Large-scale computational drug repositioning to find treatments for rare diseases

Govindaraj, Rajiv Gandhi; Naderi, Misagh; Singha, Manali; Lemoine, Jeffrey Mitchell; Bryliński, Michał

doi:10.1038/s41540-018-0050-7

Cited by 35 publications

(19 citation statements)

References 84 publications

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“…For targets with more than 400 amino acid residues, the performance of Vina was significantly lower (average EF1% of only 6.1, AUC of 65. 4) AutoDock exhibited a more uniform behavior, with average EF1% values in the range 7.9-9.4 for small (less than 250 aa) and large targets (more than 400 aa), resulting in an improved performance over Vina for the small targets (<250 aa) and the large targets (>400 aa).…”

Section: Evaluation Of the Performance Of Autodock And Vinamentioning

confidence: 99%

“…Protein-ligand docking and virtual screening are two of the most used techniques in this field that continue to show promise in hit identification and subsequent optimization [1]. They are also helpful tools for drug repositioning [2][3][4]. These methods are effective and fast, and allow researchers to evaluate large virtual databases of molecular compounds as a first attempt to guide the selection of more limited sets of compounds for experimental testing.…”

Section: Introductionmentioning

confidence: 99%

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Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

2019

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AutoDock and Vina are two of the most widely used protein-ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.of the poses previously created, discriminating between the best and not so good alternatives [1,5]. This estimate, which in some cases is a prediction of the free energy of binding, must be able to discriminate between molecules that bind to the target and those that do not [23]. When looking at the two enantiomers, for example, it is still not possible to identify the most active form with most of the scoring functions used by the most common docking software [24].Even with all the significant improvements in computational power and docking software, considering all interactions that happen when a ligand binds to its target is an extremely challenging task. In order to be rigorous, the scoring functions would have to be much more complex, involve quantum calculations and, thus, these assays would turn out to be considerably expensive and time-consuming. When applying a virtual screening protocol, one wishes to screen very large databases of compounds in a relatively small period of time and, therefore, scoring functions are often simplified to improve the speed and cost of the computational screenings [6]. These simplifications come with a cost in accuracy, which might not be problematic for one ligand-target situation but takes a much more challenging scope when talking about virtual screening of thousands or millions of compounds [5].The goal of virtual screening (VS) is to guide the selection of molecules for experimental testing. In these assays, millions of compounds are docked into one specific target and only a selection of the top scores proceeds for experimental testing. If a scoring functions fails to identify a potential strong binder, then, it remains hidden among those million compounds, despite their pharmacological potential. In fact, that is one of the main problems in VS, the false negatives, or molecules that the docki...

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Section: Evaluation Of the Performance Of Autodock And Vinamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

2019

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“…Here, we have provided details of at least 12 developmental proteins with the potential to be targets for drug repositioning and the design of novel anthelmintic drugs. For instance, Mark2 and MAGI2 already have orthologs with available structural data that could be used for structural modeling, binding site prediction, and drug-protein molecular docking studies [44]. Likewise, the strobilation-related proteins identified as being cestode-exclusive could also constitute an interesting set of proteins for functional studies and for design-specific therapeutic approaches to cestodiases.…”

Section: Discussionmentioning

confidence: 99%

Cestode strobilation: prediction of developmental genes and pathways

et al. 2020

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Background: Cestoda is a class of endoparasitic worms in the flatworm phylum (Platyhelminthes). During the course of their evolution cestodes have evolved some interesting aspects, such as their increased reproductive capacity. In this sense, they have serial repetition of their reproductive organs in the adult stage, which is often associated with external segmentation in a developmental process called strobilation. However, the molecular basis of strobilation is poorly understood. To assess this issue, an evolutionary comparative study among strobilated and non-strobilated flatworm species was conducted to identify genes and proteins related to the strobilation process. Results: We compared the genomic content of 10 parasitic platyhelminth species; five from cestode species, representing strobilated parasitic platyhelminths, and five from trematode species, representing non-strobilated parasitic platyhelminths. This dataset was used to identify 1813 genes with orthologues that are present in all cestode (strobilated) species, but absent from at least one trematode (non-strobilated) species. Developmentrelated genes, along with genes of unknown function (UF), were then selected based on their transcriptional profiles, resulting in a total of 34 genes that were differentially expressed between the larval (pre-strobilation) and adult (strobilated) stages in at least one cestode species. These 34 genes were then assumed to be strobilation related; they included 12 encoding proteins of known function, with 6 related to the Wnt, TGF-β/BMP, or G-protein coupled receptor signaling pathways; and 22 encoding UF proteins. In order to assign function to at least some of the UF genes/proteins, a global gene co-expression analysis was performed for the cestode species Echinococcus multilocularis. This resulted in eight UF genes/proteins being predicted as related to developmental, reproductive, vesicle transport, or signaling processes. Conclusions: Overall, the described in silico data provided evidence of the involvement of 34 genes/proteins and at least 3 developmental pathways in the cestode strobilation process. These results highlight on the molecular mechanisms and evolution of the cestode strobilation process, and point to several interesting proteins as potential developmental markers and/or targets for the development of novel antihelminthic drugs.

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“…On that account, a diverse dataset of small, drug-like molecules bound to high-quality models with accurately annotated pockets provides an invaluable resource for drug repositioning employing sequence order-independent pocket matching algorithms [ 40 – 43 ]. It is noteworthy that computational drug repurposing has suggested new opportunities to combat tuberculosis [ 44 , 45 ], malaria [ 46 ], and rare diseases [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database

2018

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BackgroundThe structural information on proteins in their ligand-bound conformational state is invaluable for protein function studies and rational drug design. Compared to the number of available sequences, not only is the repertoire of the experimentally determined structures of holo-proteins limited, these structures do not always include pharmacologically relevant compounds at their binding sites. In addition, binding affinity databases provide vast quantities of information on interactions between drug-like molecules and their targets, however, often lacking structural data. On that account, there is a need for computational methods to complement existing repositories by constructing the atomic-level models of drug-protein assemblies that will not be determined experimentally in the near future.ResultsWe created eModel-BDB, a database of 200,005 comparative models of drug-bound proteins based on 1,391,403 interaction data obtained from the Binding Database and the PDB library of 31 January 2017. Complex models in eModel-BDB were generated with a collection of the state-of-the-art techniques, including protein meta-threading, template-based structure modeling, refinement and binding site detection, and ligand similarity-based docking. In addition to a rigorous quality control maintained during dataset generation, a subset of weakly homologous models was selected for the retrospective validation against experimental structural data recently deposited to the Protein Data Bank. Validation results indicate that eModel-BDB contains models that are accurate not only at the global protein structure level but also with respect to the atomic details of bound ligands.ConclusionsFreely available eModel-BDB can be used to support structure-based drug discovery and repositioning, drug target identification, and protein structure determination.

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Large-scale computational drug repositioning to find treatments for rare diseases

Cited by 35 publications

References 84 publications

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Cestode strobilation: prediction of developmental genes and pathways

eModel-BDB: a database of comparative structure models of drug-target interactions from the Binding Database

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