Virtual drug screening for prion diseases: A valuable step?
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SUBMITTER: Cordeiro Y
PROVIDER: S-EPMC4972553 | biostudies-literature | 2016 Jul
REPOSITORIES: biostudies-literature
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Project description:Prion diseases such as Creutzfeldt-Jakob disease (CJD) are incurable and rapidly fatal neurodegenerative diseases. Because prion protein (PrP) is necessary for prion replication but dispensable for the host, we developed the PrP-FRET-enabled high throughput assay (PrP-FEHTA) to screen for compounds that decrease PrP expression. We screened a collection of drugs approved for human use and identified astemizole and tacrolimus, which reduced cell-surface PrP and inhibited prion replication in neuroblastoma cells. Tacrolimus reduced total cellular PrP levels by a nontranscriptional mechanism. Astemizole stimulated autophagy, a hitherto unreported mode of action for this pharmacophore. Astemizole, but not tacrolimus, prolonged the survival time of prion-infected mice. Astemizole is used in humans to treat seasonal allergic rhinitis in a chronic setting. Given the absence of any treatment option for CJD patients and the favorable drug characteristics of astemizole, including its ability to cross the blood-brain barrier, it may be considered as therapy for CJD patients and for prophylactic use in familial prion diseases. Importantly, our results validate PrP-FEHTA as a method to identify antiprion compounds and, more generally, FEHTA as a unique drug discovery platform.
| S-EPMC3637718 | biostudies-literature
Project description:Over the past two decades biologists and bioinformaticians have unearthed substantial evidence supporting a role for G-quadruplexes as important mediators of biological processes. This includes telomere damage signaling, transcriptional activity, and splicing. Both their structural heterogeneity and their abundance in oncogene promoters makes them ideal targets for drug discovery. Currently, there are hundreds of deposited DNA and RNA quadruplex atomic structures which have allowed researchers to begin using in silico drug screening approaches to develop novel stabilizing ligands. Here we provide a review of the past decade of G-quadruplex virtual drug discovery approaches and campaigns. With this we introduce relevant virtual screening platforms followed by a discussion of best practices to assist future G4 VS campaigns.
| S-EPMC6134840 | biostudies-literature
Project description:HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, virtual screening and docking methods increase the potential of rational drug design The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer's disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A -R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.
| S-EPMC4901078 | biostudies-literature
Project description:Drug screening is an important part of the drug development pipeline for the pharmaceutical industry. Traditional, lab-based methods are increasingly being augmented with computational methods, ranging from simple molecular similarity searches through more complex pharmacophore matching to more computationally intensive approaches, such as molecular docking. The latter simulates the binding of drug molecules to their targets, typically protein molecules. In this work, we describe BUDE, the Bristol University Docking Engine, which has been ported to the OpenCL industry standard parallel programming language in order to exploit the performance of modern many-core processors. Our highly optimized OpenCL implementation of BUDE sustains 1.43 TFLOP/s on a single Nvidia GTX 680 GPU, or 46% of peak performance. BUDE also exploits OpenCL to deliver effective performance portability across a broad spectrum of different computer architectures from different vendors, including GPUs from Nvidia and AMD, Intel's Xeon Phi and multi-core CPUs with SIMD instruction sets.
| S-EPMC4425459 | biostudies-other
Project description:Computationally identifying new targets for existing drugs has drawn much attention in drug repurposing due to its advantages over de novo drugs, including low risk, low costs, and rapid pace. To facilitate the drug repurposing computation, we constructed an automated and parameter-free virtual screening server, namely DrugRep, which performed molecular 3D structure construction, binding pocket prediction, docking, similarity comparison and binding affinity screening in a fully automatic manner. DrugRep repurposed drugs not only by receptor-based screening but also by ligand-based screening. The former automatically detected possible binding pockets of the receptor with our cavity detection approach, and then performed batch docking over drugs with a widespread docking program, AutoDock Vina. The latter explored drugs using seven well-established similarity measuring tools, including our recently developed ligand-similarity-based methods LigMate and FitDock. DrugRep utilized easy-to-use graphic interfaces for the user operation, and offered interactive predictions with state-of-the-art accuracy. We expect that this freely available online drug repurposing tool could be beneficial to the drug discovery community. The web site is http://cao.labshare.cn/drugrep/ .
| S-EPMC9549438 | biostudies-literature
Project description:Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.
| S-EPMC10751785 | biostudies-literature
Project description:Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.
| S-EPMC1978274 | biostudies-other
Project description:Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever-increasing sizes of libraries of drug-like compounds and separating true positives from false positives. Here, we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally, a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome, and a drug target for breast cancer.
| S-EPMC8478848 | biostudies-literature
Project description:Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.:
| S-EPMC7105918 | biostudies-literature
Project description:Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.
| S-EPMC11377542 | biostudies-literature