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Small interfering RNAs (siRNAs) are now established as the preferred tool to inhibit gene function in mammalian cells yet trigger unintended gene silencing due to their inherent miRNA-like behavior. Such off-target effects are primarily mediated by the sequence-specific interaction between the siRNA seed regions (position 2–8 of either siRNA strand counting from the 5'-end) and complementary sequences in the 3'UTR of (off-) targets. It was previously shown that chemical modification of siRNAs can reduce off-targeting but only very few modifications have been tested leaving more to be identified. Here we developed a luciferase reporter-based assay suitable to monitor siRNA off-targeting in a high throughput manner using stable cell lines. We investigated the impact of chemically modifying single nucleotide positions within the siRNA seed on siRNA function and off-targeting using 10 different types of chemical modifications, three different target sequences and three siRNA concentrations. We found several differently modified siRNAs to exercise reduced off-targeting yet incorporation of the strongly destabilizing unlocked nucleic acid (UNA) modification into position 7 of the siRNA most potently reduced off-targeting for all tested sequences. Notably, such position-specific destabilization of siRNA–target interactions did not significantly reduce siRNA potency and is therefore well suited for future siRNA designs especially for applications in vivo where siRNA concentrations, expectedly, will be low.
The thermodynamics of base pairing is of fundamental importance. Fluorinated base analogs are valuable tools for investigating pairing interactions. To understand the influence of direct base–base interactions in relation to the role of water, pairing free energies between natural nucleobases and fluorinated analogs are estimated by potential of mean force calculations. Compared to pairing of AU and GC, pairing involving fluorinated analogs is unfavorable by 0.5–1.0 kcal mol -1. Decomposing the pairing free energies into enthalpic and entropic contributions reveals fundamental differences for Watson–Crick pairs compared to pairs involving fluorinated analogs. These differences originate from direct base–base interactions and contributions of water. Pairing free energies of fluorinated base analogs with natural bases are less unfavorable by 0.5–1.0 kcal mol -1 compared to non-fluorinated analogs. This is attributed to stabilizing C–F…H–N dipolar interactions and stronger N…H–C hydrogen bonds, demonstrating direct and indirect influences of fluorine. 7-methyl-7H-purine and its 9-deaza analog (Z) have been suggested as members of a new class of non-fluorinated base analogs. Z is found to be the least destabilizing universal base in the context of RNA known to date. This is the first experimental evidence for nitrogen-containing heterocylces as bioisosteres of aromatic rings bearing fluorine atoms.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 Protein kinases are important targets for drug development. The almost identical protein folding of kinases and the common co-substrate ATP leads to the problem of inhibitor selectivity. Type II inhibitors, targeting the inactive conformation of kinases, occupy a hydrophobic pocket with less conserved surrounding amino acids. Human polo-like kinase 1 (Plk1) represents a promising target for approaches to identify new therapeutic agents. Plk1 belongs to a family of highly conserved serine/threonine kinases, and is a key player in mitosis, where it modulates the spindle checkpoint at metaphase/anaphase transition. Plk1 is over-expressed in all today analyzed human tumors of different origin and serves as a negative prognostic marker in cancer patients. The newly identified inhibitor, SBE13, a vanillin derivative, targets Plk1 in its inactive conformation. This leads to selectivity within the Plk family and towards Aurora A. This selectivity can be explained by docking studies of SBE13 into the binding pocket of homology models of Plk1, Plk2 and Plk3 in their inactive conformation. SBE13 showed anti-proliferative effects in cancer cell lines of different origins with EC50 values between 5 microM and 39 microM and induced apoptosis. Increasing concentrations of SBE13 result in increasing amounts of cells in G2/M phase 13 hours after double thymidin block of HeLa cells. The kinase activity of Plk1 was inhibited with an IC50 of 200 pM. Taken together, we could show that carefully designed structure-based virtual screening is well-suited to identify selective type II kinase inhibitors targeting Plk1 as potential anti-cancer therapeutics.
The transcription factor p63 is expressed as at least six different isoforms, of which two have been assigned critical biological roles within ectodermal development and skin stem cell biology on the one hand and supervision of the genetic stability of oocytes on the other hand. These two isoforms contain a C-terminal inhibitory domain that negatively regulates their transcriptional activity. This inhibitory domain contains two individual components: one that uses an internal binding mechanism to interact with and mask the transactivation domain and one that is based on sumoylation. We have carried out an extensive alanine scanning study to identify critical regions within the inhibitory domain. These experiments show that a stretch of ~13 amino acids is crucial for the binding function. Further, investigation of transcriptional activity and the intracellular level of mutants that cannot be sumoylated suggests that sumoylation reduces the concentration of p63. We therefore propose that the inhibitory function of the C-terminal domain is in part due to direct inhibition of the transcriptional activity of the protein and in part due to indirect inhibition by controlling the concentration of p63. Keywords: p63, transcriptional regulation, auto-inhibition, sumoylation