Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2021 Nov 20;16(12):2752–2756. doi: 10.1021/acschembio.1c00714

Antibacterial Discovery via Phenotypic DNA-Encoded Library Screening

Wesley G Cochrane †, Patrick R Fitzgerald ‡, Brian M Paegel †,§,*
PMCID: PMC8688339  NIHMSID: NIHMS1761445  PMID: 34806373

Abstract

graphic file with name cb1c00714_0005.jpg

The global rise of multidrug resistant infections poses an imminent, existential threat. Numerous pipelines have failed to convert biochemically active molecules into bona fide antibacterials, owing to a lack of chemical material with antibacterial-like physical properties in high-throughput screening compound libraries. Here, we demonstrate scalable design and synthesis of an antibacterial-like solid-phase DNA-encoded library (DEL, 7488 members) and facile hit deconvolution from whole-cell Escherichia coli and Bacillus subtilis cytotoxicity screens. The screen output identified two low-micromolar inhibitors of B. subtilis growth and recapitulated known structure–activity relationships of the fluoroquinolone antibacterial class. This phenotypic DEL screening strategy is also potentially applicable to adherent cells and will broadly enable the discovery and optimization of cell-active molecules.

Multidrug-resistant (MDR) pathogens are on the rise, while antibacterial discovery has stalled. The pharmaceutical industry has largely abandoned antibacterial discovery and development in the wake of substantial failures to produce clinical candidates in the postgenome era.1−4 This stands in sharp contrast to the record-setting numbers of non-antibacterial FDA drug approvals.5,6 Expanded efforts both to understand the nature of antibacterial drug efficacy7−9 and to develop technology for identifying novel antibacterial scaffolds10,11 are sorely needed in the face of this impending global health crisis.

Most approved antibacterials are natural products or their derivatives; therefore recent efforts to identify novel scaffolds have focused on screening libraries of synthetics. However, these efforts were manifestly unproductive.1,2 High-throughput screening (HTS) campaigns have yielded many biochemically active compounds, but their subsequent optimization for antibacterial activity has proven to be a Sisyphean task: hydrophobic interactions improve target binding, but increased hydrophilicity confers bacterial accumulation.4,12 Moreover, HTS collections are devoid of novel bacterial cell-active compounds,13,14 suggesting that alternative sources of chemical diversity with more advantageous property distributions would be key to rekindling antibacterial discovery.

DNA-encoded libraries (DELs)15,16 can provide ready access to chemical space that is underrepresented in HTS compound collections, and emergent screening modalities suggest potential opportunities for cellular screening. DEL affinity selection is a widely deployed hit finding technology that has produced several clinical candidates,17−19 including lead molecules targeting HTS-refractory protein–protein interactions20 and antibacterial targets.21 Additionally, recent technology development efforts have afforded activity-based screening of individual solid-phase DEL beads in microfluidic water-in-oil droplets.22−24 Integrating solid-phase DEL technology with bead diffusion assays25−28 would address two fundamental challenges of antibacterial discovery: libraries could feature designable chemical diversity and that diversity could be screened directly for cellular activity.

We designed a 3-cycle solid-phase DEL (7488 members) to sample the physicochemical property space of known antibacterials. Hydrophobicity and molecular weight were key design considerations, as FDA-approved antibacterial drugs are less hydrophobic and have higher molecular masses than CNS-active FDA-approved drugs (Figure 1A), among other divergent properties (Figures S1 and S2). The DEL was prepared by split-and-pool synthesis with each building block coupling encoded by ligating a corresponding oligonucleotide (Figure 1B, Table S1, Scheme S1).22 Each DEL bead harbors DNA encoding tags and a single library member (∼106 copies and 800 pmol, respectively) covalently attached to resin (Figure 1B) via photocleavable linker, allowing mild (λ = 365 nm) library member release from the bead for bioactivity assays.

Figure 1.

Figure 1

Open in a new tab

Physicochemical properties of the synthesized DEL and its general structure. (A) Antibacterials tend to exhibit higher molecular mass and lower hydrophobicity (cLogP) compared to CNS-active drugs. The DEL properties skew toward those of approved antibacterials. (B) Individual DEL beads polyvalently display a combinatorially generated small molecule and a structure-encoding DNA tag. The DNA encoding tag contains forward and reverse primer binding sites (FWD, REV) flanking the small molecule synthesis coding region. Molecules are photochemically released from the bead to interact with the screening target while the DNA tag remains attached to the bead for sequencing and compound decoding.

We screened the DEL for antibacterial compounds using a whole cell bead diffusion assay and identified multiple cell-active compounds. In the assay modified from previous approaches,25−28 UV irradiation of DEL beads distributed on inoculated agar plates liberated compounds from beads, allowing their diffusion through the surrounding agar to yield a sufficient screening hit rate. This approach for phenotypic DEL screening was validated using photocleavable ciprofloxacin (PC-cipro) beads. PC-cipro beads only formed growth inhibition zones on bacterial plates upon UV irradiation, and the same UV irradiation did not affect bacterial growth (Figures S3 and S4). Similarly, a growth inhibition zone (GIZ) formed around DEL hit beads after overnight incubation (Figure S5). In total, 97 GIZ-forming hit beads (24 Escherichia coli, 73 Bacillus subtilis) were isolated from screening approximately 85 000 beads (45 000 E. coli, 40 000 B. subtilis; 0.05% and 0.2% hit rates, respectively; Table S2). Hit bead DNA tags were PCR amplified, sequenced, and decoded. Position 2 of the hit pool was overrepresented by amine-containing building blocks (65% of library; 98% of hits) but was devoid of related hydroxyl analogues (19% of library; 0% of hits). A fluoronaphthyridone building block was highly enriched at position 3 (53/97 hits, Figure 2A). Structurally related pyrrolopiperidine and piperazine monomers in position 2 appeared frequently with the fluoronaphthyridone in screening hits (Figure 2B,C), suggesting that the position 1 building block identity may be inconsequential to these compounds’ activity. These collected structure–activity trends guided the selection and subsequent experimental characterization of the key hits’ antibacterial properties.

Figure 2.

Figure 2

Open in a new tab

Antibacterial phenotypic DEL screening hit collection analysis. (A) In comparing all the unique combinations of building blocks at positions 1 and 2, the heat map of the antibacterial screening hit output indicates strong conservation of a fluoronaphthyridone building block in position 3 (dashed vertical line). Pyrrolopiperidinyl (+) and piperazinyl (*) building blocks in position 2 were highly represented in combination with a position 3 fluoronaphthyridone. The k class indicates the number of independent observations of a particular molecule in the screening hit pool. (B) Seven out of 12 possible position 1 building blocks were observed in the hit pool in combination with the position 2 piperazinyl and position 3 fluoronaphthyridone. (C) In an analysis of position 2/3 truncates (disregarding the position 1 building block identity), piperazinyl and pyrrolopiperidinyl naphthyridone analogues are present in 10 and 18 hit structures, respectively. None of the other 624 possible position 2/3 truncates were represented in more than 5 hit structures.

Hit characterization through scaled synthesis and evaluation of MIC in broth microdilution assays validated the results of cellular DEL screening. Compounds were selected for synthesis to test two hypotheses: (1) that hits observed the most times during screening are bona fide antibacterials and (2) that structure–activity trends suggested by the hit output can guide compound optimization. Following synthesis, purified hit compounds were assayed for antibacterial activity against the E. coli and B. subtilis strains used for DEL screening (Table 1). Hits 1 and 2, the only high-quality hits (each observed in the hit pool n = 3 times)29 that did not utilize the fluoronaphthyridone moiety, were active against B. subtilis with low-micromolar MICs, but only 2 (a doubly acylated side product) exhibited activity against E. coli. While these compounds are potent for primary phenotypic screening hits, the related screening output data did not provide clear SAR trends, and thus we did not pursue them further.

Table 1. Characterization of Antibacterial Hits and Derivatives in Broth Microdilution Assaysa.

graphic file with name cb1c00714_0003.jpg

Open in a new tab
a

Reported as MIC (μg/mL).

Synthesis of fluoronaphthyridones 3 and 6 revealed unanticipated active products. The fluoronaphthyridone coupled with amines through nucleophilic substitution of the C7 chlorine, not the planned amidation. These substitution products both validated as antibacterials, and piperazine 6 was more potent. Hit collection SAR suggested that the isoindoline was potentially dispensable, and its removal in 4 and 7 dramatically improved antibacterial activity. Truncation of the primary amides further increased potency of 5 and 8 by ∼20- and ∼50-fold, respectively. Truncation of the position 2 building block from 5 and 8 yielded 9, which had substantially reduced potency and thus evidenced the functional role of the cationic amine. Hit synthesis collectively validated the DEL phenotypic screening platform in both establishing 1 and 2 as Gram-positive antibacterials and deriving 8 as the most potent compound based on screening SAR trends.

The promising results demonstrating activity of 8 against screening strains spurred further mechanistic studies and evaluation of its potential as a broad spectrum antibacterial. Compound 8 inhibited E. coli DNA gyrase biochemical activity, albeit with one-fifth the potency of ciprofloxacin (IC50 = 1.43 ± 0.06 and 0.28 ± 0.04 μM, respectively) (Figure S6). Antibacterial disk diffusion assays were conducted to evaluate 5 and 8 against three diverse bacterial panels: standard antibacterial susceptibility ATCC organisms, clinical isolates, and carbapenem-resistant (MDR) enterobacter strains. Disks impregnated with 5 or 8 generated GIZs on lawns of various Gram positive (B. subtilis, Staphylococcus aureus) and Gram negative (E. coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, Serratia marcescens) bacteria. Furthermore, 8 generated GIZs for 2/4 clinical isolates but was inactive against 10/11 MDR strains (Table 2). For all strains tested, GIZ measurements of ciprofloxacin and 8 agreed within 3 mm, while 5 was uniformly less potent.

Table 2. Antibacterial Disk Susceptibility Testing against ATCC Control and Clinical Strainsa.

organism 8 (5 μg) ciprofloxacin (5 μg) cefiderocol (30 μg)
ATCC Control Strains
E. coli ATCC 25922 31 33 25
B. subtilis ATCC 23857 28 31 15
P. aeruginosa ATCC 27853 24 27 25
K. pneumoniae ATCC 700603 20 21 23
K. pneumoniae BAA 1705 (CRE) b b 19
S. aureus ATCC 25923 20 23 9
Clinical Strains
E. coli b b 25
K. pneumoniae 23 23 26
K. pneumoniae b b 25
S. marcescens 20 18 29
Open in a new tab
a

Reported as GIZ diameter (mm). CRE = Carbapenem-Resistant Enterobacterales.

b

Undetectable.

Fluoronaphthyridone 8 is a broad-spectrum synthetic antibacterial compound and, together with the other structures reported here, represents the first hits from cell-based DEL screening. Though its synthesis was previously described,30 its characterization was limited. Our analysis found that 8 and ciprofloxacin exhibited nearly identical activity against all bacterial strains tested. Failure of 8 in disk diffusion studies against the carbapenem-resistant strain panel underscores the challenge of developing therapeutics to address MDR strains. These strains were resistant to most classes of antibacterials tested, and some were even resistant to cefiderocol, an antibacterial of last resort.31,32 This encouraging demonstration provides compelling motivation to design DELs around known antibacterial scaffolds and conduct phenotypic screens on clinically relevant MDR strains.

This study introduces cellular DEL screening capabilities to the expanding repertoire of activity-based DEL modalities.23,24 Previous combinatorial library screening techniques have shown that cells (prokaryotic or eukaryotic) and beads can be distributed either in wells or on culture dishes to identify active library members based on cellular response in the periphery of the bead.25,33−36 These studies inspired our work, which further integrated the pivotal advantages of DEL. DNA tags were readily amplified, sequenced, and decoded (97% and 35% loss after incubation with B. subtilis and E. coli, respectively). Recapitulating library synthesis conditions when preparing hit compounds revealed that an unplanned side reaction yielded the most potent hits, highlighting the importance of encoding chemical synthesis.

Although synthetic antibacterial discovery via HTS has been fraught with seemingly insurmountable challenges owing to the limited compound library physicochemical property space, phenotypic DEL screening offers a viable path forward. Indeed, synthetic compound collections have been completely and unsuccessfully mined for broad-spectrum antibacterials, and the rare discoveries of novel natural product antibacterial scaffolds are considered to be globally significant scientific advances.10 DEL technology enables efficient design, synthesis, and scouting of antibacterial-like and potentially many other novel chemical spaces. For example, amines have been observed to confer membrane permeability in Gram negative bacteria,7,37 and this hypothesis was supported by the enrichment of amine-containing compounds in the hit collection of this screen. Moreover, additional amine-enriched space is readily accessed using well-established38 and more recently discovered DNA-compatible reactions.39−41 Exploration of these new spaces will demand further technology development to overcome the present bottleneck of manual hit detection and bead picking. We anticipate that these approaches will begin to unravel the complex interplay between chemical properties, bacterial cell pharmacokinetics, and the desired broad-spectrum antibacterial response.

Acknowledgments

This work was funded by NIH grant awards to B.M.P (GM120491, GM140890). We gratefully acknowledge helpful discussions with A. Price, who was supported by an NIH K25 career development award (AI128000). We thank C. Bittencourt and L. Thrupp (UCI Health Clinical Microbiology Laboratory) for strain selection and clinical isolate testing.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschembio.1c00714.

  • Experimental methods including DEL synthesis and screening, hit deconvolution, and synthesis, antibacterial activity assays, and supplementary tables and figures describing DEL synthesis, DEL BB and hit structures, DEL screening data, and activity assay data (PDF)

Author Present Address

⊥ The Salk Institute for Biological Studies, La Jolla, California 92037, United States

The authors declare the following competing financial interest(s): B.M.P. declares a significant financial interest in 1859, a company seeking to commercialize some aspects of this work.

Supplementary Material

cb1c00714_si_001.pdf (4.9MB, pdf)

References

  1. Payne D. J.; Gwynn M. N.; Holmes D. J.; Pompliano D. L. Drugs for Bad Bugs: Confronting the Challenges of Antibacterial Discovery. Nat. Rev. Drug Discovery 2007, 6, 29–40. 10.1038/nrd2201. [DOI] [PubMed] [Google Scholar]
  2. Tommasi R.; Brown D. G.; Walkup G. K.; Manchester J. I.; Miller A. A. ESKAPEing the Labyrinth of Antibacterial Discovery. Nat. Rev. Drug Discovery 2015, 14, 529–542. 10.1038/nrd4572. [DOI] [PubMed] [Google Scholar]
  3. Tommasi R.; Iyer R.; Miller A. A. Antibacterial Drug Discovery: Some Assembly Required. ACS Infect. Dis. 2018, 4, 686–695. 10.1021/acsinfecdis.8b00027. [DOI] [PubMed] [Google Scholar]
  4. Brown E. D.; Wright G. D. Antibacterial Drug Discovery in the Resistance Era. Nature 2016, 529, 336–343. 10.1038/nature17042. [DOI] [PubMed] [Google Scholar]
  5. Mullard A. 2019 FDA Drug Approvals. Nat. Rev. Drug Discovery 2020, 19, 79–84. 10.1038/d41573-020-00001-7. [DOI] [PubMed] [Google Scholar]
  6. Plackett B. Why Big Pharma has Abandoned Antibiotics. Nature 2020, 586, S50–S52. 10.1038/d41586-020-02884-3. [DOI] [Google Scholar]
  7. Richter M. F.; Drown B. S.; Riley A. P.; Garcia A.; Shirai T.; Svec R. L.; Hergenrother P. J. Predictive Compound Accumulation Rules Yield a Broad-Spectrum Antibiotic. Nature 2017, 545, 299–304. 10.1038/nature22308. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Zhao S.; Adamiak J. W.; Bonifay V.; Mehla J.; Zgurskaya H. I.; Tan D. S. Defining New Chemical Space for Drug Penetration into Gram-Negative Bacteria. Nat. Chem. Biol. 2020, 16, 1293–1302. 10.1038/s41589-020-00674-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Lin J.; Zhou D.; Steitz T. A.; Polikanov Y. S.; Gagnon M. G. Ribosome-Targeting Antibiotics: Modes of Action, Mechanisms of Resistance, and Implications for Drug Design. Annu. Rev. Biochem. 2018, 87, 451. 10.1146/annurev-biochem-062917-011942. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ling L. L.; Schneider T.; Peoples A. J.; Spoering A. L.; Engels I.; Conlon B. P.; Mueller A.; Schäberle T. F.; Hughes D. E.; Epstein S.; et al. A New Antibiotic Kills Pathogens Without Detectable Resistance. Nature 2015, 517, 455–459. 10.1038/nature14098. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Schmitt S.; Montalbán-López M.; Peterhoff D.; Deng J.; Wagner R.; Held M.; Kuipers O. P.; Panke S. Analysis of Modular Bioengineered Antimicrobial Lanthipeptides at Nanoliter Scale. Nat. Chem. Biol. 2019, 15, 437. 10.1038/s41589-019-0250-5. [DOI] [PubMed] [Google Scholar]
  12. Brown D. G.; May-Dracka T. L.; Gagnon M. M.; Tommasi R. Trends and Exceptions of Physical Properties on Antibacterial Activity for Gram-Positive and Gram-Negative Pathogens. J. Med. Chem. 2014, 57, 10144–10161. 10.1021/jm501552x. [DOI] [PubMed] [Google Scholar]
  13. Fisher J. F.; Mobashery S. Endless Resistance. Endless Antibiotics?. MedChemComm 2016, 7, 37–49. 10.1039/C5MD00394F. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Gwynn M. N.; Portnoy A.; Rittenhouse S. F.; Payne D. J. Challenges of Antibacterial Discovery revisited. Ann. N. Y. Acad. Sci. 2010, 1213, 5–19. 10.1111/j.1749-6632.2010.05828.x. [DOI] [PubMed] [Google Scholar]
  15. Clark M. A.; Acharya R. A.; Arico-Muendel C. C.; Belyanskaya S. L.; Benjamin D. R.; Carlson N. R.; Centrella P. A.; Chiu C. H.; Creaser S. P.; Cuozzo J. W.; et al. Design, Synthesis and Selection of DNA-Encoded Small-Molecule Libraries. Nat. Chem. Biol. 2009, 5, 647–654. 10.1038/nchembio.211. [DOI] [PubMed] [Google Scholar]
  16. Mannocci L.; Zhang Y.; Scheuermann J.; Leimbacher M.; De Bellis G.; Rizzi E.; Dumelin C.; Melkko S.; Neri D. High-Throughput Sequencing Allows the Identification of Binding Molecules Isolated From DNA-Encoded Chemical Libraries. Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 17670–17675. 10.1073/pnas.0805130105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Belyanskaya S. L.; Ding Y.; Callahan J. F.; Lazaar A. L.; Israel D. I. Discovering Drugs with DNA-Encoded Library Technology: From Concept to Clinic with an Inhibitor of Soluble Epoxide Hydrolase. ChemBioChem 2017, 18, 837–842. 10.1002/cbic.201700014. [DOI] [PubMed] [Google Scholar]
  18. Harris P. A.; Berger S. B.; Jeong J. U.; Nagilla R.; Bandyopadhyay D.; Campobasso N.; Capriotti C. A.; Cox J. A.; Dare L.; Dong X.; et al. Discovery of a First-in-Class Receptor Interacting Protein 1 (RIP1) Kinase Specific Clinical Candidate (GSK2982772) for the Treatment of Inflammatory Diseases. J. Med. Chem. 2017, 60, 1247–1261. 10.1021/acs.jmedchem.6b01751. [DOI] [PubMed] [Google Scholar]
  19. Gironda-Martínez A.; Donckele E. J.; Samain F.; Neri D. DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges. ACS Pharmacol. Transl. Sci. 2021, 4, 1265–1279. 10.1021/acsptsci.1c00118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kollmann C. S.; Bai X.; Tsai C. H.; Yang H.; Lind K. E.; Skinner S. R.; Zhu Z.; Israel D. I.; Cuozzo J. W.; Morgan B. A.; et al. Application of Encoded Library Technology (ELT) to a Protein-Protein Interaction Target: Discovery of a Potent Class of Integrin Lymphocyte Function-Associated Antigen 1 (LFA-1) Antagonists. Bioorg. Med. Chem. 2014, 22, 2353–2365. 10.1016/j.bmc.2014.01.050. [DOI] [PubMed] [Google Scholar]
  21. Machutta C. A.; Kollmann C. S.; Lind K. E.; Bai X.; Chan P. F.; Huang J.; Ballell L.; Belyanskaya S.; Besra G. S.; Barros-Aguirre D.; et al. Prioritizing Multiple Therapeutic Targets in Parallel Using Automated DNA-Encoded Library Screening. Nat. Commun. 2017, 8, 16081. 10.1038/ncomms16081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. MacConnell A. B.; McEnaney P. J.; Cavett V. J.; Paegel B. M. DNA-Encoded Solid-Phase Synthesis: Encoding Language Design and Complex Oligomer Library Synthesis. ACS Comb. Sci. 2015, 17, 518–534. 10.1021/acscombsci.5b00106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Cochrane W.; Malone M. L.; Dang V. Q.; Cavett V. J.; Satz A. L.; Paegel B. M. Activity-Based DNA-Encoded Library Screening. ACS Comb. Sci. 2019, 21, 425–435. 10.1021/acscombsci.9b00037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hackler A. L.; FitzGerald F. G.; Dang V. Q.; Satz A. L.; Paegel B. M. Off-DNA DNA-Encoded Library Affinity Screening. ACS Comb. Sci. 2020, 22, 25–34. 10.1021/acscombsci.9b00153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Silen J. L.; Lu A. T.; Solas D. W.; Gore M. A.; Maclean D.; Shah N. H.; Coffin J. M.; Bhinderwala N. S.; Wang Y.; Tsutsui K. T.; et al. Screening for Novel Antimicrobials from Encoded Combinatorial Libraries by Using a Two-Dimensional Agar Format. Antimicrob. Agents Chemother. 1998, 42, 1447–1453. 10.1128/AAC.42.6.1447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Fluxà V. S.; Maillard N.; Page M. G. P.; Reymond J.-L. Bead Diffusion Assay for Discovering Antimicrobial Cyclic Peptides. Chem. Commun. 2011, 47, 1434–1436. 10.1039/C0CC04670A. [DOI] [PubMed] [Google Scholar]
  27. Fisher K. J.; Turkett J. A.; Corson A. E.; Bicker K. L. Peptoid Library Agar Diffusion (PLAD) Assay for the High-Throughput Identification of Antimicrobial Peptoids. ACS Comb. Sci. 2016, 18, 287–291. 10.1021/acscombsci.6b00039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Xiao Q.; Pei D. High-Throughput Synthesis and Screening of Cyclic Peptide Antibiotics. J. Med. Chem. 2007, 50, 3132–3137. 10.1021/jm070282e. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. MacConnell A. B.; Paegel B. M. Poisson Statistics of Combinatorial Library Sampling Predict False Discovery Rates of Screening. ACS Comb. Sci. 2017, 19, 524–532. 10.1021/acscombsci.7b00061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Miyamoto T.; Egawa H.; Shibamori K.-I.; Matsuraoto J.-I. Synthesis and Reactions of 7-Substituted 1-Cyclopropyl-6-Fluoro-1,4-Dihydro-4-Oxo-1,8-Naphthyridine-3-Carboxylic Acids as an Antibacterial Agent. J. Heterocycl. Chem. 1987, 24, 1333–1339. 10.1002/jhet.5570240520. [DOI] [Google Scholar]
  31. Zhanel G. G.; Golden A. R.; Zelenitsky S.; Wiebe K.; Lawrence C. K.; Adam H. J.; Idowu T.; Domalaon R.; Schweizer F.; Zhanel M. A.; et al. Cefiderocol: A Siderophore Cephalosporin With Activity Against Carbapenem-Resistant and Multidrug-Resistant Gram-Negative Bacilli. Drugs 2019, 79, 271–289. 10.1007/s40265-019-1055-2. [DOI] [PubMed] [Google Scholar]
  32. Bassetti M.; Echols R.; Matsunaga Y.; Ariyasu M.; Doi Y.; Ferrer R.; Lodise T. P.; Naas T.; Niki Y.; Paterson D. L.; et al. Efficacy and Safety of Cefiderocol or Best Available Therapy for the Treatment of Serious Infections Caused by Carbapenem-Resistant Gram-Negative Bacteria (CREDIBLE-CR): a Randomised, Open-Label, Multicentre, Pathogen-Focused, Descriptive, Phase 3 Trial. Lancet Infect. Dis. 2021, 21, 226–240. 10.1016/S1473-3099(20)30796-9. [DOI] [PubMed] [Google Scholar]
  33. Shih T.-C.; Liu R.; Fung G.; Bhardwaj G.; Ghosh P. M.; Lam K. S. A Novel Galectin-1 Inhibitor Discovered through One-Bead Two-Compound Library Potentiates the Antitumor Effects of Paclitaxel in vivo. Mol. Cancer Ther. 2017, 16, 1212–1223. 10.1158/1535-7163.MCT-16-0690. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Borchardt A.; Liberles S. D.; Biggar S. R.; Crabtree G. R.; Schreiber S. L. Small Molecule-Dependent Genetic Selection in Stochastic Nanodroplets as a Means of Detecting Protein-Ligand Interactions on a Large Scale. Chem. Biol. 1997, 4, 961–968. 10.1016/S1074-5521(97)90304-5. [DOI] [PubMed] [Google Scholar]
  35. You A. J.; Jackman R. J.; Whitesides G. M.; Schreiber S. L. A Miniaturized Arrayed Assay Format for Detecting Small Molecule-Protein Interactions in Cells. Chem. Biol. 1997, 4, 969–975. 10.1016/S1074-5521(97)90305-7. [DOI] [PubMed] [Google Scholar]
  36. Oldenburg K. R.; Vo K. T.; Ruhland B.; Schatz P. J.; Yuan Z. A Dual Culture Assay for Detection of Antimicrobial Activity. J. Biomol. Screening 1996, 1, 123–130. 10.1177/108705719600100305. [DOI] [Google Scholar]
  37. Parker E. N.; Drown B. S.; Geddes E. J.; Lee H. Y.; Ismail N.; Lau G. W.; Hergenrother P. J. Implementation of Permeation Rules Leads to a FabI Inhibitor with Activity Against Gram-Negative Pathogens. Nat. Microbiol. 2020, 5, 67–75. 10.1038/s41564-019-0604-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Malone M. L.; Paegel B. M. What is a “DNA-Compatible” Reaction?. ACS Comb. Sci. 2016, 18, 182–187. 10.1021/acscombsci.5b00198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Flood D. T.; Asai S.; Zhang X.; Wang J.; Yoon L.; Adams Z. C.; Dillingham B. C.; Sanchez B. B.; Vantourout J. C.; Flanagan M. E.; et al. Expanding Reactivity in DNA-Encoded Library Synthesis via Reversible Binding of DNA to an Inert Quaternary Ammonium Support. J. Am. Chem. Soc. 2019, 141, 9998–10006. 10.1021/jacs.9b03774. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Westphal M. V.; Hudson L.; Mason J. W.; Pradeilles J. A.; Zécri F. J.; Briner K.; Schreiber S. L. Water-Compatible Cycloadditions of Oligonucleotide-Conjugated Strained Allenes for DNA-Encoded Library Synthesis. J. Am. Chem. Soc. 2020, 142, 7776–7782. 10.1021/jacs.9b13186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Ruff Y.; Martinez R.; Pellé X.; Nimsgern P.; Fille P.; Ratnikov M.; Berst F. An Amphiphilic Polymer-Supported Strategy Enables Chemical Transformations under Anhydrous Conditions for DNA-Encoded Library Synthesis. ACS Comb. Sci. 2020, 22, 120–128. 10.1021/acscombsci.9b00164. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

cb1c00714_si_001.pdf (4.9MB, pdf)

Articles from ACS Chemical Biology are provided here courtesy of American Chemical Society

RESOURCES