Automating Molecular Docking with Explicit Receptor Flexibility Using Scientific Workflows

Machado, Karina; Schroeder, E.; Ruiz, Duncan D.; Souza, Osmar Norberto de

doi:10.1007/978-3-540-73731-5_1

Cited by 10 publications

(20 citation statements)

References 19 publications

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“…In this work, we chose to model the explicit receptor flexibility by performing a series of molecular docking experiments considering in each one a different receptor snapshot derived from a MD simulation [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…The 3,100 InhA receptor conformations (or snapshots) were obtained from a MD simulation trajectory as described in [ 31 ]. Considering this set of snapshots we performed molecular docking experiments [ 24 ] for each of the four ligands described. After the execution of over 3,000 docking experiments, for each ligand, as a result we have a large amount of data that need to be dissected to produce useful information about the receptor-ligands interactions.…”

Section: Introductionmentioning

confidence: 99%

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Mining flexible-receptor docking experiments to select promising protein receptor snapshots

et al. 2010

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BackgroundMolecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts.ResultsWe previously docked four ligands (NADH, TCL, PIF and ETH) to 3,100 conformations of the InhA receptor from M. tuberculosis. Based on the receptor residues-ligand distances we preprocessed all docking results to generate appropriate input to mine data. Data preprocessing was done by calculating the shortest interatomic distances between the ligand and the receptor’s residues for each docking result. They were the predictive attributes. The target attribute was the estimated free-energy of binding (FEB) value calculated by the AutodDock3.0.5 software. The mining inputs were submitted to the M5P model tree algorithm. It resulted in short and understandable trees. On the basis of the correlation values, for NADH, TCL and PIF we obtained more than 95% correlation while for ETH, only about 60%. Post processing the generated model trees for each of its linear models (LMs), we calculated the average FEB for their associated instances. From these values we considered a LM as representative if its average FEB was smaller than or equal the average FEB of the test set. The instances in the selected LMs were considered the most promising snapshots. It totalized 1,521, 1,780, 2,085 and 902 snapshots, for NADH, TCL, PIF and ETH respectively.ConclusionsBy post processing the generated model trees we were able to propose a criterion of selection of linear models which, in turn, is capable of selecting a set of promising receptor conformations. As future work we intend to go further and use these results to elaborate a strategy to preprocess the receptors 3-D spatial conformation in order to predict FEB values. Besides, we intend to select other compounds, among the million catalogued, that may be promising as new drug candidates for our particular protein receptor target.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining flexible-receptor docking experiments to select promising protein receptor snapshots

et al. 2010

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“…In a FFR model, as we [28] and others [25,26] proposed, the CPU time to span a database like ZINC should explode to an unachievable cost. Hence, the necessity to reduce the CPU time for docking simulations using a FFR model.…”

Section: Resultsmentioning

confidence: 99%

FReDoWS: a method to automate molecular docking simulations with explicit receptor flexibility and snapshots selection

et al. 2011

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BackgroundIn silico molecular docking is an essential step in modern drug discovery when driven by a well defined macromolecular target. Hence, the process is called structure-based or rational drug design (RDD). In the docking step of RDD the macromolecule or receptor is usually considered a rigid body. However, we know from biology that macromolecules such as enzymes and membrane receptors are inherently flexible. Accounting for this flexibility in molecular docking experiments is not trivial. One possibility, which we call a fully-flexible receptor model, is to use a molecular dynamics simulation trajectory of the receptor to simulate its explicit flexibility. To benefit from this concept, which has been known since 2000, it is essential to develop and improve new tools that enable molecular docking simulations of fully-flexible receptor models.ResultsWe have developed a Flexible-Receptor Docking Workflow System (FReDoWS) to automate molecular docking simulations using a fully-flexible receptor model. In addition, it includes a snapshot selection feature to facilitate acceleration the virtual screening of ligands for well defined disease targets. FReDoWS usefulness is demonstrated by investigating the docking of four different ligands to flexible models of Mycobacterium tuberculosis’ wild type InhA enzyme and mutants I21V and I16T. We find that all four ligands bind effectively to this receptor as expected from the literature on similar, but wet experiments.ConclusionsA work that would usually need the manual execution of many computer programs, and the manipulation of thousands of files, was efficiently and automatically performed by FReDoWS. Its friendly interface allows the user to change the docking and execution parameters. Besides, the snapshot selection feature allowed the acceleration of docking simulations. We expect FReDoWS to help us explore more of the role flexibility plays in receptor-ligand interactions. FReDoWS can be made available upon request to the authors.

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“…We name this type of receptor representation fully flexible-receptor (FFR) model [6,7], and we investigate this methodology with target receptor InhA enzyme from Mycobacterium tuberculosis [8] (Mtb), which was modeled as a set of 3,100 snapshots derived from a 3.1 ns MD simulation trajectory [9]. For that, we generated molecular docking data sets with data from docking simulations of FFR-InhA [10] to six different ligands: nicotinamide adenine dinucleotide ( NADH ) [8], triclosan ( TCL ) [11], pentacyano(isoniazid)ferrate(II) ( PIF ) [12], ethionamide ( ETH ) [13], Isoniazid ( INH ) [14], and Triclosan derivative 20 ( JPM ) [15]. Explicitly including the receptor flexibility in docking simulations is computationally demanding and generates large amounts of data, which need to be analyzed and interpreted.…”

Section: Introductionmentioning

confidence: 99%

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

et al. 2012

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BackgroundThis paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance.ResultsThe empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application.ConclusionsWe conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

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Automating Molecular Docking with Explicit Receptor Flexibility Using Scientific Workflows

Cited by 10 publications

References 19 publications

Mining flexible-receptor docking experiments to select promising protein receptor snapshots

Mining flexible-receptor docking experiments to select promising protein receptor snapshots

FReDoWS: a method to automate molecular docking simulations with explicit receptor flexibility and snapshots selection

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

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