Harnessing Protein-Ligand Interaction Fingerprints to Predict New Scaffolds of RIPK1 Inhibitors

Necroptosis has emerged as an exciting target in oncological, inflammatory, neurodegenerative, and autoimmune diseases, in addition to acute ischemic injuries. It is known to play a role in innate immune response, as well as in antiviral cellular response. Here we devised a concerted in silico and experimental framework to identify novel RIPK1 inhibitors, a key necroptosis factor. We propose the first in silico model for the prediction of new RIPK1 inhibitor scaffolds by combining docking and machine learning methodologies. Through the data analysis of patterns in docking results, we derived two rules, where rule #1 consisted of a four-residue signature filter, and rule #2 consisted of a six-residue similarity filter based on docking calculations. These were used in consensus with a machine learning QSAR model from data collated from ChEMBL, the literature, in patents, and from PubChem data. The models allowed for good prediction of actives of >90, 92, and 96.4% precision, respectively. As a proof-of-concept, we selected 50 compounds from the ChemBridge database, using a consensus of both molecular docking and machine learning methods, and tested them in a phenotypic necroptosis assay and a biochemical RIPK1 inhibition assay. A total of 7 of the 47 tested compounds demonstrated around 20–25% inhibition of RIPK1’s kinase activity but, more importantly, these compounds were discovered to occupy new areas of chemical space. Although no strong actives were found, they could be candidates for further optimization, particularly because they have new scaffolds. In conclusion, this screening method may prove valuable for future screening efforts as it allows for the exploration of new areas of the chemical space in a very fast and inexpensive manner, therefore providing efficient starting points amenable to further hit-optimization campaigns.     

Ion synthesis and optical properties of gold nanoparticles in an Al2O3 matrix

Single-crystal Al₂O₃(0001) and Al₂O₃(1120) substrates are implanted by 160-keV Au⁺ ions with doses from 10¹⁵ to 10¹⁷ cm^?2. Some of the implanted samples are air-annealed at 800–1200°C. The properties of the synthesized composite layers are studied by Rutherford backscattering and linear optical reflection measurements, and their nonlinear optical characteristics are examined by RZ-scanning using a picosecond Nd: YAG laser operating at a wavelength of 1064 nm. The Rutherford backscattering spectra indicate that the implanted impurity concentrates near the surface of the Al₂O₃. The formation of gold nanoparticles in the Al₂O₃ can be judged from the characteristic optical plasmon resonance band in the reflectance spectra of the samples irradiated to a dose higher than 6.0 × 10¹⁶ cm^?2. The synthesized particles are shown to be responsible for nonlinear optical refraction in the samples. The nonlinear refractive index, n ₂, and the real part of the third-order susceptibility, Rex⁽³⁾, of the composite layers are determined.     

Electronic transport in graphene nanostructures on SiO2

We report two experiments on graphene nanostructures. The first was performed on a graphene nanoribbon, where the nature of electronic transport was investigated in detail. Electrons or holes are found to localize in pockets of the potential along the ribbon. Transport is governed by the joint action of localization and Coulomb interaction. The temperature-dependence of the conductance shows activated behavior at temperatures above a few Kelvin. The activation energy retraces the edges of Coulomb blockade diamonds found in nonlinear transport. In the second experiment the metallic tip of a low-temperature scanning force microscope was scanned above a graphene quantum dot. In addition to the familiar Coulomb blockade fringes, localized states are detected forming in the constrictions connecting the dot to source and drain.     

Rapid and easy quantification of elemental sulphur in aqueous samples from biological reactors: the turbidimetric method revisited

Recent developments in wastewater treatment have led to a renewed interest to obtain elemental sulphur (S°) as a by-product from bioreactors. However, practical studies are limited by the gap of adequate analytical techniques for its determination. This paper provides a statistical study and matrix effect evaluation of an adapted spectrophotometric method for routine S° analyses in aqueous samples, based on a methodology previously described by Hart (1961). Four complex matrices were tested: domestic sewage and effluent samples from three different bioreactors. Tested performance criteria included linearity, matrix effect, limit of detection and quantification and S° recovery. Results were linear (R² = 0.99994) in the studied range (5 to 100 mg S° L^?1) and no matrix effect was observed. The accuracy was based on recovery values that varied from 100% to 106%. The colloidal S° separation and extraction protocol was also considered suitable for aqueous samples, reaching more than 99.0% of S° recovery.