Development of a chemogenomics library for phenotypic screening.
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ABSTRACT:
SUBMITTER: Dafniet B
PROVIDER: S-EPMC8611952 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Aim:High-throughput phenotypic screens have emerged as a promising avenue for small-molecule drug discovery. The challenge faced in high-throughput phenotypic screens is target deconvolution once a small molecule hit is identified. Chemogenomics libraries have emerged as an important tool for meeting this challenge. Here, we investigate their target-specificity by deriving a 'polypharmacology index' for broad chemogenomics screening libraries. Methods:All known targets of all the compounds in each library were plotted as a histogram and fitted to a Boltzmann distribution, whose linearized slope is indicative of the overall polypharmacology of the library. Results & conclusion:Comparison of libraries clearly distinguished the most target-specific library, which might be assumed to be more useful for target deconvolution in a phenotypic screen.
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Project description:During host colonization, Campylobacter jejuni is exposed to harmful reactive oxygen species (ROS) produced from the host immune system and from the gut microbiota. Consequently, identification and characterization of oxidative stress defenses are important for understanding how C. jejuni survives ROS stress during colonization of the gastrointestinal tract. Previous transcriptomic studies have defined the genes belonging to oxidant stimulons within C. jejuni. We have constructed isogenic deletion mutants of these identified genes to assess their role in oxidative stress survival. Phenotypic screening of 109 isogenic deletion mutants identified 22 genes which were either hypersensitive or hyposensitive to oxidants, demonstrating important roles for these genes in oxidant defense. The significance of these genes in host colonization was also assessed in an in vivo chick model of C. jejuni colonization. Overall, our findings identify an indirect role for motility in resistance to oxidative stress. We found that a nonmotile flagellum mutant, the ?motAB mutant, displayed increased sensitivity to oxidants. Restoration of sensitivity to superoxide in the ?motAB mutant was achieved by fumarate supplementation or tandem deletion of motAB with ccoQ, suggesting that disruption of the proton gradient across the inner membrane resulted in increased superoxide production in this strain. Furthermore, we have identified genes involved in cation transport and binding, detoxification, and energy metabolism that are also important factors in oxidant defense. This report describes the first isogenic deletion mutant library construction for screening of relevant oxidative stress defense genes within C. jejuni, thus providing a comprehensive analysis of the total set of oxidative stress defenses.
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Project description:BACKGROUND: The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies. RESULTS: We show that interaction prediction in the chemogenomics framework outperforms state-of-the-art individual ligand-based methods in accuracy both for receptor with known ligands and without known ligands. This is done with no knowledge of the receptor 3D structure. In particular we are able to predict ligands of orphan GPCRs with an estimated accuracy of 78.1%. CONCLUSION: We propose new methods for in silico chemogenomics and validate them on the virtual screening of GPCRs. The methods represent an extension of a recently proposed machine learning strategy, based on support vector machines (SVM), which provides a flexible framework to incorporate various information sources on the biological space of targets and on the chemical space of small molecules. We investigate the use of 2D and 3D descriptors for small molecules, and test a variety of descriptors for GPCRs. We show that incorporating information about the known hierarchical classification of the target family and about key residues in their inferred binding pockets significantly improves the prediction accuracy of our model.
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Project description:A screen of a focused kinase inhibitor library against Trypanosoma brucei rhodesiense led to the identification of seven series, totaling 121 compounds, which showed >50?% inhibition at 5??m. Screening of these hits in a T.?b.?brucei proliferation assay highlighted three compounds with a 1H-imidazo[4,5-b]pyrazin-2(3H)-one scaffold that showed sub-micromolar activity and excellent selectivity against the MRC5 cell line. Subsequent rounds of optimisation led to the identification of compounds that exhibited good in vitro drug metabolism and pharmacokinetics (DMPK) properties, although in general this series suffered from poor solubility. A scaffold-hopping exercise led to the identification of a 1H-pyrazolo[3,4-b]pyridine scaffold, which retained potency. A number of examples were assessed in a T.?b.?brucei growth assay, which could differentiate static and cidal action. Compounds from the 1H-imidazo[4,5-b]pyrazin-2(3H)-one series were found to be either static or growth-slowing and not cidal. Compounds with the 1H-pyrazolo[3,4-b]pyridine scaffold were found to be cidal and showed an unusual biphasic nature in this assay, suggesting they act by at least two mechanisms.
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Project description:Protein-protein interaction (PPI) network maintains proper function of all organisms. Simple high-throughput technologies are desperately needed to delineate the landscape of PPI networks. While recent state-of-the-art yeast two-hybrid (Y2H) systems improved screening efficiency, either individual colony isolation, library preparation arrays, gene barcoding or massive sequencing are still required. Here, we developed a recombination-based 'library vs library' Y2H system (RLL-Y2H), by which multi-library screening can be accomplished in a single pool without any individual treatment. This system is based on the phiC31 integrase-mediated integration between bait and prey plasmids. The integrated fragments were digested by MmeI and subjected to deep sequencing to decode the interaction matrix. We applied this system to decipher the trans-kingdom interactome between Mycobacterium tuberculosis and host cells and further identified Rv2427c interfering with the phagosome-lysosome fusion. This concept can also be applied to other systems to screen protein-RNA and protein-DNA interactions and delineate signaling landscape in cells.
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Project description:Capitalizing on the inherent multiplexing capability of AsCpf1, we developed a multiplexed, high-throughput screening strategy that minimizes library size without sacrificing gene targeting efficiency. We demonstrated that AsCpf1 can be used for functional genomics screenings and that an AsCpf1-based multiplexed library performs similarly as compared to currently available monocistronic CRISPR/Cas9 libraries, with only one vector required for each gene. We construct the smallest whole-genome CRISPR knock-out library, Mini-human, for the human genome (n = 17,032 constructs targeting 16,977 protein-coding genes), which performs favorably compared to conventional Cas9 libraries.
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Project description:Stem cell transplantation and genetic therapies offer potential cures for patients with sickle cell disease (SCD), but these options require advanced medical facilities and are expensive. Consequently, these treatments will not be available for many years to the majority of patients suffering from this disease. What is urgently needed now is an inexpensive oral drug in addition to hydroxyurea, the only drug approved by the FDA that inhibits sickle-hemoglobin polymerization. Here, we report the results of the first phase of our phenotypic screen of the 12,657 compounds of the Scripps ReFRAME drug repurposing library using a recently developed high-throughput assay to measure sickling times following deoxygenation to 0% oxygen of red cells from sickle trait individuals. The ReFRAME library is a very important collection because the compounds are either FDA-approved drugs or have been tested in clinical trials. From dose-response measurements, 106 of the 12,657 compounds exhibit statistically significant antisickling at concentrations ranging from 31 nM to 10 μM. Compounds that inhibit sickling of trait cells are also effective with SCD cells. As many as 21 of the 106 antisickling compounds emerge as potential drugs. This estimate is based on a comparison of inhibitory concentrations with free concentrations of oral drugs in human serum. Moreover, the expected therapeutic potential for each level of inhibition can be predicted from measurements of sickling times for cells from individuals with sickle syndromes of varying severity. Our results should motivate others to develop one or more of these 106 compounds into drugs for treating SCD.
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Project description:The hypothesis in the current study is that the simultaneous direct in vivo testing of thousands to millions of systematically arranged mixture-based libraries will facilitate the identification of enhanced individual compounds. Individual compounds identified from such libraries may have increased specificity and decreased side effects early in the discovery phase. Testing began by screening ten diverse scaffolds as single mixtures (ranging from 17,340 to 4,879,681 compounds) for analgesia directly in the mouse tail withdrawal model. The "all X" mixture representing the library TPI-1954 was found to produce significant antinociception and lacked respiratory depression and hyperlocomotor effects using the Comprehensive Laboratory Animal Monitoring System (CLAMS). The TPI-1954 library is a pyrrolidine bis-piperazine and totals 738,192 compounds. This library has 26 functionalities at the first three positions of diversity made up of 28,392 compounds each (26 × 26 × 42) and 42 functionalities at the fourth made up of 19,915 compounds each (26 × 26 × 26). The 120 resulting mixtures representing each of the variable four positions were screened directly in vivo in the mouse 55 °C warm-water tail-withdrawal assay (ip administration). The 120 samples were then ranked in terms of their antinociceptive activity. The synthesis of 54 individual compounds was then carried out. Nine of the individual compounds produced dose-dependent antinociception equivalent to morphine. In practical terms what this means is that one would not expect multiexponential increases in activity as we move from the all-X mixture, to the positional scanning libraries, to the individual compounds. Actually because of the systematic formatting one would typically anticipate steady increases in activity as the complexity of the mixtures is reduced. This is in fact what we see in the current study. One of the final individual compounds identified, TPI 2213-17, lacked significant respiratory depression, locomotor impairment, or sedation. Our results represent an example of this unique approach for screening large mixture-based libraries directly in vivo to rapidly identify individual compounds.
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Project description:The identification of interactions between drugs and target proteins plays a key role in genomic drug discovery. In the present study, the quantitative binding affinities of drug-target pairs are differentiated as a measurement to define whether a drug interacts with a protein or not, and then a chemogenomics framework using an unbiased set of general integrated features and random forest (RF) is employed to construct a predictive model which can accurately classify drug-target pairs. The predictability of the model is further investigated and validated by several independent validation sets. The built model is used to predict drug-target associations, some of which were confirmed by comparing experimental data from public biological resources. A drug-target interaction network with high confidence drug-target pairs was also reconstructed. This network provides further insight for the action of drugs and targets. Finally, a web-based server called PreDPI-Ki was developed to predict drug-target interactions for drug discovery. In addition to providing a high-confidence list of drug-target associations for subsequent experimental investigation guidance, these results also contribute to the understanding of drug-target interactions. We can also see that quantitative information of drug-target associations could greatly promote the development of more accurate models. The PreDPI-Ki server is freely available via: http://sdd.whu.edu.cn/dpiki.
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