Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
Techniques to evaluate gene expression profiling, including real-time quantitative PCR, TaqMan low-density arrays, and sufficiently sensitive cDNA microarrays, are efficient methods for monitoring human embryonic stem cell (hESC) cultures. However, most of these high-throughput tests have a limited use due to high cost, extended turnaround time, and the involvement of highly specialized technical expertise. Hence, there is a paucity of rapid, cost-effective, robust, yet sensitive methods for routine screening of hESCs. A critical requirement in hESC cultures is to maintain a uniform undifferentiated state and to determine their differentiation capacity by showing the expression of gene markers representing all germ layers, including ecto-, meso-, and endoderm. To quantify the modulation of gene expression in hESCs during their propagation, expansion, and differentiation via embryoid body (EB) formation, the authors developed a simple, rapid, inexpensive, and definitive multimarker, semiquantitative multiplex RT-PCR (mxPCR) platform technology. Among the 15 gene primers tested, 4 were pluripotent markers comprising set 1, and 3 lineage-specific markers from each ecto-, meso-, and endoderm layers were combined as sets 2 to 4, respectively. The authors found that these 4 sets were not only effective in determining the relative differentiation in hESCs, but were easily reproducible. In this study, they used the HUES-7 cell line to standardize the technique, which was subsequently validated with HUES-9, NTERA-2, and mouse embryonic fibroblast cells. This single-reaction mxPCR assay was flexible and, by selecting appropriate reporter genes, can be designed for characterization of different hESC lines during routine maintenance and directed differentiation.
The use of human variable heavy (VH) domain antibodies has been on the rise due to their small scaffold size and simple folding mechanism. A highly diverse library is largely dependent on the diversity introduced within the complementarity-determining region (CDR) cassettes. Here we introduced diversity with the use of a single framework diversifying all three CDRs using tailored codons consisting of degenerate trinucleotides (NNK). The length of the degeneracy in the CDRs was also taken into consideration based on the most frequently occurring length of CDRs and the canonical confirmation for each antibody subfamily. The semisynthetic human VH domain genes were assembled in a single pot using a temperature cascading process. The affinity selection process with Mycobacterium tuberculosis (MTb) α-crystalline was done using a semiautomated process. Enrichment of target-specific clones was observed with successful identification of monoclonal VH domain antibodies for MTb α-crystalline. In short, the semisynthetic library generated was able to select monoclonal VH domain antibodies against full MTb α-crystalline protein with complete semisynthetic CDRs displayed on a single scaffold. The library has the potential to be applied for the isolation of antibodies against other pathogenic proteins.