Evaluation of the Stereochemistry of Staphyloferrin A for Developing Staphylococcus-specific Targeting Conjugates.

Bacteria in the genus Staphylococcus are pathogenic and harmful to humans. Alarmingly, some Staphylococcus, such as methicillin-resistant S. aureus (MRSA) and vancomycin-resistant S. aureus (VRSA) have spread worldwide and become notoriously resistant to antibiotics, threatening and concerning public health. Hence, the development of new Staphylococcus-targeting diagnostic and therapeutic agents is urgent. Here, we chose the S. aureus-secreted siderophore staphyloferrin A (SA) as a guiding unit. We developed a series of Staphyloferrin A conjugates (SA conjugates) and showed the specific targeting ability to Staphylococcus bacteria. Furthermore, among the structural factors we evaluated, the stereo-chemistry of the amino acid backbone of SA conjugates is essential to efficiently target Staphylococci. Finally, we demonstrated that fluorescent Staphyloferrin A probes (SA-FL probes) could specifically target Staphylococci in complex bacterial mixtures.

Reaction Tracking and High-Throughput Screening of Active Compounds in Combinatorial Chemistry by Tandem Mass Spectrometry Molecular Networking.

Combinatorial synthesis has been widely used as an efficient strategy to screen for active compounds. Mass spectrometry is the method of choice in the identification of hits resulting from high-throughput screenings due to its high sensitivity, specificity, and speed. However, manual data processing of mass spectrometry data, especially for structurally diverse products in combinatorial chemistry, is extremely time-consuming and one of the bottlenecks in this process. In this study, we demonstrated the effectiveness of a tandem mass spectrometry molecular networking-based strategy for product identification, reaction dynamics monitoring, and active compound targeting in combinatorial synthesis. Molecular networking connects compounds with similar tandem mass spectra into a cluster and has been widely used in natural products analysis. We show that both the expected and side products can be readily characterized using molecular networking based on their mass spectrometry fragmentation patterns. Additionally, time-dependent molecular networking was integrated to track reaction dynamics to determine the optimal reaction time to maximize target product yields. We also present a proof-of-concept experiment that successfully identified and isolated active molecules from a dynamic combinatorial library. These results demonstrated the potential of using molecular networking for identifying, tracking, and high-throughput screening of active compounds in combinatorial synthesis.