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      Drug Design, Development and Therapy (submit here)

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      Could the FDA-approved anti-HIV PR inhibitors be promising anticancer agents? An answer from enhanced docking approach and molecular dynamics analyses

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          Abstract

          Based on experimental data, the anticancer activity of nelfinavir (NFV), a US Food and Drug Administration (FDA)-approved HIV-1 protease inhibitor (PI), was reported. Nevertheless, the mechanism of action of NFV is yet to be verified. It was hypothesized that the anticancer activity of NFV is due to its inhibitory effect on heat shock protein 90 (Hsp90), a promising target for anticancer therapy. Such findings prompted us to investigate the potential anticancer activity of all other FDA-approved HIV-1 PIs against human Hsp90. To accomplish this, “loop docking” – an enhanced in-house developed molecular docking approach – followed by molecular dynamic simulations and postdynamic analyses were performed to elaborate on the binding mechanism and relative binding affinities of nine FDA-approved HIV-1 PIs against human Hsp90. Due to the lack of the X-ray crystal structure of human Hsp90, homology modeling was performed to create its 3D structure for subsequent simulations. Results showed that NFV has better binding affinity (Δ G =−9.2 kcal/mol) when compared with other PIs: this is in a reasonable accordance with the experimental data (IC50 3.1 μM). Indinavir, saquinavir, and ritonavir have close binding affinity to NFV (Δ G =−9.0, −8.6, and −8.5 kcal/mol, respectively). Per-residue interaction energy decomposition analysis showed that hydrophobic interaction (most importantly with Val534 and Met602) played the most predominant role in drug binding. To further validate the docking outcome, 5 ns molecular dynamic simulations were performed in order to assess the stability of the docked complexes. To our knowledge, this is the first account of detailed computational investigations aimed to investigate the potential anticancer activity and the binding mechanism of the FDA-approved HIV PIs binding to human Hsp90. Information gained from this study should also provide a route map toward the design, optimization, and further experimental investigation of potential derivatives of PIs to treat HER2+ breast cancer.

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          Most cited references34

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          MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity

          Background MTML-msBayes uses hierarchical approximate Bayesian computation (HABC) under a coalescent model to infer temporal patterns of divergence and gene flow across codistributed taxon-pairs. Under a model of multiple codistributed taxa that diverge into taxon-pairs with subsequent gene flow or isolation, one can estimate hyper-parameters that quantify the mean and variability in divergence times or test models of migration and isolation. The software uses multi-locus DNA sequence data collected from multiple taxon-pairs and allows variation across taxa in demographic parameters as well as heterogeneity in DNA mutation rates across loci. The method also allows a flexible sampling scheme: different numbers of loci of varying length can be sampled from different taxon-pairs. Results Simulation tests reveal increasing power with increasing numbers of loci when attempting to distinguish temporal congruence from incongruence in divergence times across taxon-pairs. These results are robust to DNA mutation rate heterogeneity. Estimating mean divergence times and testing simultaneous divergence was less accurate with migration, but improved if one specified the correct migration model. Simulation validation tests demonstrated that one can detect the correct migration or isolation model with high probability, and that this HABC model testing procedure was greatly improved by incorporating a summary statistic originally developed for this task (Wakeley's ΨW ). The method is applied to an empirical data set of three Australian avian taxon-pairs and a result of simultaneous divergence with some subsequent gene flow is inferred. Conclusions To retain flexibility and compatibility with existing bioinformatics tools, MTML-msBayes is a pipeline software package consisting of Perl, C and R programs that are executed via the command line. Source code and binaries are available for download at http://msbayes.sourceforge.net/ under an open source license (GNU Public License).
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            Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

            Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.
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              Molecular dynamics simulations and drug discovery

              This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.
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                Author and article information

                Journal
                Drug Des Devel Ther
                Drug Des Devel Ther
                Drug Design, Development and Therapy
                Drug Design, Development and Therapy
                Dove Medical Press
                1177-8881
                2015
                13 November 2015
                : 9
                : 6055-6065
                Affiliations
                Molecular Modelling and Drug Design Lab, School of Health Sciences, Westville Campus, University of KwaZulu-Natal, Durban, South Africa
                Author notes
                Correspondence: Mahmoud ES Soliman, Molecular Modelling and Drug Design Lab, School of Health Sciences, Westville Campus, University of KwaZulu-Natal, University Road, Westville 3600, South Africa, Tel +27 31 260 7413, Fax +27 31 26 0779, Email soliman@ 123456ukzn.ac.za
                Article
                dddt-9-6055
                10.2147/DDDT.S87653
                4654555
                26622167
                7277dce6-5600-430c-a5c0-a559cfd7cd0b
                © 2015 Arodola and Soliman. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License

                The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

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                Categories
                Original Research

                Pharmacology & Pharmaceutical medicine
                binding free energy,loop docking,molecular dynamics,hiv-1 protease inhibitors,anticancer

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