Synthesis and antiviral activity of ethidium-arginine conjugates directed against the TAR RNA of HIV-1.

The regulatory protein Tat is essential for viral gene expression and replication of the human immunodeficiency virus type 1 (HIV-1). Tat transactivates the HIV-1 long terminal repeat (LTR) via its binding to the transactivation responsive element (TAR) and increases the viral transcription. Studies have shown that the binding of arginine and arginine derivatives induces a conformational change of the TAR RNA at the Tat-binding site. The unpaired A17 residue delimits a small cavity which constitutes a receptor site for small molecules, especially for ethidium bromide. These binding characteristics have prompted us to design a series of ethidium-arginine conjugates capable of interacting with the TAR RNA. Here we report the synthesis of six ethidium derivatives equipped with arginine side chains. These molecules were biologically evaluated, and two compounds (17 and 20) exhibited in vitro anti-HIV-1 activity at micromolar concentration, without toxicity (up to 100 microM concentration). Melting temperature studies indicated that the most active molecule (20) bound strongly to TAR in vitro. RNase protection experiments agreed with the molecular modeling studies which suggested that the ethidium moiety of 20 was inserted next to the A17 residue while the arginine side chain occupied the pyrimidine bulge.

Amphiphilic anionic analogues of galactosylceramide: synthesis, anti-HIV-1 activity, and gp120 binding.

We describe the synthesis together with the results of anti-HIV-1 activity and gp120-monolayer binding experiments of new galactosyl amphiphiles, analogues of galactosylceramide, an alternative receptor used by HIV to infect CD4 negative cells. These compounds consist of single- and double-chain amphiphiles containing one or two galactose residues. To favor their clustering into galactosyl-rich microdomains, their molecular structure contains also an amino group or several hydroxyls or anionic groups, such as carboxylate, sulfate, sulfonate, and phosphate. Among the 12 new galactosylated compounds reported, a specific anti-HIV activity, although moderate (IC(50) from 10 to 50 microM), was detected only for three of them, i.e., I-GalSer[CO2Na][C14], II-GalSer[C14][C7SO3Na], and II-GalSer[C2SO4Na][C14], which contain an anionic group. The marked increase of surface pressure which was observed upon addition of gp120 into the aqueous subphase underneath the monolayers containing these galactolipids indicated gp120 insertion into the monolayers, suggesting that binding of these three derivatives to HIV-1 gp120 may be responsible for their anti-HIV activity.

Interaction of new PNA-based molecules with TAR RNA of HIV-1: molecular modelling and biological evaluation.

During the HIV-1 replication process, interactions between the RNA sequence, named TAR RNA, and the viral protein, Tat, permit a fast and efficient transcription of viral DNA into RNA. Based on the NMR structure of TAR RNA from the PDB, two Peptidic Nucleic Analog- (PNA) based molecules were designed by molecular modelling, the first one targeting G32 U31 and the second targeting U31 C30 free loop bases. Before designing the molecules, the flexibility of the TAR RNA was evaluated by molecular dynamics (MD). The molecules studied are composed of three domains: an arginine, a linker, and two PNA bases. First, molecules were designed and the linker length was optimized to fit the TAR RNA; second, a MD simulation on the TAR RNA molecule complex was performed to validate the molecular structure. Optimal molecules were synthesized and tested on infected cells. The experimental results support the choices made in the design of the molecules.

Comparative receptor surface analysis (CoRSA) model for calcium channel antagonists.

Three-dimensional quantitative structure-activity relationships (3D QSAR) are widely used for the prediction of in vitro or in vivo interactions between chemical compounds and their biological targets (transporters, receptors, ion channels, enzymes). Comparative receptor surface analysis (CoRSA) is a new 3D QSAR algorithm that can be applied to study ligand-receptor interactions whenever the structure of the biological target is not known. The steric and electrostatic features of the most active compounds from a QSAR set are used by CoRSA to generate a virtual receptor model, represented as points on a surface complementary to the van der Waals surface of the aligned compounds. The CoRSA structural descriptors, represented by the total interaction energies between each surface point of the virtual receptor and all atoms in a molecule, are used in a partial least squares data analysis to generate a structure-activity model. In this paper the calcium channel antagonist activity of 35 dihydropyridine derivatives is modeled with CoRSA, giving a 3D QSAR with r2 = 0.928 for calibration and r2cv = 0.921 for the leave-one-out cross-validation.

Identification of groupings of graph theoretical molecular descriptors using a hybrid cluster analysis approach.

There is an abundance of structural molecular descriptors of various forms that have been proposed and tested over the years. Very often different descriptors represent, more or less, the same aspects of molecular structures and, thus, they have diminished discriminating power for the identification of different structural features that might contribute to the molecular property, or activity of interest. Therefore, it is essential that noncorrelated descriptors be employed to ensure the wider and the less inflated possible coverage of the chemical space. The most usual approach for reducing the number of descriptors and employing noncorrelated (or orthogonal) descriptors involves principal component analysis (PCA) or other factor analytical techniques. In this work we present an approach for determining relationships (groupings) among 240 graph-theoretical descriptors, as a means for selecting nonredundant ones, based on the application of cluster analysis (CA). To remove inherent biases and particularities of different CA algorithms, several clustering solutions, using these algorithms, were "hybridized" to obtain a reliable and confident overall solution concerning how the interrelationships within the data are structured. The calculated correlation coefficients between descriptors were used as a reference for a discussion on the different CA methods employed, and the resulted clusters of descriptors were statistically analyzed for deriving the intercorrelations between the different operators, weighting schemes and matrices used for the computation of these descriptors.

Automatic generation of knowledge base from infrared spectral database for substructure recognition

This paper presents a new methodology of chemical substructure recognition by interpretation of an infrared spectrum. The approach in spectrum interpretation is based on the determination of functional groups, which may be present or absent in compounds whose structure is unknown. The process of searching for spectrum-substructure correlation is realized by application of a statistical algorithm. In this method, correlations are generalized and condensed into a set of interpretation rules which are applied to the interpretation of an unknown compound's spectrum in order to predict whether the respective substructures are present or absent in the unknown molecule.

Evaluation in quantitative structure--property relationship models of structural descriptors derived from information-theory operators

During the search for new structural descriptors we have defined the information-theory operators U(M), V(M), X(M), and Y(M), that are computed from atomic invariants and measure the information content of the elements of molecular matrices. Structural descriptors computed with these four information-theory operators are used to develop structure-property models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. The information-theory operators were applied to six molecular matrices, namely, the distance D, the reciprocal distance RD, the distance-path Dp, the reciprocal distance-path RDp, the path Szeged Sz(p), and the reciprocal path Szeged RSz(p) matrices. In combination with other topological indices, the information-theory indices offer good structure-property models for all six alkane properties investigated in this study.

Comparison of weighting schemes for molecular graph descriptors: application in quantitative structure-retention relationship models for alkylphenols in gas-liquid chromatography

Organic compounds containing heteroatoms or multiple bonds can be conveniently represented as vertex- and edge-weighted molecular graphs. These atom and bond parameters can be computed for any organic compound with two parameter sets that we have recently defined, namely, the relative electronegativity X and the relative covalent radius Y weighting schemes. Structural descriptors computed with these two weighting schemes and the previously defined atomic number Z parameter set are used to develop quantitative structure-retention relationship (QSRR) models for alkylphenols in gas-liquid chromatography. The QSRR models are generated with structural descriptors computed with several newly introduced graph operators, namely, the Wiener, hyper-Wiener, minimum eigenvalue, maximum eigenvalue, Ivanciuc-Balaban, and information on distance operators. These molecular graph operators were applied to the distance D and the reciprocal distance RD matrixes.

Quasi-orthogonal basis sets of molecular graph descriptors as a chemical diversity measure

In the pharmaceutical industry, the virtual screening of combinatorial libraries is used to rationally select compounds for biological testing from databases of hundreds of thousands of compounds. In addition to structural descriptors, such as fingerprints and pharmacophores, the application of relatively simple structural descriptors traditionally used in quantitative structure-activity studies offers speed and efficiency for rapidly measuring the molecular diversity of such collections. We explore new topological indices computed from the molecular graph as potential structural descriptors for the characterization of molecular diversity. A database of 2000 compounds randomly selected from the National Cancer Institute AIDS database was used to measure the intercorrelation of the descriptors. The initial collection of 240 structural descriptors was reduced to several quasi-orthogonal sets of up to 9 descriptors, using different thresholds for the maximum intercorrelation coefficient.

Identification of structural features from mass spectrometry using a neural network approach: application to trimethylsilyl derivatives used for medical diagnosis.

An artificial neural network (ANN) has been trained to recognize the presence or absence of specific structural features (SF) in trimethylsilyl derivatives of organic acids from their mass spectra. The input vector is constructed without knowledge of the molecular ion, which is generally not observed in the spectra of these compounds. The results are used in conjunction with a classical search in a spectral library to identify organic acids in biological fluids for rapid acidemias diagnosis.

Modeling of the interaction between new ethidium derivatives and TAR RNA of HIV-1.

During the HIV-1 replication process, interactions between the first sequence of RNA synthesized named TAR RNA and a viral protein named Tat permit a fast and efficient transcription of viral DNA in RNA. Based on the NMR structure of TAR RNA found on the PDB, new derivatives of ethidium were designed by molecular modeling to inhibit this interaction. The studied molecules are composed of three domains: an arginine, a linker, and an ethidium. Three linkers of different lengths were considered in the first step, with the TAR RNA-arginine interaction and the intercalation of the ethidium simulated by docking methods. In a second step, the structure of the TAR RNA was completed to obtain a whole ethidium interaction site and docking of the whole studied molecules was investigated. Molecules were synthesized and tested on infected cells. The predicted models and activity are in good agreement with the reported experimental results.