Displaying publications 1 - 20 of 262 in total

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  1. Fulltext Composition of Overlapping Protein-Protein and Protein-Ligand Interfaces
    Mohamed R, Degac J, Helms V
    PLoS One, 2015;10(10):e0140965.
    PMID: 26517868 DOI: 10.1371/journal.pone.0140965
    Protein-protein interactions (PPIs) play a major role in many biological processes and they represent an important class of targets for therapeutic intervention. However, targeting PPIs is challenging because often no convenient natural substrates are available as starting point for small-molecule design. Here, we explored the characteristics of protein interfaces in five non-redundant datasets of 174 protein-protein (PP) complexes, and 161 protein-ligand (PL) complexes from the ABC database, 436 PP complexes, and 196 PL complexes from the PIBASE database and a dataset of 89 PL complexes from the Timbal database. In all cases, the small molecule ligands must bind at the respective PP interface. We observed similar amino acid frequencies in all three datasets. Remarkably, also the characteristics of PP contacts and overlapping PL contacts are highly similar.
    Matched MeSH terms: Ligands
  2. Fulltext A Quantum-Based Similarity Method in Virtual Screening
    Al-Dabbagh MM, Salim N, Himmat M, Ahmed A, Saeed F
    Molecules, 2015;20(10):18107-27.
    PMID: 26445039 DOI: 10.3390/molecules201018107
    One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.
    Matched MeSH terms: Ligands
  3. Bis[1-(pyridin-2-yl)ethanone-kappa N 4-phenylthiosemicarbazonato-kappa 2N4,S]manganese(II)
    Usman A, Razak IA, Chantrapromma S, Fun HK, Sreekanth A, Sivakumar S, et al.
    Acta Crystallogr C, 2002 Sep;58(Pt 9):m461-3.
    PMID: 12205370
    One half of the molecule of the title complex, [Mn(C(14)H(13)N(4)S)(2)], is related to the other half by a twofold axis passing through the Mn atom. This high-spin Mn atom is six-coordinated, in an octahedral geometry, by the azomethine N, the pyridyl N and the thiolate S atom of two planar 1-(pyridin-2-yl)ethanone N(4)-phenylthiosemicarbazone ligands. In the crystal, the molecules are interconnected by N-H.S and C-H.N interactions, forming a three-dimensional network.
    Matched MeSH terms: Ligands
  4. Fulltext Density Functional Theory Studies of the Electronic Structure and Muon Hyperfine Interaction in [Au25(SR)18]0 and [Au25(SeR)18]0 Nanoclusters
    Ahmad SN, Zaharim WN, Sulaiman S, Hasan Baseri DF, Mohd Rosli NA, Ang LS, et al.
    ACS Omega, 2020 Dec 29;5(51):33253-33261.
    PMID: 33403287 DOI: 10.1021/acsomega.0c04937
    Density functional theory computational investigation was performed to study the electronic structures, muon sites, and the associated hyperfine interactions in [Au25(SR)18]0 and [Au25(SeR)18]0 where R is phenylethane. The calculated electronic structures show inhomogeneous spin density distribution and are also affected by different ligands. The two most stable muon sites near Au atoms in the thiolated system are MAu11 and MAu6. When the thiolate ligands were replaced by selenolate ligands, the lowest energy positions of muons moved to MAu6 and MAu5. Muons prefer to stop inside the Au12 icosahedral shell, away from the central Au and the staple motifs region. Muonium states at phenyl ring and S/Se atoms in the ligand were found to be stable and the Fermi contact fields are much larger as compared to the field experienced by muons near Au atoms.
    Matched MeSH terms: Ligands
  5. Simulation of live-cell imaging system reveals hidden uncertainties in cooperative binding measurements
    Watabe M, Arjunan SNV, Chew WX, Kaizu K, Takahashi K
    Phys Rev E, 2019 Jul;100(1-1):010402.
    PMID: 31499827 DOI: 10.1103/PhysRevE.100.010402
    We propose a computational method to quantitatively evaluate the systematic uncertainties that arise from undetectable sources in biological measurements using live-cell imaging techniques. We then demonstrate this method in measuring the biological cooperativity of molecular binding networks, in particular, ligand molecules binding to cell-surface receptor proteins. Our results show how the nonstatistical uncertainties lead to invalid identifications of the measured cooperativity. Through this computational scheme, the biological interpretation can be more objectively evaluated and understood under a specific experimental configuration of interest.
    Matched MeSH terms: Ligands
  6. Fulltext Condorcet and borda count fusion method for ligand-based virtual screening
    Ahmed A, Saeed F, Salim N, Abdo A
    J Cheminform, 2014;6:19.
    PMID: 24883114 DOI: 10.1186/1758-2946-6-19
    BACKGROUND: It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database.

    RESULTS: The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints.

    CONCLUSIONS: Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.

    Matched MeSH terms: Ligands
  7. Ligand expansion in ligand-based virtual screening using relevance feedback
    Abdo A, Saeed F, Hamza H, Ahmed A, Salim N
    J Comput Aided Mol Des, 2012 Mar;26(3):279-87.
    PMID: 22249773 DOI: 10.1007/s10822-012-9543-4
    Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
    Matched MeSH terms: Ligands
  8. Small Molecules for Active Targeting in Cancer
    Kue CS, Kamkaew A, Burgess K, Kiew LV, Chung LY, Lee HB
    Med Res Rev, 2016 Apr;36(3):494-575.
    PMID: 26992114 DOI: 10.1002/med.21387
    For the purpose of this review, active targeting in cancer research encompasses strategies wherein a ligand for a cell surface receptor expressed on tumor cells is used to deliver a cytotoxic or imaging cargo. This area of research is more than two decades old, but in those 20 and more years, how many receptors have been studied extensively? What kinds of the ligands are used for active targeting? Are they mostly naturally occurring molecules such as folic acid, or synthetic substances developed in campaigns for medicinal chemistry efforts? This review outlines the most important receptor or ligand combinations that have been used in active targeting to answer these questions, and therefore to address the most important one of all: is research in active targeting affording diminishing returns, or is this an area for which the potential far exceeds progress made so far?
    Matched MeSH terms: Ligands
  9. Dichlorobis(trimethylenethiourea-kappaS)antimony(III) chloride
    Razak IA, Usman A, Fun HK, Yamin BM, Keat GW
    Acta Crystallogr C, 2002 Feb;58(Pt 2):m122-3.
    PMID: 11828100
    In the title compound, [SbCl(2)(C(4)H(8)N(2)S)(2)]Cl, the coordination around the Sb atom can be described as distorted pseudo-octahedral. Both rings of the trimethylenethiourea ligands [alternatively 3,4,5,6-tetrahydropyrimidine-2(1H)-thione] adopt an envelope conformation. The molecules are connected into dimers in the ab plane by two intermolecular hydrogen bonds. The dimers are arranged into infinite one-dimensional chains along the a axis as a result of the Cl(-) ions forming intermolecular hydrogen bonds with three NH groups.
    Matched MeSH terms: Ligands
  10. Tris(8-quinolinolato-N,O)cobalt(III) ethanol solvate
    Shanmuga Sundara Raj S, Razak IA, Fun HK, Zhao PS, Jian F, Yang X, et al.
    Acta Crystallogr C, 2000 Apr 15;56(Pt 4):E130-1.
    PMID: 15263175
    In the crystal of the title complex, [Co(C(9)H(6)NO)(3)].C(2)H(5)OH, the central Co atom has a distorted octahedral coordination comprised of three N atoms and three O atoms from the three 8-quinolinolato ligands. The three Co-O bond distances are in the range 1.887 (2)-1.910 (2) A, while the three Co-N bond distances range from 1.919 (2) to 1.934 (2) A. The solvent ethanol molecule forms an intermolecular O-H.O hydrogen bonding with a quinolinolato ligand.
    Matched MeSH terms: Ligands
  11. A luminescent edge-interlocked prismatic heteroleptic metallocage assembled through a ligand replacement reaction
    Zhan SZ, Li JH, Zhang GH, Liu XW, Li M, Zheng J, et al.
    Chem Commun (Camb), 2019 Oct 03;55(80):11992-11995.
    PMID: 31498358 DOI: 10.1039/c9cc05236d
    A luminescent edge-interlocked heteroleptic metallocage based on Cu3(pyrazolate)3 was prepared through a ligand replacement reaction from a homoleptic metallocage and a new ligand. Its structure was confirmed by XRD and MALDI-TOF mass spectrometry. Theoretical calculations revealed the new ligand was evidently responsible for the bathochromic shift of the optimal excitation. This work provides a heteroleptic strategy to regulate the interlocking fashion and photophysical mechanism of metallocages based on Cu3(pyrazolate)3.
    Matched MeSH terms: Ligands
  12. A critical view on the analysis of fluorescence quenching data for determining ligand-protein binding affinity
    Bakar KA, Feroz SR
    Spectrochim Acta A Mol Biomol Spectrosc, 2019 Dec 05;223:117337.
    PMID: 31302564 DOI: 10.1016/j.saa.2019.117337
    The past decade has seen an increase in the number of research papers on ligand binding to proteins based on fluorescence spectroscopy. In most cases, determination of the binding affinity is made by analyzing the quenching of protein fluorescence induced by the ligand. However, many such articles, even those published in reputed journals, suffer from several mistakes with regard to analysis of fluorescence quenching data. Using the binding of phenylbutazone to human serum albumin as a model, we consider some of these mistakes and show how they affect the values of the association constant. In particular, the failure to correct for the inner filter effect and the use of unsuitable equations are discussed. Ligand binding data presented in these articles should be treated with caution, especially in the absence of data from complementary techniques.
    Matched MeSH terms: Ligands
  13. Fulltext Secondary bonding in di-methyl-bis-(morpholine-4-carbodi-thio-ato-κ(2)S,S')tin(IV): crystal structure and Hirshfeld surface analysis
    Zaldi NB, Hussen RSD, Lee SM, Halcovitch NR, Jotani MM, Tiekink ERT
    Acta Crystallogr E Crystallogr Commun, 2017 Jun 01;73(Pt 6):842-848.
    PMID: 28638641 DOI: 10.1107/S2056989017006855
    The title compound, [Sn(CH3)2(C5H8NOS2)2], has the Sn(IV) atom bound by two methyl groups which lie over the weaker Sn-S bonds formed by two asymmetrically chelating di-thio-carbamate ligands so that the coordination geometry is skew-trapezoidal bipyramidal. The most prominent feature of the mol-ecular packing are secondary Sn⋯S inter-actions [Sn⋯S = 3.5654 (7) Å] that lead to centrosymmetric dimers. These are connected into a three-dimensional architecture via methyl-ene-C-H⋯S and methyl-C-H⋯O(morpholino) inter-actions. The Sn⋯S inter-actions are clearly evident in the Hirshfeld surface analysis of the title compound along with a number of other inter-molecular contacts.
    Matched MeSH terms: Ligands
  14. Fulltext Di-n-butyl-bis-[N-(2-meth-oxy-eth-yl)-N-methyl-dithio-carbamato-κ2S,S']tin(IV): crystal structure and Hirshfeld surface analysis
    Mohamad R, Awang N, Kamaludin NF, Jotani MM, Tiekink ER
    Acta Crystallogr E Crystallogr Commun, 2017 Feb 01;73(Pt 2):260-265.
    PMID: 28217355 DOI: 10.1107/S2056989017001098
    The complete mol-ecule of the title compound, [Sn(C4H9)2(C5H10NOS2)2], is generated by a crystallographic mirror plane, with the SnIV atom and the two inner methyl-ene C atoms of the butyl ligands lying on the mirror plane; statistical disorder is noted in the two terminal ethyl groups, which deviate from mirror symmetry. The di-thio-carbamate ligand coordinates to the metal atom in an asymmetric mode with the resulting C2S4 donor set defining a skew trapezoidal bipyramidal geometry; the n-butyl groups are disposed to lie over the longer Sn-S bonds. Supra-molecular chains aligned along the a-axis direction and sustained by methyl-ene-C-H⋯S(weakly coordinating) inter-actions feature in the mol-ecular packing. A Hirshfeld surface analysis reveals the dominance of H⋯H contacts in the crystal.
    Matched MeSH terms: Ligands
  15. Fulltext fac-Aceto-nitrile-tricarbon-yl(di-methyl-carbamodi-thio-ato-κ(2)S,S')rhenium(I): crystal structure and Hirshfeld surface analysis
    Tan SL, Lee SM, Heard PJ, Halcovitch NR, Tiekink ER
    Acta Crystallogr E Crystallogr Commun, 2017 Feb 01;73(Pt 2):213-218.
    PMID: 28217345 DOI: 10.1107/S2056989017000755
    The title compound, [Re(C3H6NS2)(C2H3N)(CO)3], features an octa-hedrally coordinated Re(I) atom within a C3NS2 donor set defined by three carbonyl ligands in a facial arrangement, an aceto-nitrile N atom and two S atoms derived from a symmetrically coordinating di-thio-carbamate ligand. In the crystal, di-thio-carbamate-methyl-H⋯O(carbon-yl) inter-actions lead to supra-molecular chains along [36-1]; both di-thio-carbamate S atoms participate in intra-molecular methyl-H⋯S inter-actions. Further but weaker aceto-nitrile-C-H⋯O(carbonyl) inter-actions assemble mol-ecules in the ab plane. The nature of the supra-molecular assembly was also probed by a Hirshfeld surface analysis. Despite their weak nature, the C-H⋯O contacts are predominant on the Hirshfeld surface and, indeed, on those of related [Re(CO)3(C3H6NS2)L] structures.
    Matched MeSH terms: Ligands
  16. [N-Benzyl-N-(2-phenyl-eth-yl)di-thio-carbamato-κ(2)S,S']tri-phenyl-tin(IV) and [bis-(2-meth-oxy-eth-yl)di-thio-carbamato-κ(2)S,S']tri-phenyl-tin(IV): crystal structures and Hirshfeld surface analysis
    Mohamad R, Awang N, Kamaludin NF, Jotani MM, Tiekink ER
    Acta Crystallogr E Crystallogr Commun, 2016 Oct 1;72(Pt 10):1480-1487.
    PMID: 27746946
    The crystal and mol-ecular structures of two tri-phenyl-tin di-thio-carbamates, [Sn(C6H5)3(C16H16NS2)], (I), and [Sn(C6H5)3(C7H14NO2S2)], (II), are described. In (I), the di-thio-carbamate ligand coordinates the Sn(IV) atom in an asymmetric manner, leading to a highly distorted trigonal-bipyramidal coordination geometry defined by a C3S2 donor set with the weakly bound S atom approximately trans to one of the ipso-C atoms. A similar structure is found in (II), but the di-thio-carbamate ligand coordinates in an even more asymmetric fashion. The packing in (I) features supra-molecular chains along the c axis sustained by C-H⋯π inter-actions; chains pack with no directional inter-actions between them. In (II), supra-molecular layers are formed, similarly sustained by C-H⋯π inter-actions; these stack along the b axis. An analysis of the Hirshfeld surfaces for (I) and (II) confirms the presence of the C-H⋯π inter-actions but also reveals the overall dominance of H⋯H contacts in the respective crystals.
    Matched MeSH terms: Ligands
  17. Fulltext Crystal structure of tetra-aqua-bis(3,5-di-amino-4H-1,2,4-triazol-1-ium)cobalt(II) bis-[bis-(pyridine-2,6-di-carboxyl-ato)cobaltate(II)] dihydrate
    Johnson A, Mbonu J, Hussain Z, Loh WS, Fun HK
    Acta Crystallogr E Crystallogr Commun, 2015 Jun 1;71(Pt 6):m139-40.
    PMID: 26090171 DOI: 10.1107/S2056989015010014
    The asymmetric unit of the title compound, [Co(C2H6N5)2(H2O)4][Co(C7H3NO4)2]2·2H2O, features 1.5 Co(II) ions (one anionic complex and one half cationic complex) and one water mol-ecule. In the cationic complex, the Co(II) atom is located on an inversion centre and is coordinated by two triazolium cations and four water mol-ecules, adopting an octa-hedral geometry where the N atoms of the two triazolium cations occupy the axial positions and the O atoms of the four water mol-ecules the equatorial positions. The two triazole ligands are parallel offset (with a distance of 1.38 Å between their planes). In the anionic complex, the Co(II) ion is six-coordinated by two N and four O atoms of the two pyridine-2,6-di-carboxyl-ate anions, exhibiting a slightly distorted octa-hedral coordination geometry in which the mean plane of the two pyridine-2,6-di-carboxyl-ate anions are almost perpendicular to each other, making a dihedral angle of 85.87 (2)°. In the crystal, mol-ecules are linked into a three-dimensional network via C-H⋯O, C-H⋯N, O-H⋯O and N-H⋯O hydrogen bonds.
    Matched MeSH terms: Ligands
  18. Assessing GPCR homology models constructed from templates of various transmembrane sequence identities: Binding mode prediction and docking enrichment
    Loo JSE, Emtage AL, Ng KW, Yong ASJ, Doughty SW
    J Mol Graph Model, 2017 Dec 29;80:38-47.
    PMID: 29306746 DOI: 10.1016/j.jmgm.2017.12.017
    GPCR crystal structures have become more readily accessible in recent years. However, homology models of GPCRs continue to play an important role as many GPCR structures remain unsolved. The new crystal structures now available provide not only additional templates for homology modelling but also the opportunity to assess the performance of homology models against their respective crystal structures and gain insight into the performance of such models. In this study we have constructed homology models from templates of various transmembrane sequence identities for eight GPCR targets to better understand the relationship between transmembrane sequence identity and model quality. Model quality was assessed relative to the crystal structure in terms of structural accuracy as well as performance in two typical structure-based drug design applications: ligand binding pose prediction and docking enrichment in virtual screening. Crystal structures significantly outperformed homology models in both assessments. Accurate ligand binding pose prediction was possible but difficult to achieve using homology models, even with the use of induced fit docking. In virtual screening using homology models still conferred significant enrichment compared to random selection, with a clear benefit also observed in using models optimized through induced fit docking. Our results indicate that while homology models that are reasonably accurate structurally can be constructed, without significant refinement homology models will be outperformed by crystal structures in ligand binding pose prediction and docking enrichment regardless of the template used, primarily due to the extremely high level of structural accuracy needed for such applications.
    Matched MeSH terms: Ligands
  19. Fulltext 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity
    Wongrattanakamon P, Lee VS, Nimmanpipug P, Jiranusornkul S
    Data Brief, 2016 Dec;9:35-42.
    PMID: 27626051 DOI: 10.1016/j.dib.2016.08.004
    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.
    Matched MeSH terms: Ligands
  20. Cooperativity transitions driven by higher-order oligomer formations in ligand-induced receptor dimerization
    Watabe M, Arjunan SNV, Chew WX, Kaizu K, Takahashi K
    Phys Rev E, 2019 Dec;100(6-1):062407.
    PMID: 31962468 DOI: 10.1103/PhysRevE.100.062407
    While cooperativity in ligand-induced receptor dimerization has been linked with receptor-receptor couplings via minimal representations of physical observables, effects arising from higher-order oligomer, e.g., trimer and tetramer, formations of unobserved receptors have received less attention. Here we propose a dimerization model of ligand-induced receptors in multivalent form representing physical observables under basis vectors of various aggregated receptor states. Our simulations of multivalent models not only reject Wofsy-Goldstein parameter conditions for cooperativity, but show that higher-order oligomer formations can shift cooperativity from positive to negative.
    Matched MeSH terms: Ligands
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