Aminoglycoside microarrays to explore interactions of antibiotics with RNAs and proteins

RNA is an important target for drug discovery efforts. Several clinically used aminoglycoside antibiotics bind to bacterial rRNA and inhibit protein synthesis. Aminoglycosides, however, are losing efficacy due to their inherent toxicity and the increase in antibiotic resistance. Targeting of other RNAs is also becoming more attractive thanks to the discovery of new potential RNA drug targets through genome sequencing and biochemical efforts. Identification of new compounds that target RNA is therefore urgent, and we report here on the development of rapid screening methods to probe binding of low molecular weight ligands to proteins and RNAs. A series of aminoglycosides has been immobilized onto glass microscope slides, and binding to proteins and RNAs has been detected by fluorescence. Construction and analysis of the arrays is completed by standard DNA genechip technology. Binding of immobilized aminoglycosides to proteins that are models for study of aminoglycoside toxicity (DNA polymerase and phospholipase C), small RNA oligonucleotide mimics of aminoglycoside binding sites in the ribosome (rRNA A-site mimics), and a large (approximately 400 nucleotide) group I ribozyme RNA is detected. The ability to screen large RNAs alleviates many complications associated with binding experiments that use isolated truncated regions from larger RNAs. These studies lay the foundation for rapid identification of small organic ligands from combinatorial libraries that exhibit strong and selective RNA binding while displaying decreased affinity to toxicity-causing proteins.     

Mimicking/Extracting Structure and Functions of Natural Products: Synthetic Approaches that Address Unexplored Needs in Chemical Biology

Natural products are often attractive and challenging targets for synthetic chemists, and many have interesting biological activities. However, synthetic chemists need to be more than simply suppliers of compounds to biologists. Therefore, we have been seeking ways to actively apply organic synthetic methods to chemical biology studies of natural products and their activities. In this personal review, I would like to introduce our work on the development of new biologically active compounds inspired by, or extracted from, the structures of natural products, focusing on enhancement of functional activity and specificity and overcoming various drawbacks of the parent natural products.     

Re‐engineering the Immune Response to Metastatic Cancer: Antibody‐Recruiting Small Molecules Targeting the Urokinase Receptor

Developing selective strategies to treat metastatic cancers remains a significant challenge. Herein, we report the first antibody‐recruiting small molecule (ARM) that is capable of recognizing the urokinase‐type plasminogen activator receptor (uPAR), a uniquely overexpressed cancer cell‐surface marker, and facilitating the immune‐mediated destruction of cancer cells. A co‐crystal structure of the ARM‐U2/uPAR complex was obtained, representing the first crystal structure of uPAR complexed with a non‐peptide ligand. Finally, we demonstrated that ARM‐U2 substantially suppresses tumor growth in vivo with no evidence of weight loss, unlike the standard‐of‐care agent doxorubicin. This work underscores the promise of antibody‐recruiting molecules as immunotherapeutics for treating cancer.     

A quantitative application of the J-based configuration analysis method to a flexible small molecule

An example of the use of the J-based configuration analysis method to determine relative stereochemistry of a small molecule related to reboxetine is described. This study was complicated by the fact that the molecule did not exhibit J-couplings and NOEs consistent with a single conformation, but rather an ensemble average. A quantitative fitting procedure using predicted couplings and NOEs from all possible conformers was used. This gave a clear indication of the stereochemistry, and the populations of the conformers involved.     

An in vitro study on probable inhibition of cerebrovascular disease by salidroside as a potent small molecule against induction of protein amyloid fibrils and cytotoxicity

Protein aggregation and associated amyloid formation is linked with several harmful human pathophysiologies including Alzheimer's, Parkinson's, and cerebrovascular diseases. A potential approach for modulating and exploring amyloid fibrillization is the control of protein self-assembly. Herein, anti-aggregation effects of salidroside, its influence on the kinetics of amyloid fibrillization of Aβ_1-42 peptide and its cytotoxicity against cerebrovascular endothelial cells (bEnd.3) were assessed by using a wide range of spectroscopic and cellular techniques. The present outcome of Thioflavin T (ThT) and 8-anilino-1-naphthalenesulfonic acid (ANS) fluorescence, Congo red (CR), and circular dichroism (CD) analyses indicated that salidroside potentially inhibits protein fibril formation. The cellular studies inferred that salidroside protects bEnd.3 cells against Aβ_1-42 oligomers -triggered cytotoxicity through modulation of oxidative stress [reactive oxygen species (ROS), superoxide dismutase (SOD) and catalase (CAT) activities] and apoptosis (caspase-3 activity). Therefore, the data signifies the role of salidroside as a promising small molecule in inhibiting Aβ_1-42 aggregation and associated cerebrovascular endothelial cell toxicity. Hence, salidroside can serve as a potential inhibitor in the therapeutic advancement to combat cerebrovascular diseases.     

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.