Synthesis of C7/C8-cyclitols and C7N-aminocyclitols from maltose and X-ray crystal structure of Streptomyces coelicolor GlgEI V279S in a complex with an amylostatin GXG-like derivative.

C7/C8-cyclitols and C7N-aminocyclitols find applications in the pharmaceutical sector as α-glucosidase inhibitors and in the agricultural sector as fungicides and insecticides. In this study, we identified C7/C8-cyclitols and C7N-aminocyclitols as potential inhibitors of Streptomyces coelicolor (Sco) GlgEI-V279S based on the docking scores. The protein and the ligand (targets 11, 12, and 13) were prepared, the states were generated at pH 7.0 ± 2.0, and the ligands were docked into the active sites of the receptor via Glide™. The synthetic route to these targets was similar to our previously reported route used to obtain 4-⍺-glucoside of valienamine (AGV), except the protecting group for target 12 was a p-bromobenzyl (PBB) ether to preserve the alkene upon deprotection. While compounds 11-13 did not inhibit Sco GlgEI-V279S at the concentrations evaluated, an X-ray crystal structure of the Sco GlgE1-V279S/13 complex was solved to a resolution of 2.73 Å. This structure allowed assessment differences and commonality with our previously reported inhibitors and was useful for identifying enzyme-compound interactions that may be important for future inhibitor development. The Asp 394 nucleophile formed a bidentate hydrogen bond interaction with the exocyclic oxygen atoms (C(3)-OH and C(7)-OH) similar to the observed interactions with the Sco GlgEI-V279S in a complex with AGV (PDB:7MGY). In addition, the data suggest replacing the cyclohexyl group with more isosteric and hydrogen bond-donating groups to increase binding interactions in the + 1 binding site.

Predicting the Anticancer Activity of 2-alkoxycarbonylallyl Esters against MDA-MB-231 Breast Cancer - QSAR, Machine Learning and Molecular Docking.

BACKGROUND: The continuous increase in mortality of breast cancer and other forms of cancer due to the failure of current drugs, resistance, and associated side effects calls for the development of novel and potent drug candidates. METHODS: In this study, we used the QSAR and extreme learning machine models in predicting the bioactivities of some 2-alkoxycarbonylallyl esters as potential drug candidates against MDA-MB-231 breast cancer. The lead candidates were docked at the active site of a carbonic anhydrase target. RESULTS: The QSAR model of choice satisfied the recommended values and was statistically significant. The R2pred (0.6572) was credence to the predictability of the model. The extreme learning machine ELM-Sig model showed excellent performance superiority over other models against MDAMB- 231 breast cancer. Compound 22 with a docking score of 4.67 kcal mol-1 displayed better inhibition of the carbonic anhydrase protein, interacting through its carbonyl bonds. CONCLUSION: The extreme learning machine's ELM-Sig model showed excellent performance superiority over other models and should be exploited in the search for novel anticancer drugs.

Predicting the bioactivity of 2-alkoxycarbonylallyl esters as potential antiproliferative agents against pancreatic cancer (MiaPaCa-2) cell lines: GFA-based QSAR and ELM-based models with molecular docking.

BACKGROUND: The number of cancer-related deaths is on the increase, combating this deadly disease has proved difficult owing to resistance and some serious side effects associated with drugs used to combat it. Therefore, scientists continue to probe into the mechanism of action of cancer cells and designing novel drugs that could combat this disease more safely and effectively. Here, we developed a genetic function approximation model to predict the bioactivity of some 2-alkoxyecarbonyl esters and probed into the mode of interaction of these molecules with an epidermal growth factor receptor (3POZ) using the three-dimensional quantitative structure activity relationship (QSAR), extreme learning machine (ELM), and molecular docking techniques. RESULTS: The developed QSAR model with predicted (R2pred) of 0.756 showed that the model was fit to be validated parameter for a built model and also proved that the developed model could be used in practical situation, R2 for training set (0.9929) and test set (0.8397) confirmed that the model could successfully predict the activity of new compounds due to its correlation with the experimental activity, the models generated with ELM models showed improved prediction of the activity of the molecules. The lead compounds (22 and 23) had binding energies of -6.327 and -7.232 kcalmol-1 for 22 and 23 respectively and displayed better inhibition at the binding sites of 3POZ when compared with that of the standard drug, chlorambucil (-6.0 kcalmol-1). This could be attributed to the presence of double bonds and the α-ester groups. CONCLUSION: The QSAR and ELM models had good prognostic ability and could be used to predict the bioactivity of novel anti-proliferative drugs.

In silico design and molecular docking study of CDK2 inhibitors with potent cytotoxic activity against HCT116 colorectal cancer cell line.

BACKGROUND: Colorectal cancer is common to both sexes; third in terms of morbidity and second in terms of mortality, accounting for 10% and 9.2% of cancer cases in men and women globally. Although drugs such as bevacizumab, Camptosar, and cetuximab are being used to manage colorectal cancer, the efficacy of the drugs has been reported to vary from patient to patient. These drugs have also been reported to have varying degrees of side effects; thus, the need for novel drug therapies with better efficacy and lesser side effects. In silico drugs design methods provide a faster and cost-effect method for lead identification and optimization. The aim of this study, therefore, was to design novel imidazol-5-ones via in silico design methods. RESULTS: A QSAR model was built using the genetic function algorithm method to model the cytotoxicity of the compounds against the HCT116 colorectal cancer cell line. The built model had statistical parameters; R2 = 0.7397, R2adj = 0.6712, Q2cv = 0.5547, and R2ext. = 0.7202 and revealed the cytotoxic activity of the compounds to be dependent on the molecular descriptors nS, GATS5s, VR1_Dze, ETA_dBetaP, and L3i. These molecular descriptors were poorly correlated (VIF < 4.0) and made unique contributions to the built model. The model was used to design a novel set of derivatives via the ligand-based drug design approach. Compounds e, h, j, and l showed significantly better cytotoxicity (IC50 < 5.0 µM) compared to the template. The interaction of the compounds with the CDK2 enzyme (PDB ID: 6GUE) was investigated via molecular docking study. The compounds were potent inhibitors of the enzyme having binding affinity of range -10.8 to -11.0 kcal/mol and primarily formed hydrogen bond interaction with lysine, aspartic acid, leucine, and histidine amino acid residues of the enzyme. CONCLUSION: The QSAR model built was stable, robust, and had a good predicting ability. Thus, predictions made by the model were reliably employed in further in silico studies. The compounds designed were more active than the template and showed better inhibition of the CDK2 enzyme compared to the standard drugs sorafenib and kenpaullone.