overexpression.

CONCLUSIONS

This study provided a sensitive method for detecting PGAM1, which may serve as a novel indicator for poor prognosis of gastric cancer, as well as a potent drug target for gastric cancer.