Garbage in, garbage out: how reliable training data improved a virtual screening approach against SARS-CoV-2 MPro

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have the power to integrate, process and analyze multiple data in a short time. However, these initiatives may yield unrealistic results if the applied models are not inferred from reliable data and the resulting predictions are not confirmed by experimental evidence.Methods: We undertook a drug discovery campaign against the essential major protease (MPro) from SARS-CoV-2, which relied on an in silico search strategy –performed in a large and diverse chemolibrary– complemented by experimental validation. The computational method comprises a recently reported ligand-based approach developed upon refinement/learning cycles, and structure-based approximations. Search models were applied to both retrospective (in silico) and prospective (experimentally confirmed) screening.Results: The first generation of ligand-based models were fed by data, which to a great extent, had not been published in peer-reviewed articles. The first screening campaign performed with 188 compounds (46 in silico hits and 100 analogues, and 40 unrelated compounds: flavonols and pyrazoles) yielded three hits against MPro (IC50 ≤ 25 μM): two analogues of in silico hits (one glycoside and one benzo-thiazol) and one flavonol. A second generation of ligand-based models was developed based on this negative information and newly published peer-reviewed data for MPro inhibitors. This led to 43 new hit candidates belonging to different chemical families. From 45 compounds (28 in silico hits and 17 related analogues) tested in the second screening campaign, eight inhibited MPro with IC50 = 0.12–20 μM and five of them also impaired the proliferation of SARS-CoV-2 in Vero cells (EC50 7–45 μM).Discussion: Our study provides an example of a virtuous loop between computational and experimental approaches applied to target-focused drug discovery against a major and global pathogen, reaffirming the well-known “garbage in, garbage out” machine learning principle

Dual-Wavelength Thulium-Doped Fiber Laser With Separate Wavelengths Selection Based On A Two Mmi Filters Configuration

Dual-wavelength laser emission of a ring cavity Tm-doped fiber laser with a separate wavelength selection of the simultaneously generated laser lines is experimentally demonstrated. A dual-wavelength laser operation is obtained by using two homemade multimode interference (MMI) filters with transmission peaks at the wavelengths of 1814.23 and 1872.15 nm, which are placed in a Mach Zehnder interferometer configuration for independent selection of the dual-wavelength generated laser lines. The selection of the shorter laser wavelength is achieved by submerging the MMI filter in different ethylene glycol/water mixtures, reaching a wavelength selection in the broad range of ∼24 nm. The stability results of the simultaneously generated laser lines are also shown. The use of the proposed MMI filters as reliable spectral filters for dual-wavelength generation in all-fiber Tm-doped fiber laser systems is experimentally demonstrated

Microplastics in terrestrial ecosystems: Moving beyond the state of the art to minimize the risk of ecological surprise

Microplastic (plastic particles measuring <5mm) pollution is ubiquitous. Unlike in other well-studied ecosystems, for example, marine and freshwater environments, microplastics in terrestrial systems are relatively understudied. Their potential impacts on terrestrial environments, in particular the risk of causing ecological surprise, must be better understood and quantified. Ecological surprise occurs when ecosystem behavior deviates radically from expectations and generally has negative consequences for ecosystem services. The properties and behavior of microplastics within terrestrial environments may increase their likelihood of causing ecological surprises as they (a) are highly persistent global pollutants that will last for centuries, (b) can interact with the abiotic environment in a complex manner, (c) can impact terrestrial organisms directly or indirectly and (d) interact with other contaminants and can facilitate their transport. Here, we compiled findings of previous research on microplastics in terrestrial environments. We systematically focused on studies addressing different facets of microplastics related to their distribution, dispersion, impact on soil characteristics and functions, levels of biological organization of tested terrestrial biota (single species vs. assemblages), scale of experimental study and corresponding ecotoxicological effects. Our systematic assessment of previous microplastic research revealed that most studies have been conducted on single species under laboratory conditions with short-term exposures; few studies were conducted under more realistic long-term field conditions and/or with multi-species assemblages. Studies targeting multi-species assemblages primarily considered soil bacterial communities and showed that microplastics can alter essential nutrient cycling functions. More ecologically meaningful studies of terrestrial microplastics encompassing multi-species assemblages, critical ecological processes (e.g., biogeochemical cycles and pollination) and interactions with other anthropogenic stressors must be conducted. Addressing these knowledge gaps will provide a better understanding of microplastics as emerging global stressors and should lower the risk of ecological surprise in terrestrial ecosystems