Virtual Screening Based on QSAR and Molecular Docking of Possible Inhibitors Targeting Chagas CYP51

Cotuá, José; Solano, Humberto Llinás; Cotes, Sandra

doi:10.1155/2021/6640624

Cited by 10 publications

(9 citation statements)

References 46 publications

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“…A particularly appealing yet somewhat less common approach to virtual screening is presented by QSAR equations derived from easy to calculate 1D, 2D and sometimes global 3D descriptors. Several such studies were reported in the literature, and in most cases, the descriptors were calculated for the ligands in their unbound states [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. Some of these efforts were summarized in several review articles [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Spiegel

Senderowitz

2021

IJMS

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Virtual screening (VS) is a well-established method in the initial stages of many drug and material design projects. VS is typically performed using structure-based approaches such as molecular docking, or various ligand-based approaches. Most docking tools were designed to be as global as possible, and consequently only require knowledge on the 3D structure of the biotarget. In contrast, many ligand-based approaches (e.g., 3D-QSAR and pharmacophore) require prior development of project-specific predictive models. Depending on the type of model (e.g., classification or regression), predictive ability is typically evaluated using metrics of performance on either the training set (e.g.,QCV2) or the test set (e.g., specificity, selectivity or QF1/F2/F32). However, none of these metrics were developed with VS in mind, and consequently, their ability to reliably assess the performances of a model in the context of VS is at best limited. With this in mind we have recently reported the development of the enrichment optimization algorithm (EOA). EOA derives QSAR models in the form of multiple linear regression (MLR) equations for VS by optimizing an enrichment-based metric in the space of the descriptors. Here we present an improved version of the algorithm which better handles active compounds and which also takes into account information on inactive (either known inactive or decoy) compounds. We compared the improved EOA in small-scale VS experiments with three common docking tools, namely, Glide-SP, GOLD and AutoDock Vina, employing five molecular targets (acetylcholinesterase, human immunodeficiency virus type 1 protease, MAP kinase p38 alpha, urokinase-type plasminogen activator, and trypsin I). We found that EOA consistently outperformed all docking tools in terms of the area under the ROC curve (AUC) and EF1% metrics that measured the overall and initial success of the VS process, respectively. This was the case when the docking metrics were calculated based on a consensus approach and when they were calculated based on two different sets of single crystal structures. Finally, we propose that EOA could be combined with molecular docking to derive target-specific scoring functions.

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Section: Introductionmentioning

confidence: 99%

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

Spiegel

Senderowitz

2021

IJMS

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“…CYP51 inhibits the sterol synthesis, which is lethal to the parasite. Ravuconazole and posaconazole act through the coordination of nitrogen with heme iron into the binding cavity of CYP51 [ 59 ]. Studies on animal models found that posaconazole could be used for the treatment of acute and chronic CD [ 11 ].…”

Section: Resultsmentioning

confidence: 99%

A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets

et al. 2023

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Chagas disease (CD) is endemic in large parts of Central and South America, as well as in Texas and the southern regions of the United States. Successful parasites, such as the causative agent of CD, Trypanosoma cruzi have adapted to specific hosts during their phylogenesis. In this work, we have assembled an interactive network of the complex relations that occur between molecules within T. cruzi. An expert curation strategy was combined with a text-mining approach to screen 10,234 full-length research articles and over 200,000 abstracts relevant to T. cruzi. We obtained a scale-free network consisting of 1055 nodes and 874 edges, and composed of 838 proteins, 43 genes, 20 complexes, 9 RNAs, 36 simple molecules, 81 phenotypes, and 37 known pharmaceuticals. Further, we deployed an automated docking pipeline to conduct large-scale docking studiesinvolving several thousand drugs and potential targets to identify network-based binding propensities. These experiments have revealed that the existing FDA-approved drugs benznidazole (Bz) and nifurtimox (Nf) show comparatively high binding energies to the T. cruzi network proteins (e.g., PIF1 helicase-like protein, trans-sialidase), when compared with control datasets consisting of proteins from other pathogens. We envisage this work to be of value to those interested in finding new vaccines for CD, as well as drugs against the T. cruzi parasite.

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“…The model function is trained using data from all molecules except one during the validation step. The properties of the model were then used to predict the pIC 50 for the chemical that wasn't included in the study [15][16][17][18][19]. The quality of the suggested QSAR models was assessed using statistical indicators such as Q 2 (Cross-validated correlation coe cient), R 2 (Regression coe cient), SD (Standard deviation) and SE (Standard error):…”

Section: Model Validationmentioning

confidence: 99%

QSAR modelling, ADMET prediction, molecular docking and molecular dynamics studies of novel 2-amino thiazole derivatives as Aurora kinase inhibitors

Bathula

Murugesan

Ilango

2023

Preprint

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The Aurora kinase is implicated in tumor growth and several small medicines that target the Aurora kinase have been found to have strong anti-cancer effects. Quantitative structure-activity relationship (QSAR) studies on 2-amino thiazole compounds for Aurora kinase inhibitory action targeting breast cancer were conducted out by QSARINS in this study. With statistical values R2 = 0.6102, CCCtr = 0.7580, Q2 LOO = 0.6875, Q2LMO = 0.6624, CCCcv = 0.6535, R2ext = 0.7735 and CCCext = 0.8383. Best top two models created comprise five variables: Estate VSA5, PSA, MoRSEP3, MATSp5 and RDFC24. Atomic charges, atomic volume and Sanderson's electronegativity all play a part in the design and optimization of the lead. In silico docking study was performed AutoDock v4.2.6. There are eight novel compounds have been designed, the residues from the active site region, which contains both hydrophilic and hydrophobic regions, interacted with the best active site pockets of 1MQ4, predicted active molecules 1a, 2a, 3e, 4d, 5d and 6d. Hydrophobic and hydrophilic stable contacts were seen in the active site of compound 1a in Desmond's molecular dynamics simulation investigations lasting 100 ns. The stable binding of compound 1a with the 1MQ4 structure was verified by RMSD, RMSF, RoG, H-bond and SASA analyses. We suggest that the proposed compound 1a can be used as the best theoretical lead for further experimental investigation of selective inhibition. Furthermore, newly designed compounds exhibited good ADMET properties. The study will result in the development of new breast cancer inhibitors.

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Virtual Screening Based on QSAR and Molecular Docking of Possible Inhibitors Targeting Chagas CYP51

Cited by 10 publications

References 46 publications

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

A Data-Driven Approach to Construct a Molecular Map of Trypanosoma cruzi to Identify Drugs and Vaccine Targets

QSAR modelling, ADMET prediction, molecular docking and molecular dynamics studies of novel 2-amino thiazole derivatives as Aurora kinase inhibitors

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