Inhibitors of \u3cem\u3eN\u3csup\u3eα\u3c/sup\u3e\u3c/em\u3e-acetyl-l-ornithine Deacetylase: Synthesis, Characterization and Analysis of their Inhibitory Potency

A series of N α-acyl (alkyl)- and N α-alkoxycarbonyl-derivatives of l- and d-ornithine were prepared, characterized, and analyzed for their potency toward the bacterial enzyme N α-acetyl-l-ornithine deacetylase (ArgE). ArgE catalyzes the conversion of N α-acetyl-l-ornithine to l-ornithine in the fifth step of the biosynthetic pathway for arginine, a necessary step for bacterial growth. Most of the compounds tested provided IC50 values in the μM range toward ArgE, indicating that they are moderately strong inhibitors. N α-chloroacetyl-l-ornithine (1g) was the best inhibitor tested toward ArgE providing an IC50 value of 85 μM while N α-trifluoroacetyl-l-ornithine (1f), N α-ethoxycarbonyl-l-ornithine (2b), and N α-acetyl-d-ornithine (1a) weakly inhibited ArgE activity providing IC50 values between 200 and 410 μM. Weak inhibitory potency toward Bacillus subtilis-168 for N α-acetyl-d-ornithine (1a) and N α-fluoro- (1f), N α-chloro- (1g), N α-dichloro- (1h), and N α-trichloroacetyl-ornithine (1i) was also observed. These data correlate well with the IC50 values determined for ArgE, suggesting that these compounds might be capable of getting across the cell membrane and that ArgE is likely the bacterial enzymatic target

EVEREST: A design environment for extreme-scale big data analytics on heterogeneous platforms

High-Performance Big Data Analytics (HPDA) applications are characterized by huge volumes of distributed and heterogeneous data that require efficient computation for knowledge extraction and decision making. Designers are moving towards a tight integration of computing systems combining HPC, Cloud, and IoT solutions with artificial intelligence (AI). Matching the application and data requirements with the characteristics of the underlying hardware is a key element to improve the predictions thanks to high performance and better use of resources. We present EVEREST, a novel H2020 project started on October 1, 2020, that aims at developing a holistic environment for the co-design of HPDA applications on heterogeneous, distributed, and secure platforms. EVEREST focuses on programmability issues through a data-driven design approach, the use of hardware-accelerated AI, and an efficient runtime monitoring with virtualization support. In the different stages, EVEREST combines state-of-the-art programming models, emerging communication standards, and novel domain-specific extensions. We describe the EVEREST approach and the use cases that drive our research