RESUMO
Alzheimer's disease (AD) is a neurodegenerative pathology responsible for 70% of dementia cases worldwide. Despite its relevance, the few drugs available for the treatment of this disease offer only symptomatic relief, with limited efficacy and serious adverse effects. The most accepted hypothesis about the pathogenesis involves the aggregation and deposition of ß-amyloid peptides, mainly in the cerebral cortex and hippocampus, through the catalytic action of beta-secretase 1 (BACE-1), making this enzyme a promising target for the development of new drugs. In order to prioritize candidates for BACE-1 inhibitors, a hierarchical virtual screening by pharmacophore model and molecular docking was performed against the 216,833 molecules contained in several databases. Our previously built pharmacophore model was used for the first filtering step, which resulted in the selection of 399 molecules. The remaining molecules were filtered through molecular docking with GOLD 5.4.0. In this step, molecules with scoring values ââgreater than the mean plus standard deviation were evaluated for commercial availability and absence of asymmetric centers. Four molecules were selected and evaluated for mutagenic potential by the AMES test with the help of the pkCSM server. Finally, they were tested against the descriptors on Lipinski and Veber rules, and ZINC01589617 (QFIT = 56.52/Score = 44.95) satisfied all requirements, being subjected to molecular dynamics simulations (t = 100 ns) in order to obtain robust data on the mode of bonding and profile of intermolecular interactions. Those in silico strategies demonstrated that ZINC01589617 is a potential candidate for biological tests.Communicated by Ramaswamy H. Sarma.
Assuntos
Doença de Alzheimer , Simulação de Dinâmica Molecular , Humanos , Simulação de Acoplamento Molecular , Secretases da Proteína Precursora do Amiloide , Doença de Alzheimer/tratamento farmacológicoRESUMO
DNA is a molecular target for the treatment of several diseases, including cancer, but there are few docking methodologies exploring the interactions between nucleic acids with DNA intercalating agents. Different docking methodologies, such as AutoDock Vina, DOCK 6, and Consensus, implemented into Molecular Architect (MolAr), were evaluated for their ability to analyze those interactions, considering visual inspection, redocking, and ROC curve. Ligands were refined by Parametric Method 7 (PM7), and ligands and decoys were docked into the minor DNA groove (PDB code: 1VZK). As a result, the area under the ROC curve (AUC-ROC) was 0.98, 0.88, and 0.99 for AutoDock Vina, DOCK 6, and Consensus methodologies, respectively. In addition, we proposed a machine learning model to determine the experimental ∆Tm value, which found a 0.84 R2 score. Finally, the selected ligands mono imidazole lexitropsin (42), netropsin (45), and N,N'-(1H-pyrrole-2,5-diyldi-4,1-phenylene)dibenzenecarboximidamide (51) were submitted to Molecular Dynamic Simulations (MD) through NAMD software to evaluate their equilibrium binding pose into the groove. In conclusion, the use of MolAr improves the docking results obtained with other methodologies, is a suitable methodology to use in the DNA system and was proven to be a valuable tool to estimate the ∆Tm experimental values of DNA intercalating agents.
RESUMO
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.