In silico virtual screening approaches for anti-viral drug discovery.
Ontology highlight
ABSTRACT:
SUBMITTER: Murgueitio MS
PROVIDER: S-EPMC7105918 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Structure-based drug discovery has emerged as a powerful tool in computational drug discovery and has gained rapid acceleration due to the development of better algorithms for high-end computation in an affordable time. Molecular docking and virtual screening methods are routinely used for the purpose but computing the ligand binding energies with inbuilt scoring functions accurately is still a limitation. Although, MMPBSA and MMGBSA are routinely employed tools for achieving accurate binding free energies, they are applied on well-equilibrated explicitly solvated systems and are computationally expensive and time-consuming. This study compares different post-processing protocols performed on an in-silico screened benchmarked P. falciparum Dihydrofolate reductase (PfDHFR) dataset with AutoDockVina. The docked and implicitly solvated complexes were subjected to (1) rescoring, (2) energy minimization and (3) Binding Estimation After Refinement (BEAR) algorithm. Subsequently, binding free energies were computed using three different tools-MMPBSA, MMGBSA and "g_mmpbsa". Surprisingly, rescoring alone displays lower accuracy than the inherent scoring function of the AutoDockVina. However, encouraging results were seen after post-processing with the other two protocols. The results suggest that MMPBSA applied on energy minimized conformations is able to achieve 42-fold reductions in computational time as opposed to the BEAR algorithm with comparable accuracy.
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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.
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Project description:This review aims to summarize the latest efforts performed in the search for novel chemical entities such as Toll-like receptor (TLR) modulators by means of virtual screening techniques. This is an emergent research field with only very recent (and successful) contributions. Identification of drug-like molecules with potential therapeutic applications for the treatment of a variety of TLR-regulated diseases has attracted considerable interest due to the clinical potential. Additionally, the virtual screening databases and computational tools employed have been overviewed in a descriptive way, widening the scope for researchers interested in the field.
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Project description:The discovery of new therapeutic options against Trypanosoma cruzi, the causative agent of Chagas disease, stands as a fundamental need. Currently, there are only two drugs available to treat this neglected disease, which represents a major public health problem in Latin America. Both available therapies, benznidazole and nifurtimox, have significant toxic side effects and their efficacy against the life-threatening symptomatic chronic stage of the disease is variable. Thus, there is an urgent need for new, improved anti-T. cruzi drugs. With the objective to reliably accelerate the drug discovery process against Chagas disease, several advances have been made in the last few years. Availability of engineered reporter gene expressing parasites triggered the development of phenotypic in vitro assays suitable for high throughput screening (HTS) as well as the establishment of new in vivo protocols that allow faster experimental outcomes. Recently, automated high content microscopy approaches have also been used to identify new parasitic inhibitors. These in vitro and in vivo early drug discovery approaches, which hopefully will contribute to bring better anti-T. cruzi drug entities in the near future, are reviewed here.
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Project description:BackgroundEpstein-Barr Virus (EBV) latent infection is associated with several human malignancies and is a causal agent of lymphoproliferative diseases during immunosuppression. While inhibitors of herpesvirus DNA polymerases, like gancyclovir, reduce EBV lytic cycle infection, these treatments have limited efficacy for treating latent infection. EBNA1 is an EBV-encoded DNA-binding protein required for viral genome maintenance during latent infection.MethodologyHere, we report the identification of a new class of small molecules that inhibit EBNA1 DNA binding activity. These compounds were identified by virtual screening of 90,000 low molecular mass compounds using computational docking programs with the solved crystal structure of EBNA1. Four structurally related compounds were found to inhibit EBNA1-DNA binding in biochemical assays with purified EBNA1 protein. Compounds had a range of 20-100 microM inhibition of EBNA1 in fluorescence polarization assays and were further validated for inhibition using electrophoresis mobility shift assays. These compounds exhibited no significant inhibition of an unrelated DNA binding protein. Three of these compounds inhibited EBNA1 transcription activation function in cell-based assays and reduced EBV genome copy number when incubated with a Burkitt lymphoma cell line.ConclusionsThese experiments provide a proof-of-principle that virtual screening can be used to identify specific inhibitors of EBNA1 that may have potential for treatment of EBV latent infection.
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