Current Computer - Aided Drug Design - Volume 13, Issue 2, 2017

Background: Schistosomiasis is a parasitic protozoal disease caused by flatworms of the genus Schistosoma. Although the disease threatens millions of lives, it is still at the top list of neglected tropical diseases and praziquantel, the only common schistosocidal drug in use, has records of decreasing efficiency and cases of resistance. Also, reports revealed that people in the rural areas, who are most affected, rely mostly on traditional herbal medicines because of limited access to modern healthcare. The use of computers in drug development has become a routine practice because they are cost and time effective. Objective: We used computational techniques to discover potent Schistosoma inhibitors of natural product origin. Methods: Computer-based techniques were employed to discover anti-schistosoma lead/hit from plant metabolites isolated from African medicinal plants which have shown activity or are responsible for activity against Schistosomes. The plant metabolites were evaluated for ‘drug-likeness’ and docked toward four selected validated Schistosoma drug targets; Glutathione S-transferase, Thioredoxin glutathione reductase, Histone deacetylase and Schistosoma masoni arginase, with PDB codes: 1M9A, 2X99, 4BZ8 and 4Q3T respectively. Results: A total of 27 bioactive compounds with anti-Schistosoma history were retrieved from literature. The phytochemicals with Lipinski violation of ≤2 were found to exhibit comparable binding affinities toward the studied targets as the co-crystallized inhibitors. Predicted binding modes of the compounds toward respective target showed key intermolecular interactions involved in the ligandtarget relationship. Moreover, one of the compounds emerged as the most interesting candidate by both being drug-like and inhibiting the activities of the studied enzyme targets at micro-molar range. Conclusion: Our study identified schistosocidal leads with high bioavailability profile and the exploration of binding modes could lay the foundation for synthetic modification of the plant metabolites for the development of novel anti-schistosoma drug(s) with new mechanism of action.

Background: Death toll due to tuberculosis is still rising day by day. Whole genome sequence of Mycobacterium tuberculosis has provided a platform to conduct research in order to identify the probable drug target. Objectives: Out of 4000 gene products of M. tuberculosis, approximately 40% of proteins are annotated as hypothetical. Identifying and characterizing these proteins could provide a new prescriptive for developing new TB drugs. Rv1288, a protein of M. tuberculosis H37Rv has been annotated as a hypothetical protein in database. Attempt has been made to assign a meaningful role to rv1288 gene product in M. tuberculosis life cycle. Methods: A homology 3D structure of both domains was separately generated and assigned as Rv1288LytE and Rv1288est. Molecular simulation of Rv1288est was carried out for proper structure analysis. To further confirm the predictive role of Rv1288 in mycobacterium life cycle, molecular docking was performed. N-acetyl glucosamine, a major constituent of cell wall was docked with LytE domain, whereas, esterase domain was docked with lipolytic substrate, pNP-ester derivatives and inhibitors THL/PMSF. Results: In-silico analysis revealed that Rv1288 is a two domain protein, an N-terminal LytE domain containing three consecutive LysM motifs and a C-terminal esterase domain of esterase D family. LytE domain has the property to bind N-acetyl glucosamine moieties of peptidoglycan, a major component of cell wall. Detailed in-silico sequence analysis revealed that this LytE domain may help in positioning the esterase domain to the cell wall of mycobacterium. Esterase domain comprised a tetrapeptide motif HGGG, a pentapeptide sequence motif GxSxG and conserved amino acid residues Ser-141, Asp-238 and His-272 which constitute a catalytic triad characteristic of other hormone sensitive lipases/ esterases. Docking studies suggested that THL and PMSF could be the potent inhibitors for Rv1288 protein. Conclusion: In the present investigation, we bioinformatically confirmed that Rv1288 is most likely a LytE domain containing lipolytic enzyme showing similarity to hormone sensitive lipases/esterases.

Background: Breast cancer is a systemic disease which has challenged physicians worldwide as it is the most predominant cancer in women often leading to fatality. One of the types of treatment is chemotherapy which includes targeted oral or intravenous cancer-killing drugs. Treatment options are often limited to surgery and/or chemotherapy. Objective: The discovery and design of new small molecule estrogen inhibitors is necessitated in order to circumvent the problem of drug-induced resistance in chemotherapy resulting in disease relapse. Chemoinformatics facilitates the design, selection and synthesis of new drug candidates for breast cancer by providing efficient in silico techniques for prediction of favourable ADMET properties, and structural descriptors to profile druggability of a compound. Method: Several molecules selected from docking studies were synthesized and evaluated for their biological activities on the MCF-7 (human breast cancer) cell line. Results: These estrogen inhibitors displayed good inhibitory activity with high selectivity and hence can be further progressed as drug candidates effective against breast cancer. Conclusion: It is for the first time that N-(2, 4-dinitrophenyl)-3-oxo-3-phenyl-N-(aryl) phenylpropanamide derivatives were reported to be biological active as potential breast cancer inhibitors.

Background: The accuracy of molecular conformation for Quantitative Structure Activity Relationship (QSAR) studies is an important criteria, and the most favourable bioactive conformer selection is a tough task. Correct ligand alignment as input for 3D-QSAR is an important step that is prone to human biases. Multiple-dimensional QSAR (mQSAR) approach provides a promising alternative to classic 3D-QSAR for drug discovery purposes. Objective: Obtaining ligand conformations from multiple receptor conformation docking (MRCD) will reduce the margin of error by incorporating the receptor based alignment of ligand conformations. To validate this assumption we performed 6D QSAR studies on reported HIV-1 protease inhibitors using Quasar 6.0. Materials & Method: The ensemble of conformation was obtained by MRCD of ligands in thirteen crystal structures of HIV-1 protease. 6D QSAR model was built using 65 cyclic urea molecules reported as HIV-1 protease inhibitors. Predictive ability of the model was validated using 35 cyclic urea molecules as test set. External predictive ability of the model was evaluated using a set of 24 HIV-1 protease inhibitors having varied structural scaffold. Result: 6D QSAR model obtained showed a reliable cross-validated r2(q2) of 0.899, r2(classic) of 0.908 and yielded a predictive r2 (p2) of 0.527. The ratio of q2/r2 was 0.991 and p2/q2 was 0.586 for external test set. Conclusion: The QSAR results invariably suggest that our approach is suitable for the identification of molecules having HIV-1 protease inhibitory potency. The underlying philosophy combines flexible docking for the identification of the binding modes and 6D QSAR for their quantification.

Background: The health and life of humans have been seriously threatened by cancer for a long period and cancer has become the leading disease-related cause of deaths of human population. Natural products such as colchicine and vinblastine inhibit microtubule assembly by preventing tubulin polymerization. GA-MLR is a powerful search technique based on the evolution of biological systems for QSAR modeling. In this paper, we studied QSAR modeling of some arylthioindole class of colchicine polymerization inhibitors as anticancer agents using GA-MLR and stepwise-MLR. Methods: The chemical structures and experimental values for inhibition of colchicine binding taken from the literature. In the study of inhibition of colchicine binding the total numbers of 49 compounds were split into the training and test sets randomly, which have 39 and 10 compounds, respectively. The Chem3D module was used in order to create the 3D structures of compounds; geometry optimization, using the Polak-Ribiere algorithm. The total numbers of 1185 molecular descriptors such as GETAWAY, RDF, WHIM and 3D-MoRSE descriptors were derived for proper characterizing the structures of arylthioindoles derivatives. These molecular descriptors were reduced to 447. In fact the variables which have low correlation with response, constant variables and also collinear descriptors were eliminated. The random sampling of the training set (80% of data) was performed 20 times and the remaining molecules have been used as external validation set. GA-MLR and S-MLR methods were applied on all random training data sets. Results: After splitting the data set by RS method, the GA-MLR and S-MLR methods were applied on the training set to select important variables. The best models consist of one, two, three, four, five and six variables created to find the best QSAR model. The best multivariate linear model based on Q2cal and Q2test values had five parameters in both GA-MLR and S-MLR methods. Conclusion: The results indicate that in this study, the Q2test values are 0.6209 and 0.1144 for GAMLR and S-MLR methods; respectively. According to the results of external validation, we can conclude that the GA-MLR method is more powerful than S-MLR in variable selecting. Also in SAR studies we can conclude that the arylthioindole derivatives with higher density of electrons in C2 position have the largest amounts of IC50. So we can use this important fact to synthesize stronger anticancer agents.

Background: Hepatitis C is a significant cause for end-stage liver diseases and liver transplantation which affects approximately 3% of the global populations. Despite the current several direct antiviral agents in the treatment of Hepatitis C, the standard treatment for HCV infection is accompanied by several drawbacks, such as adverse side effects, high pricing of medications and the rapid emerging rate of resistant HCV variants. Objectives: To discover potential inhibitors for HCV helicase through an optimized in silico approach. Methods: In this study, a homology model (HCV Genotype 3 helicase) was used as the target and screened through a benzopyran-based virtual library. Multiple-seedings of AutoDock Vina and in situ minimization were to account for the non-deterministic nature of AutoDock Vina search algorithm and binding site flexibility, respectively. ADME/T and interaction analyses were also done on the top hits via FAFDRUG3 web server and Discovery Studio 4.5. Results: This study involved the development of an improved flow for virtual screening via implemention of multiple-seeding screening approach and in situ minimization. With the new docking protocol, the redocked standards have shown better RMSD value in reference to their native conformations. Ten benzopyran-like compounds with satisfactory physicochemical properties were discovered to be potential inhibitors of HCV helicase. ZINC38649350 was identified as the most potential inhibitor. Conclusion: Ten potential HCV helicase inhibitors were discovered via a new docking optimization protocol with better docking accuracy. These findings could contribute to the discovery of novel HCV antivirals and serve as an alternative approach of in silico rational drug discovery.