Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug–drug complex networks

One limitation of almost all antiviral Quantitative Structure–Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug–drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug–drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg. Med. Chem. 2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg. Med. Chem. 2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg. Med. Chem. 2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug–drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity = 84.62%) and 119 of 139 non-active compounds (sensitivity = 85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug–drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.     

Production of nisin Z using <Emphasis Type="Italic">Lactococcus lactis</Emphasis> IO-1 from hydrolyzed sago starch

A membrane bioreactor for production of nisin Z was constructed using Lactococcus lactis IO-1 in continuous culture using hydrolyzed sago starch as carbon source. A strategy used to enhance the productivity of nisin Z was to maintain the cells in a continuous growth at high cell concentration. This resulted in a volumetric productivity of nisin Z, as 50,000 IU l⁻¹ h⁻¹ using a cell concentration of 15 g l⁻¹, 30^°C, pH 5.5 and a dilution rate of 1.24 h⁻¹. Adding 10 g l⁻¹ YE and 2 g l⁻¹ polypeptone, other inducers were unnecessary to maintain production of nisin. The operating conditions of the reactor removed nisin and lactate, thus minimizing their effects which allowed the maintenance of cells in continuous exponential growth phase mode with high metabolic activity.     

Genetic diversity and structure of an endemic and critically endangered stream river salamander (Caudata: Ambystoma leorae) in Mexico

Small or isolated populations are highly susceptible to stochastic events. They are prone and vulnerable to random demographic or environmental fluctuations that could lead to extinction due to the loss of alleles through genetic drift and increased inbreeding. We studied Ambystoma leorae an endemic and critically threatened species. We analyzed the genetic diversity and structure, effective population size, presence of bottlenecks and inbreeding coefficient of 96 individuals based on nine microsatellite loci. We found high levels of genetic diversity expressed as heterozygosity (H_o = 0.804, H_e = 0.613, H_e* = 0.626 and H_Nei = 0.622). The population presents few alleles (4–9 per locus) and genotypes (3–14 per locus) compared with other mole salamanders species. We identified three genetically differentiated subpopulations with a significant level of genetic structure (F_ST = 0.021, R_ST = 0.044 y D_est = 0.010, 95 % CI). We also detected a reduction signal in population size and evidence of a genetic bottleneck (M = 0.367). The effective population size is small (Ne = 45.2), but similar to another mole salamanders with restricted distributions or with recently fragmented habitat. The inbreeding coefficient levels detected are low (F_IS = ?0.619–0.102) as is gene flow. Despite, high levels of genetic diversity A. leorae is critically endangered because it is a small isolated population.