QSAR and molecular docking studies on designing potent inhibitors of SARS-CoVs main protease.

Background: Severe acute respiratory syndrome coronavirus (SARS-CoVs) have emerged as a global health threat, which had caused a high rate of mortality. There is an urgent need to find effective drugs against these viruses. Objective: This study aims to predict the activity of unsymmetrical aromatic disulfides by constructing a QSAR model, and to design new compounds according to the structural and physicochemical attributes responsible for higher activity towards SARS-CoVs main protease. Methods: All molecules were constructed in ChemOffice software and molecular descriptors were calculated by CODESSA software. A regression-based linear heuristic method was established by changing descriptors datasets and calculating predicted IC50 values of compounds. Then, some new compounds were designed according to molecular descriptors from the heuristic method model. The compounds with predicted values smaller than a set point were constantly screened out. Finally, the properties analysis and molecular docking were conducted to further understand the structure-activity relationships of these finalized compounds. Results: The heuristic method explored the various descriptors responsible for bioactivity and gained the best linear model with R2 0.87. The success of the model fully passed the testing set validation, proving that the model has both high statistical significance and excellent predictive ability. A total of 5 compounds with ideal predicted IC50 were found from the 96 newly designed derivatives and their properties analyze was carried out. Molecular docking experiments were conducted for the optimal compound 31a, which has the best compound activity with good target protein binding capability. Conclusion: The heuristic method was quite reliable for predicting IC50 values of unsymmetrical aromatic disulfides. The present research provides meaningful guidance for further exploration of the highly active inhibitors for SARS-CoVs.

Multi-omics integrative analysis and survival risk model construction of non-small cell lung cancer based on The Cancer Genome Atlas datasets.

Lung cancer is a major cause of cancer-associated mortality worldwide. However, the association between multi-omics data and survival in lung cancer is not fully understood. The present study investigated the performance of the methylation survival risk model in multi-platform integrative molecular subtypes and aimed to identify copy number (CN) variations and mutations that are associated with survival risk. The present study analyzed 439 lung adenocarcinoma cases based on DNA methylation, RNA, microRNA (miRNA), DNA copy number and mutations from The Cancer Genome Atlas datasets. First, six cancer subtypes were identified using integrating DNA methylation, RNA, miRNA and DNA copy number data. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to extract methylation sites of survival model and calculate the methylation-based survival risk indices for all patients. Survival for patients in the high-risk group was significantly lower compared with that for patients in the low-risk group (P<0.05). The present study also assessed methylation-based survival risks of the six subtypes and analyzed the association between survival risk and non-silent mutation rate, number of segments, fraction of segments altered, aneuploidy score, number of segments with loss of heterozygosity (LOH), fraction of segments with LOH and homologous repair deficiency. Finally, the specific copy number regions and mutant genes associated with the different subtypes were identified (P<0.01). Chromosome regions 17q24.3 and 11p15.5 were identified as those with the most survival risk-associated copy number variation regions, while a total of 29 mutant genes were significantly associated with survival (P<0.01).

3D-QSAR and Molecular Docking Studies on Design Anti-Prostate Cancer Curcumin Analogues.

BACKGROUND: Prostate cancer is one of the most common tumors in the world and the fifth leading cause of male cancer death. Although the treatment of localized androgen-dependent prostate cancer has been successful, the efficacy of androgen-independent metastatic disease is limited. Curcumin, a natural product, has been found to inhibit the proliferation of prostate cancer cells. OBJECTIVE: To design curcumin analogs with higher biological activity and lower toxicity and side effects for the treatment of prostate cancer. METHODS: In this study, the three dimensional-quantitative structure activity relationship (3DQSAR) and molecular docking studies were performed on 34 curcumin analogs as anti-prostate cancer compounds. We introduced OSIRIS Property Explorer to predict drug-related properties of newly designed compounds. RESULTS: The optimum CoMSIA model exhibited statistically significant results: the cross-validated correlation coefficient q2 is 0.540 and non-cross-validated R2 value is 0.984. The external predictive correlation coefficient Rext 2 is 0.792. The information of structure-activity relationship can be obtained from the CoMSIA contour maps. In addition, the molecular docking study of the compounds for 3ZK6 as the protein target revealed important interactions between active compounds and amino acids. CONCLUSION: Compound 28i may be a new type of anti-prostate cancer drug with higher biological activity and more promising development.

Quantitative structure-activity relationship and molecular docking studies on designing inhibitors of the perforin.

Quantitative structure-activity relationship (QSAR) studies were performed on a series of 5-arylidene-2thioxoimidazolidin-4-ones derivatives as the inhibitors of perforin and to gain insights about the structural determinants for designing new drug molecules. The heuristic method could explore the descriptors responsible for bioactivity and gain a best linear model with R2 .82. Gene expression programming method generated a novel nonlinear function model with R2 .92 for training set and R2 .85 for test set. The predicted IC50 by QSAR, molecular docking analysis, and property explorer applet show that 42a acts as a well-pleasing potent inhibitor for perforin. This study may lay a reliable theoretical foundation for the development of designing perforin inhibitor structures.

QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs.

A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.

[Effect of amygdalin on serum proteinic biomarker in pulmonary fibrosis of bleomycin-induced rat].

OBJECTIVE: To evaluate effect of amygdalin on expression of four biomarkers in the animal model of pulmonary fibrosis induced by bleomycin. METHODS: Rats were given one dose (5 mg/kg) of bleomycin in bleomycin-treated groups, amygdalin-treated groups and saline in controls by intratracheal instillation exposed surgically. The amygdalin-treated groups rats were treated with intraperitoneal injection of amygdalin (15 mg x kg(-1) x day(-1)). The rats were sacrificed 7, 14 and 28 days after bleomycin administration. Polarized light microscopy and Image-Pro Plus detected I and III collagen expressed in Paraffin-embedded lung sections stained with Sirius red. Surface-enhanced laser desorption-ionization time-of-flight mass spectrometry (SELDI-TOF MS) with weak cationic proteinchip (CM10) detected differentially expressed proteins in the pooled serum samples of all groups. RESULTS: Consistent fibrotic responses were found in all bleomycin and amygdalin-tread groups. On the 7th, 14th and 28th day after bleomycin or saline instillation, four differentially expressed proteins were detected in the pooled serum of all groups rats, consisting of 4 proteins with mass/charge ratio of 3530.7, 7043.5, 8332.6 and 9068.0, respectively. Compared with control groups, protein peaks intensity ratio with mass/charge ratio of 3530.7 on 7, 28 d and 7043.5, 8332.6 and 9068.0 on 7, 14 and 28 d was > 2 in bleomycin-treated groups. Compared with amygdalin-treated groups, protein peaks intensity with mass/charge ratio of 3530.7 at 7, 14, 28 d had no change almost, but protein peaks intensity ratio with mass/charge ratio of 7043.5 at 7 d, 8332.6 on 28 d and 9068.0 on 14 d was > 2 in bleomycin-tread groups. All the four protein peaks intensity had no change almost at other point. CONCLUSION: Amygdalin may reduce the bleomycin-induced increase of differentially expressed protein peak intensities in rat serum.