Virtual high-throughput ligand screening

Combined 4D-QSAR and target-based approaches for the determination of bioactive Isatin derivatives

The hybrid method of the Electron-Conformational Genetic Algorithm (EC-GA) was used to determine the pharmacophore groups and to estimate anticancer activity in isatin derivatives using a robust 4D-QSAR software (EMRE). To build the model, each compound is represented by a set of conformers rather than a single conformation. The Electron Conformational Matrix of Congruity (ECMC) is composed via EMRE software. Electron Conformational Submatrix of Activity (ECSA) was calculated by the comparison of these matrices. Genetic algorithm was used to select important variables to predict theoretical activity. The model with the best seven parameters produced satisfactory results. The E statistics technique was applied to the generated EC-GA model to evaluate the individual contribution of each of the descriptors on biological activity. The r² and q² values of the training set compounds were found to be 0.95 and 0.93, respectively. Because no previous 4D-QSAR studies on isatin derivatives have been conducted, this study is important in the development of new isatin derivatives. In this study, 27 isatin derivatives whose activities were estimated using the hybrid EC-GA method were also investigated through molecular docking and molecular dynamics simulations for their BCL-2 inhibitory activity.

Biparatopic Protein Nanoparticles for the Precision Therapy of CXCR4+ Cancers

Simple Summary

Aimed at minimizing side toxicities cancer therapies require appropriate functional vehicles at the nanoscale, for receptor-mediated tumor-targeted drug delivery. The aim of the present study was to explore the human peptide EPI-X4 as a CXCR4-targeting agent in self-assembled, protein-only nanoparticles. While the systemic tumor biodistribution of EPI-X4-based materials is modest, this peptide shows potent proapoptotic effects on CXCR4⁺ cancer cells. Interestingly, the in vivo selectivity of EPI-X4 was dramatically improved, once combined into biparatopic nanoparticles, with a second CXCR4 ligand, the peptide T22. Biparatopic nanoparticles promote a highly selective tumor destruction in a mouse model of human colorectal cancer, probably associated to the CXCR4 antagonist role of EPI-X4. This study not only validates a new human ligand of the tumoral marker CXCR4, but it also offers a novel strategy for the combination, in protein nanoparticles, of dual acting ligands of tumoral markers for highly selective drug delivery.

Abstract

The accumulated molecular knowledge about human cancer enables the identification of multiple cell surface markers as highly specific therapeutic targets. A proper tumor targeting could significantly avoid drug exposure of healthy cells, minimizing side effects, but it is also expected to increase the therapeutic index. Specifically, colorectal cancer has a particularly poor prognosis in late stages, being drug targeting an appropriate strategy to substantially improve the therapeutic efficacy. In this study, we have explored the potential of the human albumin-derived peptide, EPI-X4, as a suitable ligand to target colorectal cancer via the cell surface protein CXCR4, a chemokine receptor overexpressed in cancer stem cells. To explore the potential use of this ligand, self-assembling protein nanoparticles have been generated displaying an engineered EPI-X4 version, which conferred a modest CXCR4 targeting and fast and high level of cell apoptosis in tumor CXCR4⁺ cells, in vitro and in vivo. In addition, when EPI-X4-based building blocks are combined with biologically inert polypeptides containing the CXCR4 ligand T22, the resulting biparatopic nanoparticles show a dramatically improved biodistribution in mouse models of CXCR4⁺ human cancer, faster cell internalization and enhanced target cell death when compared to the version based on a single ligand. The generation of biparatopic materials opens exciting possibilities in oncotherapies based on high precision drug delivery based on the receptor CXCR4.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors

The excessive activity of acetylcholinesterase enzyme (AChE) causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration (FDA) approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer’s disease (AD) these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute (NCI) containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword “indol” were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship (QSAR) models. We then performed docking, molecular dynamics (MD) simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.

Novel AChE and BChE inhibitors using combined virtual screening, text mining and in vitro binding assays

In the current work, we developed a computational pipeline method for predicting the binding affinities of studied compounds at the specific target sites. Since many approved therapeutic compounds involve indole or indole-derivative rings, in the current study we focused compounds including these fingerprints. Initially, 212520 compounds were retrieved from Specs-SC library and after the conversion of IUPAC text file format, compounds that include 'indol' keyword (5194 compounds) were used in binary QSAR-based models to screen against a defined therapeutic activity "Alzheimer's disease" (AD). The molecules that have higher AD therapeutic activity values (>0.5) were then used in the 26 different toxicity-QSAR models. Binary QSAR models resulted 89 hits that have high AD therapeutic activity and no toxicity. Selected 89 molecules were then screened against acetylcholinesterase (AChE) targets using molecular docking and top-docking poses of compounds were used in initially short (10 ns) molecular dynamics (MD) simulations. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy calculations were performed for 89 ligands and tightly bound 17 ligands based on average MM/GBSA scores were selected for long (100 ns) MD simulations. The same protocol was also applied for the known 4 AChE inhibitors. Selected hits were also docked to the binding pocket of butyrylcholinesterase (BChE). Finally, based on MM/GBSA scores, as well as their corresponding docking scores and metabolite production profiles, 7 compounds were selected and their in vitro tests were performed. Out of 7 compounds, 6 of them showed μM-level inhibition for both AChE and BChE targets.Communicated by Ramaswamy H. Sarma.

Novel ligand-based docking; molecular dynamic simulations; and absorption, distribution, metabolism, and excretion approach to analyzing potential acetylcholinesterase inhibitors for Alzheimer's disease

Acetylcholinesterase (AChE) plays an important role in Alzheimer's disease (AD). The excessive activity of AChE causes various neuronal problems, particularly dementia and neuronal cell deaths. Generally, anti-AChE drugs induce some serious neuronal side effects in humans. Therefore, this study sought to identify alternative drug molecules from natural products with fewer side effects than those of conventional drugs for treating AD. To achieve this, we developed computational methods for predicting drug and target binding affinities using the Schrodinger suite. The target and ligand molecules were retrieved from established databases. The target enzyme has 539 amino acid residues in its sequence alignment. Ligand molecules of 20 bioactive molecules were obtained from different kinds of plants, after which we performed critical analyses such as molecular docking; molecular dynamic (MD) simulations; and absorption, distribution, metabolism, and excretion (ADME) analysis. In the docking studies, the natural compound rutin showed a superior docking score of −12.335 with a good binding energy value of −73.313 kcal/mol. Based on these findings, rutin and the target complex was used to perform MD simulations to analyze rutin stability at 30 ns. In conclusion, our study demonstrates that rutin is a superior drug candidate for AD. Therefore, we propose that this molecule is worth further investigation using in vitro studies.

The dye SYPRO orange binds to amylin amyloid fibrils but not pre-fibrillar intermediates

Amyloid deposition underlies a broad range of diseases including multiple neurodegenerative diseases, systemic amyloidosis and type-2 diabetes. Amyloid sensitive dyes, particularly thioflavin-T, are widely used to detect ex-vivo amyloid deposits, to monitor amyloid formation in vitro and to follow the kinetics of amyloid self-assembly. We show that the dye SYPRO-orange binds to amyloid fibrils formed by human amylin, the polypeptide responsible for islet amyloid formation in type-2 diabetes. No fluorescence enhancement is observed in the presence of pre-fibrillar species or in the presence of non-amyloidogenic rat amylin. The kinetics of human amylin amyloid formation can be monitored by SYPRO-orange fluorescence and match the time course determined with thioflavin-T assays. Thus, SYPRO-orange offers an alternative to thioflavin-T assays of amylin amyloid formation. The implications for the interpretation of SYPRO-orange-based assays of protein stability and protein-ligand interactions are discussed.