Integrated High-Throughput Screening and Large-Scale Isobolographic Analysis to Accelerate the Discovery of Radiosensitizers With Greater Selectivity for Cancer Cells.

PURPOSE: High-throughput screening (HTS) platforms have been widely used to identify candidate anticancer drugs and drug-drug combinations; however, HTS-based identification of new drug-ionizing radiation (IR) combinations has rarely been reported. Herein, we developed an integrated approach including cell-based HTS and computational large-scale isobolographic analysis to accelerate the identification of radiosensitizing compounds acting strongly and more specifically on cancer cells. METHODS AND MATERIALS: In a 384-well plate format, 160 compounds likely to interfere with the cell response to radiation were screened on human glioblastoma (U251-MG) and cervix carcinoma (ME-180) cell lines, as well as on normal fibroblasts (CCD-19Lu). After drug exposure, cells were irradiated or not and short-term cell survival was assessed by high-throughput cell microscopy. Computational large-scale dose-response and isobolographic approach were used to identify promising synergistic drugs radiosensitizing cancer cells rather than normal cells. Synergy of a promising compound was confirmed on ME-180 cells by an independent 96-well assay protocol, and finally, by the gold-standard colony forming assay. RESULTS: We retained 4 compounds synergistic at 2 isoeffects in U251-MG and ME-180 cell lines and 11 compounds synergistically effective in only one cancer cell line. Among these 15 promising radiosensitizers, 5 compounds showed limited toxicity combined or not with IR on normal fibroblasts. CONCLUSIONS: Overall, this study demonstrated that HTS chemoradiation screening together with large-scale computational analysis is an efficient tool to identify synergistic drug-IR combinations, with concomitant assessment of unwanted toxicity on normal fibroblasts. It sparks expectations to accelerate the discovery of highly desired agents improving the therapeutic index of radiation therapy.

The first laminin G-like domain of protein S is essential for binding and activation of Tyro3 receptor and intracellular signalling.

The homologous proteins Gas6 and protein S (ProS1) are both natural ligands for the TAM (Tyro3, Axl, MerTK) receptor tyrosine kinases. ProS1 selectively activates Tyro3; however, the precise molecular interface of the ProS1-Tyro3 contact has not been characterised. We used a set of chimeric proteins in which each of the C-terminal laminin G-like (LG) domains of ProS1 were swapped with those of Gas6, as well as a set of ProS1 mutants with novel added glycosylations within LG1. Alongside wildtype ProS1, only the chimera containing ProS1 LG1 domain stimulated Tyro3 and Erk phosphorylation in human cancer cells, as determined by Western blot. In contrast, Gas6 and chimeras containing minimally the Gas6 LG1 domain stimulated Axl and Akt phosphorylation. We performed in silico homology modelling and molecular docking analysis to construct and evaluate structural models of both ProS1-Tyro3 and Gas6-Axl ligand-receptor interactions. These analyses revealed a contact between the ProS1 LG1 domain and the first immunoglobulin domain of Tyro3, which was similar to the Gas6-Axl interaction, and involved long-range electrostatic interactions that were further stabilised by hydrophobic and polar contacts. The mutant ProS1 proteins, which had added glycosylations within LG1 but which were all outside of the modelled contact region, all activated Tyro3 in cells with no hindrance. In conclusion, we show that the LG1 domain of ProS1 is necessary for activation of the Tyro3 receptor, involving protein-protein interaction interfaces that are homologous to those of the Gas6-Axl interaction.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

High-Throughput Image-Based Aggresome Quantification.

Aggresomes are subcellular perinuclear structures where misfolded proteins accumulate by retrograde transport on microtubules. Different methods are available to monitor aggresome formation, but they are often laborious, time-consuming, and not quantitative. Proteostat is a red fluorescent molecular rotor dye, which becomes brightly fluorescent when it binds to protein aggregates. As this reagent was previously validated to detect aggresomes, we have miniaturized its use in 384-well plates and developed a method for high-throughput imaging and quantification of aggresomes. Two different image analysis methods, including one with machine learning, were evaluated. They lead to similar robust data to quantify cells having aggresome, with satisfactory Z' factor values and reproducible EC50 values for compounds known to induce aggresome formation, like proteasome inhibitors. We demonstrated the relevance of this phenotypic assay by screening a chemical library of 1280 compounds to find aggresome modulators. We obtained hits that present similarities in their structural and physicochemical properties. Interestingly, some of them were previously described to modulate autophagy, which could explain their effect on aggresome structures. In summary, we have optimized and validated the Proteostat detection reagent to easily measure aggresome formation in a miniaturized, automated, quantitative, and high-content assay. This assay can be used at low, middle, or high throughput to quantify changes in aggresome formation that could help in the understanding of chemical compound activity in pathologies such as protein misfolding disorders or cancer.

Lipase hydration state in the gas phase: sorption isotherm measurements and inverse gas chromatography.

The adsorption of water and substrate on immobilized Candida antarctica lipase B was studied by performing adsorption isotherm measurements and using inverse gas chromatography (IGC). Water adsorption isotherm of the immobilized enzyme showed singular profile absorption incompatible with the Brunauer-Emmet-Teller model, probably due to the hydrophobic nature of the support, leading to very low interactions with water. IGC allowed determining the evolution with water thermodynamic activity (a(W)) of both dispersive surface energies and acidity and basicity constants of immobilized enzyme. These results showed that water molecules progressively covered immobilized enzyme, when increasing a(W), leading to a saturation of polar groups above a(W) 0.1 and full coverage of the surface above a(W) 0.25. IGC also enabled relevant experiments to investigate the behavior of substrates under a(W) that they will experience, in a competitive situation with water. Results indicated that substrates had to displace water molecules in order to adsorb on the enzyme from a(W) values ranging from 0.1 to 0.2, depending on the substrate. As the conditions used for these adsorption studies resemble the ones of the continuous enzymatic solid/gas reactor, in which activity and selectivity of the lipase were extensively studied, it was possible to link adsorption results with particular effects of water on enzyme properties.