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.

Discovery, Structure-Activity Relationships, and In Vivo Activity of Dihydropyridone Agonists of the Bile Acid Receptor TGR5.

A novel series of potent agonists of the bile acid receptor TGR5 bearing a dihydropyridone scaffold was developed from a high-throughput screen. Starting from a micromolar hit compound, we implemented an extensive structure-activity-relationship (SAR) study with the synthesis and biological evaluation of 83 analogues. The project culminated with the identification of the potent nanomolar TGR5 agonist 77A. We report the GLP-1 secretagogue effect of our lead compound ex vivo in mouse colonoids and in vivo. In addition, to identify specific features favorable for TGR5 activation, we generated and optimized a three-dimensional quantitative SAR model that contributed to our understanding of our activity profile and could guide further development of this dihydropyridone series.

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.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

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.

FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases.

SUMMARY: Several web-based tools predict the putative targets of a small molecule query compound by similarity to molecules with known bioactivity data using molecular fingerprints. In numerous situations, it would however be valuable to be able to run such computations on a local computer. We present FastTargetPred, a new program for the prediction of protein targets for small molecule queries. Structural similarity computations rely on a large collection of confirmed protein-ligand activities extracted from the curated ChEMBL 25 database. The program allows to annotate an input chemical library of ∼100k compounds within a few hours on a simple personal computer. AVAILABILITY AND IMPLEMENTATION: FastTargetPred is written in Python 3 (≥3.7) and C languages. Python code depends only on the Python Standard Library. The program can be run on Linux, MacOS and Windows operating systems. Pre-compiled versions are available at https://github.com/ludovicchaput/FastTargetPred. FastTargetPred is licensed under the GNU GPLv3. The program calls some scripts from the free chemistry toolkit MayaChemTools. CONTACT: bruno.villoutreix@inserm.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset.

Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset to predict the binding modes and ranking of ligands. The macrocyclic nature of the BACE ligands brought additional challenges, which were dealt with by a careful preparation of the three-dimensional input structures for ligands. This provided top-performing predictions for BACE, in contrast with CatS, where the predictions in the absence of guiding constraints provided poor results. These results highlight the importance of previous structural knowledge that is needed for correct predictions on some challenging targets. After the end of the challenge, we also carried out free energy calculations (i.e. in a non-blinded manner) for CatS using the pmx software and several force fields (AMBER, Charmm). Using knowledge-based starting pose construction allowed reaching remarkable accuracy for the CatS free energy estimates. Interestingly, we show that the use of a consensus result, by averaging the results from different force fields, increases the prediction accuracy.

Identification of New Potent Acetylcholinesterase Inhibitors Using Virtual Screening and in vitro Approaches.

Acetylcholinesterase (AChE) is currently the most favorable target for the symptomatic treatment and reduction of Alzheimer's disease (AD). In order to identify new potent inhibitors of this enzyme, we describe herein a new structure-based virtual screening (SBVS) using the Institut Curie-CNRS chemical library (ICCL), which contained at the screening date 14307 compounds. The strategy undertaken in this work consisted of the use of several docking programs in SBVS calculations followed by the application of a consensus method (vSDC) and a scrupulous visual analysis. It allowed us to obtain a high degree of success, with a yield of almost 86 %, since 12 hits were identified among only 14 molecules tested in vitro. Still more remarkably, 6 of these hits were more active than galantamine, the reference inhibitor. These hits were predicted to have good ADMET properties. The two most promising compounds can serve as leads for AD treatment.

Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations.

During the last few years, we have developed a docking protocol involving two steps: (i) the choice of the most appropriate docking software and parameters for the system of interest using structural and functional information available in public databases (PDB, ChEMBL, PubChem Assay, BindingDB, etc.); (ii) the docking of ligand dataset to provide a prediction for the binding modes and ranking of ligands. We applied this protocol to the D3R Grand Challenge 3 dataset containing cathepsin S (CatS) inhibitors. Considering the size and conformational flexibility of ligands, the docking calculations afforded reasonable overall pose predictions, which are however dependent on the specific nature of each ligand. As expected, the correct ranking of docking poses is still challenging. Post-processing of docking poses with molecular dynamics simulations in explicit solvent provided a significantly better prediction, whereas free energy calculations on a subset of compounds brought no significant improvement in the ranking prediction compared with the direct ranking obtained from the scoring function.

Topology-Selective, Fluorescent "Light-Up" Probes for G-Quadruplex DNA Based on Photoinduced Electron Transfer.

Six novel probes were prepared by covalent attachment of a G4-DNA ligand (bis(quinolinium) pyridodicarboxamide; PDC) to various coumarin or pyrene fluorophores. In the absence of DNA, the fluorescence of all probes is quenched due to intramolecular photoinduced electron transfer (PET), as evidenced by photophysical and electrochemical studies, molecular modeling, and DFT calculations. All probes demonstrate similarly high thermal stabilization of various G4-DNA substrates belonging to different folding topologies, as assessed by fluorescence melting experiments; however, their fluorimetric response is strongly heterogeneous with respect to the structures of the probes and G4-DNA targets. Thus, the probes containing the 7-diethylaminocoumarin fluorophore demonstrate significant fluorescence enhancement in the presence of G4-DNA, with the strongest "light-up" response (20- to 180-fold) observed for antiparallel G4 structures as well as for hybrid G4 structures, formed by the variants of human telomeric sequence and capable of a conformation change to the antiparallel isoform. These results shed light on the influence of the linker and electronic properties of fluorophores on the efficiency of G4-DNA "light-up" probes operating via PET.

Efficient conformational sampling and weak scoring in docking programs? Strategy of the wisdom of crowds.

BACKGROUND: In drug design, an efficient structure-based optimization of a ligand needs the precise knowledge of the protein-ligand interactions. In the absence of experimental information, docking programs are necessary for ligand positioning, and the choice of a reliable program is essential for the success of such an optimization. The performances of four popular docking programs, Gold, Glide, Surflex and FlexX, were investigated using 100 crystal structures of complexes taken from the Directory of Useful Decoys-Enhanced database. RESULTS: The ligand conformational sampling was rather efficient, with a correct pose found for a maximum of 84 complexes, obtained by Surflex. However, the ranking of the correct poses was not as efficient, with a maximum of 68 top-rank or 75 top-4 rank correct poses given by Glidescore. No relationship was found between either the sampling or the scoring performance of the four programs and the properties of either the targets or the small molecules, except for the number of ligand rotatable bonds. As well, no exploitable relationship was found between each program performance in docking and in virtual screening; a wrong top-rank pose may obtain a good score that allows it to be ranked among the most active compounds and vice versa. Also, to improve the results of docking, the strengths of the programs were combined either by using a rescoring procedure or the United Subset Consensus (USC). Oddly, positioning with Surflex and rescoring with Glidescore did not improve the results. However, USC based on docking allowed us to obtain a correct pose in the top-4 rank for 87 complexes. Finally, nine complexes were scrutinized, because a correct pose was found by at least one program but poorly ranked by all four programs. Contrarily to what was expected, except for one case, this was not due to weaknesses of the scoring functions. CONCLUSIONS: We conclude that the scoring functions should be improved to detect the correct poses, but sometimes their failure may be due to other varied considerations. To increase the chances of success, we recommend to use several programs and combine their results. Graphical abstract Summary of the results obtained by semi-rigid docking of crystallographic ligands. The docking was done on 100 protein-ligand X-ray structures, taken from the DUD-E database, and using four programs, Glide, Gold, Surflex and FlexX. Based on the docking results, we applied our United Subset Consensus method (USC), for which only the top4-rank poses are relevant. The number of complexes for which the best pose is correct, is represented by the gray boxes, the blue and red boxes correspond to the number of complexes with a correct pose ranked as the top 1 or within the top 4. A pose is considered correct when its root-mean-square deviation from the crystal structure is less than 2 Å.

Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance.

BACKGROUND: In a structure-based virtual screening, the choice of the docking program is essential for the success of a hit identification. Benchmarks are meant to help in guiding this choice, especially when undertaken on a large variety of protein targets. Here, the performance of four popular virtual screening programs, Gold, Glide, Surflex and FlexX, is compared using the Directory of Useful Decoys-Enhanced database (DUD-E), which includes 102 targets with an average of 224 ligands per target and 50 decoys per ligand, generated to avoid biases in the benchmarking. Then, a relationship between these program performances and the properties of the targets or the small molecules was investigated. RESULTS: The comparison was based on two metrics, with three different parameters each. The BEDROC scores with α = 80.5, indicated that, on the overall database, Glide succeeded (score > 0.5) for 30 targets, Gold for 27, FlexX for 14 and Surflex for 11. The performance did not depend on the hydrophobicity nor the openness of the protein cavities, neither on the families to which the proteins belong. However, despite the care in the construction of the DUD-E database, the small differences that remain between the actives and the decoys likely explain the successes of Gold, Surflex and FlexX. Moreover, the similarity between the actives of a target and its crystal structure ligand seems to be at the basis of the good performance of Glide. When all targets with significant biases are removed from the benchmarking, a subset of 47 targets remains, for which Glide succeeded for only 5 targets, Gold for 4 and FlexX and Surflex for 2. CONCLUSION: The performance dramatic drop of all four programs when the biases are removed shows that we should beware of virtual screening benchmarks, because good performances may be due to wrong reasons. Therefore, benchmarking would hardly provide guidelines for virtual screening experiments, despite the tendency that is maintained, i.e., Glide and Gold display better performance than FlexX and Surflex. We recommend to always use several programs and combine their results. Graphical AbstractSummary of the results obtained by virtual screening with the four programs, Glide, Gold, Surflex and FlexX, on the 102 targets of the DUD-E database. The percentage of targets with successful results, i.e., with BDEROC(α = 80.5) > 0.5, when the entire database is considered are in Blue, and when targets with biased chemical libraries are removed are in Red.

vSDC: a method to improve early recognition in virtual screening when limited experimental resources are available.

BACKGROUND: In drug design, one may be confronted to the problem of finding hits for targets for which no small inhibiting molecules are known and only low-throughput experiments are available (like ITC or NMR studies), two common difficulties encountered in a typical academic setting. Using a virtual screening strategy like docking can alleviate some of the problems and save a considerable amount of time by selecting only top-ranking molecules, but only if the method is very efficient, i.e. when a good proportion of actives are found in the 1-10 % best ranked molecules. RESULTS: The use of several programs (in our study, Gold, Surflex, FlexX and Glide were considered) shows a divergence of the results, which presents a difficulty in guiding the experiments. To overcome this divergence and increase the yield of the virtual screening, we created the standard deviation consensus (SDC) and variable SDC (vSDC) methods, consisting of the intersection of molecule sets from several virtual screening programs, based on the standard deviations of their ranking distributions. CONCLUSIONS: SDC allowed us to find hits for two new protein targets by testing only 9 and 11 small molecules from a chemical library of circa 15,000 compounds. Furthermore, vSDC, when applied to the 102 proteins of the DUD-E benchmarking database, succeeded in finding more hits than any of the four isolated programs for 13-60 % of the targets. In addition, when only 10 molecules of each of the 102 chemical libraries were considered, vSDC performed better in the number of hits found, with an improvement of 6-24 % over the 10 best-ranked molecules given by the individual docking programs.Graphical abstractIn drug design, for a given target and a given chemical library, the results obtained with different virtual screening programs are divergent. So how to rationally guide the experimental tests, especially when only a few number of experiments can be made? The variable Standard Deviation Consensus (vSDC) method was developed to answer this issue. Left panel the vSDC principle consists of intersecting molecule sets, chosen on the basis of the standard deviations of their ranking distributions, obtained from various virtual screening programs. In this study Glide, Gold, FlexX and Surflex were used and tested on the 102 targets of the DUD-E database. Right panel Comparison of the average percentage of hits found with vSDC and each of the four programs, when only 10 molecules from each of the 102 chemical libraries of the DUD-E database were considered. On average, vSDC was capable of finding 38 % of the findable hits, against 34 % for Glide, 32 % for Gold, 16 % for FlexX and 14 % for Surflex, showing that with vSDC, it was possible to overcome the unpredictability of the virtual screening results and to improve them.

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.