Browsing by Author "Dhanik, Ankur"

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    AutoDock-based incremental docking protocol improves docking of large ligands
    (2012-10-07) Dhanik, Ankur; Kavraki, Lydia E.; McMurray, John S.
    It is well known that computer-aided docking of large ligands, with many rotatable bonds, is extremely difficult. AutoDock is a widely used docking program that can dock small ligands, with up to 5 or 6 rotatable bonds, accurately and quickly. Docking of larger ligands, however, is not very accurate and is computationally expensive. In this paper we present an AutoDock-based incremental docking protocol which docks a large ligand to its target protein in increments. A fragment of the large ligand is first chosen and then docked. Best docked conformations are incrementally grown and docked again, and this process is repeated until all the atoms of the ligand are docked. Each docking operation is performed using AutoDock. However, in each docking operation only a small number of rotatable bonds are allowed to rotate. We did a systematic docking study on a dataset of 73 protein-ligand complexes derived from the core set of PDBbind database. The number of rotatable bonds in the ligands vary from 7 to 30. Multiple docking experiments were done to evaluate the docking performance of the incremental protocol in comparison to AutoDock’s standard protocol. Results from the study show that, on average over the dataset, docking of large ligands using our incremental protocol is upto 23-fold computationally faster than docking using AutoDock’s standard protocol and also has better or comparable accuracy. We propose that, for docking large ligands, our incremental protocol can be used as an alternative to AutoDock’s standard protocol.
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    Binding Modes of Peptidomimetics Designed to Inhibit STAT3
    (Public Library of Science, 2012) Dhanik, Ankur; McMurray, John S.; Kavraki, Lydia E.
    STAT3 is a transcription factor that has been found to be constitutively activated in a number of human cancers. Dimerization of STAT3 via its SH2 domain and the subsequent translocation of the dimer to the nucleus leads to transcription of anti-apoptotic genes. Prevention of the dimerization is thus an attractive strategy for inhibiting the activity of STAT3. Phosphotyrosine-based peptidomimetic inhibitors, which mimic pTyr-Xaa-Yaa-Gln motif and have strong to weak binding affinities, have been previously investigated. It is well-known that structures of protein-inhibitor complexes are important for understanding the binding interactions and designing stronger inhibitors. Experimental structures of inhibitors bound to the SH2 domain of STAT3 are, however, unavailable. In this paper we describe a computational study that combined molecular docking and molecular dynamics to model structures of 12 peptidomimetic inhibitors bound to the SH2 domain of STAT3. A detailed analysis of the modeled structures was performed to evaluate the characteristics of the binding interactions. We also estimated the binding affinities of the inhibitors by combining MMPB/GBSA-based energies and entropic cost of binding. The estimated affinities correlate strongly with the experimentally obtained affinities. Modeling results show binding modes that are consistent with limited previous modeling studies on binding interactions involving the SH2 domain and phosphotyrosine(pTyr)-based inhibitors. We also discovered a stable novel binding mode that involves deformation of two loops of the SH2 domain that subsequently bury the C-terminal end of one of the stronger inhibitors. The novel binding mode could prove useful for developing more potent inhibitors aimed at preventing dimerization of cancer target protein STAT3.
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    DINC: A new AutoDock-based protocol for docking large ligands
    (BioMed Central, 2013) Dhanik, Ankur; McMurray, John S.; Kavraki, Lydia E.
    Background: Using the popular program AutoDock, computer-aided docking of small ligands with 6 or fewer rotatable bonds, is reasonably fast and accurate. However, docking large ligands using AutoDock's recommended standard docking protocol is less accurate and computationally slow. Results: In our earlier work, we presented a novel AutoDock-based incremental protocol (DINC) that addresses the limitations of AutoDock's standard protocol by enabling improved docking of large ligands. Instead of docking a large ligand to a target protein in one single step as done in the standard protocol, our protocol docks the large ligand in increments. In this paper, we present three detailed examples of docking using DINC and compare the docking results with those obtained using AutoDock's standard protocol. We summarize the docking results from an extended docking study that was done on 73 protein-ligand complexes comprised of large ligands. We demonstrate not only that DINC is up to 2 orders of magnitude faster than AutoDock's standard protocol, but that it also achieves the speed-up without sacrificing docking accuracy. We also show that positional restraints can be applied to the large ligand using DINC: this is useful when computing a docked conformation of the ligand. Finally, we introduce a webserver for docking large ligands using DINC. Conclusions: Docking large ligands using DINC is significantly faster than AutoDock's standard protocol without any loss of accuracy. Therefore, DINC could be used as an alternative protocol for docking large ligands. DINC has been implemented as a webserver and is available at http://?dinc.?kavrakilab.?org. Applications such as therapeutic drug design, rational vaccine design, and others involving large ligands could benefit from DINC and its webserver implementation.
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    Targeting the Src Homology 2 (SH2) Domain of Signal Transducer and Activator of Transcription 6 (STAT6) with Cell-Permeable, Phosphatase-Stable Phosphopeptide Mimics Potently Inhibits Tyr641 Phosphorylation and Transcriptional Activity
    (American Chemical Society, 2015) Mandal, Pijus K.; Morlacchi, Pietro; Knight, J. Morgan; Link, Todd M.; Lee, Gilbert R. IV; Nurieva, Roza; Singh, Divyendu; Dhanik, Ankur; Kavraki, Lydia; Corry, David B.; Ladbury, John E.; McMurray, John S.
    Signal transducer and activator of transcription 6 (STAT6) transmits signals from cytokines IL-4 and IL-13 and is activated in allergic airway disease. We are developing phosphopeptide mimetics targeting the SH2 domain of STAT6 to block recruitment to phosphotyrosine residues on IL-4 or IL-13 receptors and subsequent Tyr641 phosphorylation to inhibit the expression of genes contributing to asthma. Structure–affinity relationship studies showed that phosphopeptides based on Tyr631 from IL-4Rα bind with weak affinity to STAT6, whereas replacing the pY+3 residue with simple aryl and alkyl amides resulted in affinities in the mid to low nM range. A set of phosphatase-stable, cell-permeable prodrug analogues inhibited cytokine-stimulated STAT6 phosphorylation in both Beas-2B human airway cells and primary mouse T-lymphocytes at concentrations as low as 100 nM. IL-13-stimulated expression of CCL26 (eotaxin-3) was inhibited in a dose-dependent manner, demonstrating that targeting the SH2 domain blocks both phosphorylation and transcriptional activity of STAT6.