More effective dithiocarbamate derivatives inhibiting carbonic anhydrases, generated by QSAR and computational design.

Dithiocarbamates (DTC) are promising compounds with potential applications in antitumoral and glaucoma therapy. Our aim is to understand molecular features affecting DTC interaction with carbonic anhydrases (CAs), zinc-containing enzymes maintaining acid-base balance in blood and other tissues. To this end, we generate QSAR models based on a compound series containing 25 DTC, inhibitors of four human (h) CAs isoforms: hCA I, II, IX and XII. We establish that critical physicochemical parameters for DTC inhibitory activity are: hydrophobic, electronic, steric, topological and shape. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q(2) (0.55-0.73), fitted correlation r(2) (0.75-0.84) and standard error of prediction (0.47-0.23). Based on the established QSAR equations, we analyse 22 new DTC derivatives and identify DTC dicarboxilic acids derivatives and their esters as potentially improved inhibitors of CA I, II, IX and XII.

3D-QSAR study indicates an enhancing effect of membrane ions on psychiatric drugs targeting serotonin receptor 5-HT1A.

Antidepressants and antipsychotics are psychiatric agents used for the treatment of various types of psychiatric diseases. Although currently among the most commonly prescribed drugs, their effectiveness and adverse effects are the topic of many studies and controversial claims. Here we generate QSAR models based on compounds series including 20 drugs recommended for two critical psychiatric diseases: depression and schizophrenia and we use these QSAR models to predict the biological activity of these 20 antidepressants and antipsychotics. We establish the membrane ions' contributions (sodium, potassium, calcium and iron) mediated by water to the antagonism of these drugs at the 5-HT1A receptor. The reliability of our QSAR models in predicting compounds activity is indicated by significant values for cross-validated correlation q² (0.60-0.76) and fitted correlation r² (0.96-0.98) coefficients. Our results indicate that potassium, calcium and iron play a key role for the antagonistic activity of drugs at the 5-HT1A receptor. Moreover, based on the established QSAR equations, we analysed 24 new escitalopram derivatives as possibly improved antidepressants targeting the 5-HT1A receptor. We identified that the presence of methyl groups and hydrogen atoms improves antidepressant activity while the simultaneous presence of ethyl, propyl or halogens decreased drastically antidepressant activity at the 5-HT1A site.

More effective antimicrobial mastoparan derivatives, generated by 3D-QSAR-Almond and computational mutagenesis.

Antimicrobial peptides are drugs used against a wide range of pathogens which present a great advantage: in contrast with antibiotics they do not develop resistance. The wide spectrum of antimicrobial peptides advertises them in the research and pharmaceutical industry as attractive starting points for obtaining new, more effective analogs. Here we predict the antimicrobial activity against Bacillus subtilis (expressed as minimal inhibitory concentration values) for 33 mastoparan analogs and their new derivatives by a non-aligned 3D-QSAR (quantitative structure-activity relationship) method. We establish the contribution to antimicrobial activity of molecular descriptors (hydrophobicity, hydrogen bond donor and steric), correlated with contributions from the membrane environment (sodium, potassium, chloride). Our best QSAR models show significant cross-validated correlation q(2) (0.55-0.75), fitted correlation r(2) (greater than 0.90) coefficients and standard error of prediction SDEP (less than 0.250). Moreover, based on our most accurate 3D-QSAR models, we propose nine new mastoparan analogs, obtained by computational mutagenesis, some of them predicted to have significantly improved antimicrobial activity compared to the parent compound.

Structural and functional characterization of a novel, host penetration-related pectate lyase from the potato cyst nematode Globodera rostochiensis.

SUMMARY The cell wall, a strong extraprotoplasmic layer surrounding plant cells that mainly consists of a variety of polysaccharides, constitutes a major barrier for potential parasites. Plant-parasitic nematodes are well equipped to overcome this barrier as they produce and secrete cell-wall-degrading enzymes. Expression profiling of various life stages of the potato cyst nematode Globodera rostochiensis revealed a novel pectate lyase gene (Gr-pel2, 759 bp). The Gr-PEL2 protein showed highest similarity to pectate lyases from the facultative plant-parasitic nematodes Bursaphelenchus mucronatus and B. xylophilus and the soil-inhabiting saprophytic Streptomyces and Frankia species (i.e. 40-42% identity and 58-60% similarity), whereas only a remote relatedness to the previously identified Gr-PEL1 was observed (i.e. 28% identity and 43% similarity). Transient expression of Gr-pel2 in leaves of Nicotiana benthamiana resulted in severe malformations of the infiltrated tissues, not relating to maceration and soft rot symptoms. Ca(2+) is known to be essential for pectate lyase activity, and the most likely calcium-binding site was identified in the Gr-PEL2 protein by combining homology modelling of the three-dimensional structure, site-directed mutagenesis and transient expression in leaves. A highly charged cleft in Gr-PEL2, which is likely to be involved in substrate binding and which is also significantly more hydrophobic in Gr-PEL1, was shown to be essential for protein activity. Our results underline the broad spectrum of pectate lyases and cell-wall-degrading enzymes necessary for successful parasitism by cyst nematodes.

Evaluation of a neural networks QSAR method based on ligand representation using substituent descriptors. Application to HIV-1 protease inhibitors.

We present here a neural networks method designed to predict biological activity based on a local representation of the ligand. The compounds of the series are represented by a vector mapping for each of four substituent properties: volume, log P, dipole moment and a simple 'steric' parameter relating to its shape. This ligand representation was tested using neural networks on a set of 42 cyclic-urea derivatives, inhibiting HIV-1 protease. The leave-one-out cross-validation using all descriptors in the input gave a correlation factor between prediction and experiment of 0.76 for the overall set and 0.88 when three outliers were left out. To rank the significance of the four descriptors, we further tested all combinations of two and three parameters for each substituent, using two disjunctive testing sets of five inhibitors. In these sets, vectors with extreme descriptor values were used either in the training or the testing set (sets A and B, respectively). The method is a very good interpolator (set A, 95+/-2% accuracy) but a less effective extrapolator (set B, 85+/-2% accuracy). Generally, the combinations including the 'steric' parameter predict better than average, while those containing the volume are less effective. The best prediction, 98.8+/-1.2%, was obtained when log P, the dipole and the steric parameter were used on set A. At the opposite end, the lowest ranked descriptor set was obtained when replacing log P with the volume, giving 92.3+/-6.7% accuracy over the set A.