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  • 1.
    Carvalho, Alexandra T P
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Barrozo, Alexandre
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Doron, Dvir
    Kilshtain, Alexandra Vardi
    Major, Dan Thomas
    Kamerlin, Lynn Shina Caroline
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Challenges in computational studies of enzyme structure, function and dynamics2014In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 54, p. 62-79Article, review/survey (Refereed)
    Abstract [en]

    In this review we give an overview of the field of Computational enzymology. We start by describing the birth of the field, with emphasis on the work of the 2013 chemistry Nobel Laureates. We then present key features of the state-of-the-art in the field, showing what theory, accompanied by experiments, has taught us so far about enzymes. We also briefly describe computational methods, such as quantum mechanics-molecular mechanics approaches, reaction coordinate treatment, and free energy simulation approaches. We finalize by discussing open questions and challenges.

  • 2. Chen, Hongming
    et al.
    Winiwarter, Susanne
    Fridén, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Antonsson, Madeleine
    Engkvist, Ola
    In silico prediction of unbound brain-to-plasma concentration ratio using machine learning algorithms2011In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 29, no 8, p. 985-995Article in journal (Refereed)
    Abstract [en]

    Distribution over the blood-brain barrier (BBB) is an important parameter to consider for compounds that will be synthesized in a drug discovery project. Drugs that aim at targets in the central nervous system (CNS) must pass the BBB. In contrast, drugs that act peripherally are often optimised to minimize the risk of CNS side effects by restricting their potential to reach the brain. Historically, most prediction methods have focused on the total compound distribution between the blood plasma and the brain. However, recently it has been proposed that the unbound brain-to-plasma concentration ratio (K(p,uu,brain)) is more relevant. In the current study, quantitative K(p,uu,brain) prediction models have been built on a set of 173 in-house compounds by using various machine learning algorithms. The best model was shown to be reasonably predictive for the test set of 73 compounds (R(2) = 0.58). When used for qualitative prediction the model shows an accuracy of 0.85 (Kappa = 0.68). An additional external test set containing 111 marketed CNS active drugs was also classified with the model and 89% of these drugs were correctly predicted as having high brain exposure.

  • 3. Garcia, Juliana
    et al.
    Carvalho, Alexandra T. P.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Dourado, Daniel F. A. R.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Baptista, Paula
    Bastos, Maria de Lourdes
    Carvalho, Felix
    New in silico insights into the inhibition of RNAP II by alpha-amanitin and the protective effect mediated by effective antidotes2014In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 51, p. 120-127Article in journal (Refereed)
    Abstract [en]

    Poisonous alpha-amanitin-containing mushrooms are responsible for the major cases of fatalities after mushroom ingestion. alpha-Amanitin is known to inhibit the RNA polymerase II (RNAP II), although the underlying mechanisms are not fully understood. Benzylpenicillin, ceftazidime and silybin have been the most frequently used drugs in the management of alpha-amanitin poisoning, mostly based on empirical rationale. The present study provides an in silica insight into the inhibition of RNAP II by alpha-amanitin and also on the interaction of the antidotes on the active site of this enzyme. Docking and molecular dynamics (MD) simulations combined with molecular mechanics-generalized Born surface area method (MM-GBSA) were carried out to investigate the binding of alpha-amanitin and three antidotes benzylpenicillin, ceftazidime and silybin to RNAP II. Our results reveal that alpha-amanitin should affects RNAP II transcription by compromising trigger loop (TL) function. The observed direct interactions between alpha-amanitin and TL residues Leu1081, Asn1082, Thr1083, His1085 and G1y1088 alters the elongation process and thus contribute to the inhibition of RNAP II. We also present evidences that alpha-amanitin can interact directly with the bridge helix residues G1y819, Gly820 and Glu822, and indirectly with His816 and Phe815. This destabilizes the bridge helix, possibly causing RNAP II activity loss. We demonstrate that benzylpenicillin, ceftazidime and silybin are able to bind to the same site as alpha-amanitin, although not replicating the unique alpha-amanitin binding mode. They establish considerably less intermolecular interactions and the ones existing are essential confine to the bridge helix and adjacent residues. Therefore, the therapeutic effect of these antidotes does not seem to be directly related with binding to RNAP II. RNAP II alpha-amanitin binding site can be divided into specific zones with different properties providing a reliable platform for the structure-based drug design of novel antidotes for alpha-amatoxin poisoning. An ideal drug candidate should be a competitive RNAP II binder that interacts with Arg726, 11e756, Ala759, Gln760 and G1n767, but not with TL and bridge helix residues. 

  • 4. Iribarne, F.
    et al.
    Paulino, M.
    Aguilera, S.
    Tapia, Orlando
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry, Physical Chemistry.
    Assaying phenothiazine derivatives as trypanothione reductase and glutathione reductase inhibitors by theoretical docking and Molecular Dynamics studies2009In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 28, no 4, p. 371-381Article in journal (Refereed)
    Abstract [en]

    A theoretical docking study, conducted on a sample of previously reported phenothiazine derivatives, at the binding sites of Trypanosoma cruzi trypanothione reductase (TR) and human erythrocyte glutathione reductase (GR), examines interaction energies (affinities) towards the parasite enzyme to check for selectivity with respect to the human counterpart. Phenothiazine compounds were previously shown to be TR inhibitors. The analysis of data collected from the docking procedure was undertaken both from the numeric and graphical standpoints, including the comparison of force field, energies, molecular contacts and spatial location of the different orientations that ligands acquired at the binding sites. Molecular Dynamics simulations were also carried out for derivatives with known quantitative inhibition kinetics (Ki). The results indicate that (positively) charged phenothiazines attain larger interaction energies at TR active site, in line with previous experimental information. Suitable molecular size and shape is also needed to complement the electrostatic effect, as clearly evidenced by graphical analysis of output docked conformations. Docking energies values are reasonably well correlated with those obtained by Molecular Dynamics as well as with the experimental Ki values, confirming once again the validity of this type of scoring methods to rapidly assess ligand–receptor affinities. Alongside newly discovered classes of TR inhibitors, the promazine (N-alkylaminopropylphenothiazine) nucleus should still be considered when good candidates are sought as leaders for selective TR inhibition.

  • 5. Isaksen, Geir Villy
    et al.
    Andberg, Tor Arne Heim
    Åqvist, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Brandsdal, Bjorn Olav
    Qgui: A high-throughput interface for automated setup and analysis of free energy calculations and empirical valence bond simulations in biological systems2015In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 60, p. 15-23Article in journal (Refereed)
    Abstract [en]

    Structural information and activity data has increased rapidly for many protein targets during the last decades. In this paper, we present a high-throughput interface (Qgui) for automated free energy and empirical valence bond (EVB) calculations that use molecular dynamics (MD) simulations for conformational sampling. Applications to ligand binding using both the linear interaction energy (LIE) method and the free energy perturbation (FEP) technique are given using the estrogen receptor (ER alpha) as a model system. Examples of free energy profiles obtained using the EVB method for the rate-limiting step of the enzymatic reaction catalyzed by trypsin are also shown. In addition, we present calculation of high-precision Arrhenius plots to obtain the thermodynamic activation enthalpy and entropy with Qgui from running a large number of EVB simulations.

  • 6.
    Muthas, Daniel
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Sabnis, Yogesh A
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Lundborg, Magnus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Karlén, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering?: An investigation of the advantages and pitfalls of post-filtering2008In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 26, no 8, p. 1237-1251Article in journal (Refereed)
    Abstract [en]

    We have investigated the influence of post-filtering virtual screening results, with pharmacophoric features generated from an X-ray structure, on enrichment rates. This was performed using three docking softwares, zdock+, Surflex and FRED, as virtual screening tools and pharmacophores generated in UNITY from co-crystallized complexes. Sets of known actives along with 9997 pharmaceutically relevant decoy compounds were docked against six chemically diverse protein targets namely CDK2, COX2, ERalpha, fXa, MMP3, and NA. To try to overcome the inherent limitations of the well-known docking problem, we generated multiple poses for each compound. The compounds were first ranked according to their scores alone and enrichment rates were calculated using only the top scoring pose of each compound. Subsequently, all poses for each compound were passed through the different pharmacophores generated from co-crystallized complexes and the enrichment factors were re-calculated based on the top-scoring passing pose of each compound. Post-filtering with a pharmacophore generated from only one X-ray complex was shown to increase enrichment rates in all investigated targets compared to docking alone. This indicates that this is a general method, which works for diverse targets and different docking softwares.

  • 7.
    Sköld, Christian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Karlén, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Development of CoMFA models of affinity and selectivity to angiotensin II type-1 and type-2 receptors2007In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 26, no 1, p. 145-153Article in journal (Refereed)
    Abstract [en]

    The renin-angiotensin system (RAS) is of major importance in cardiovascular and renal regulation and has been an attractive target in drug discovery for a long time. The main receptors involved in the RAS are the Angiotensin type-1 (AT1) and type-2 (AT2) receptors, which are both activated by the endogenous octapeptide angiotensin II (AngII). This study describes the development of 3D-QSAR models for AT1 and AT2 receptor affinity and AT1/AT2 receptor selectivity using CoMFA. A data set of 244 compounds, based on the triazolinone and quinazolinone structural classes was compiled from the literature. Before CoMFA could be performed, an alignment rule for the two structural classes was defined using the pharmacophore-searching program DISCOtech. Models were validated using a test set obtained by dividing the data set into a training set and test set using hierarchical clustering, based on the CoMFA fields, AT1-, AT2-receptor affinities, and AT1/AT2 selectivity values. Predictive models with good statistics could be developed both for AT1 and AT2 receptor affinity as well as selectivity towards these receptors.

  • 8.
    Sköld, Christian
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Nikiforovich, Gregory
    Karlén, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Organic Pharmaceutical Chemistry.
    Modeling binding modes of angiotensin II and pseudopeptide analogues to the AT2 receptor2008In: Journal of Molecular Graphics and Modelling, ISSN 1093-3263, E-ISSN 1873-4243, Vol. 26, no 6, p. 991-1003Article in journal (Refereed)
    Abstract [en]

    The 3D model of the AT(2) receptor has been built employing homology to the transmembrane domain of rhodopsin and a novel build-up procedure for restoring the extracellular loops. By docking a model peptide of angiotensin II in the AT(2) receptor model two plausible binding modes were identified. These binding modes were in agreement with most of the suggested ligand-receptor contact points reported in the literature. Eight active and one inactive pseuclopeptide angiotensin II analogue were also docked in the receptor and four of the active pseudopeptides were found to mimic the binding mode of angiotensin II. An alternative binding mode for the other four active pseudopeptides was found.

1 - 8 of 8
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