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Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii

Nature Chemical Biology volume 19, pages 1342–1350 (2023)Cite this article

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Abstract

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

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Fig. 1: Machine learning-guided discovery of abaucin.
Fig. 2: Abaucin has a narrow phylogenetic spectrum of antibacterial activity.
Fig. 3: Abaucin inhibits lipoprotein trafficking in A. baumannii.
Fig. 4: Abaucin can suppress A. baumannii infection in a wound model.

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Data Availability

GenBank accession numbers for sequencing of abaucin-resistant mutants are BankIt2629921 – OP677864, OP677865, OP677866 and OP677867. GEO accession numbers for RNA sequencing datasets are GSE214305 – GSM6603484, GSM6603485, GSM6603486, GSM6603487, GSM6603488, GSM6603489 and GSM6603490. Source data are provided with this paper.

Code Availability

All custom code used for antibiotic prediction is open source and can be accessed without restriction at https://github.com/chemprop/chemprop. A cloned snapshot used for this paper is available at https://github.com/GaryLiu152/chemprop_abaucin. All commercial software used is described in Methods. Source data are provided with this paper.

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Acknowledgements

We thank S. French from McMaster University for technical assistance with fluorescence microscopy experiments. This work was supported by the David Braley Centre for Antibiotic Discovery (to J.M.S.); the Weston Family Foundation (POP and Catalyst to J.M.S.); the Audacious Project (to J.J.C. and J.M.S.); the C3.ai Digital Transformation Institute (to R.B.); the Abdul Latif Jameel Clinic for Machine Learning in Health (to R.B.); the DTRA Discovery of Medical Countermeasures Against New and Emerging (DOMANE) threats program (to R.B.); the DARPA Accelerated Molecular Discovery program (to R.B.); the Canadian Institutes of Health Research (FRN-156361 to B.K.C.); Genome Canada GAPP (OGI-146 to M.G.S.); the Canadian Institutes of Health Research (FRN-148713 to M.G.S.); the Faculty of Health Sciences of McMaster University (to J.M.); the Boris Family (to J.M.); a Marshall Scholarship (to K.S.); and the DOE BER (DE-FG02-02ER63445 to A.C-P.).

Author information

Author notes

  1. These authors contributed equally: Gary Liu, Denise B. Catacutan, Khushi Rathod.

Authors and Affiliations

  1. Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, Canada

    Gary Liu, Denise B. Catacutan, Khushi Rathod, Jody C. Mohammed, Meghan Fragis, Kenneth Rachwalski, Jakob Magolan, Brian K. Coombes & Jonathan M. Stokes

  2. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Kyle Swanson, Wengong Jin, Tommi Jaakkola & Regina Barzilay

  3. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Anush Chiappino-Pepe & James J. Collins

  4. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Anush Chiappino-Pepe

  5. Department of Medicine, Department of Biochemistry and Biomedical Sciences, Farncombe Family Digestive Health Research Institute, McMaster University, Hamilton, Ontario, Canada

    Saad A. Syed & Michael G. Surette

  6. Department of Chemistry and Chemical Biology, McMaster University, Hamilton, Ontario, Canada

    Meghan Fragis & Jakob Magolan

  7. Abdul Latif Jameel Clinic for Machine Learning in Health, Massachusetts Institute of Technology, Cambridge, MA, USA

    Regina Barzilay & James J. Collins

  8. Department of Biological Engineering, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    James J. Collins

  9. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    James J. Collins

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Contributions

J.M.S. and J.J.C. conceptualized the study; J.M.S., G.L., K.S. and W.J. performed model building and training; J.M.S., D.B.C., K.R. and A.C-P. performed mechanistic investigations; J.M.S., K.R. and S.A.S. performed spectrum of activity experiments; J.C.M. conducted mouse model experiments; M.F. performed chemical synthesis; J.M.S. and J.J.C. wrote the paper; J.M.S., J.J.C., R.B., T.J., M.G.S., B.K.C. and J.M. supervised the research.

Corresponding authors

Correspondence to James J. Collins or Jonathan M. Stokes.

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Competing interests

J.M.S. is cofounder and scientific director of Phare Bio. J.J.C. is cofounder and scientific advisory board chair of Phare Bio. J.J.C. is cofounder and scientific advisory board chair of Enbiotix. The other authors declare no competing interests.

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Nature Chemical Biology thanks Jean Francois Collet and the other, anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Model training data and prediction.

(a) Replicate plot showing primary screening data of 7,684 small molecules for those that inhibited the growth of A. baumannii ATCC 17978 in LB medium at 50 µM. (b) Rank-ordered growth inhibition data of the prioritized 240 molecules from our prediction set that were selected for empirical validation (top); rank-ordered growth inhibition data of the 240 predicted molecules with the lowest prediction score (middle); rank-ordered growth inhibition data of the 240 predicted molecules with the highest prediction score that were not found in the training dataset (bottom). Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted. Dashed horizontal line represents the stringent hit cut-off of >80% growth inhibition at 50 µM. (c) Growth inhibition of A. baumannii by abaucin (blue) and serdemetan (red) in LB medium. Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted. The structure of serdemetan is shown. (d) Growth kinetics of A. baumannii cells after treatment with abaucin at varying concentrations for 6 hours. Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted.

Extended Data Fig. 2 Antibacterial activity of abaucin against human commensal species.

(a) Growth inhibition of A. baumannii ATCC 17978 by ampicillin (blue) and ciprofloxacin (red) in LB medium. Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted. (b) Growth inhibition of B. breve by abaucin. Experiments were conducted in biological duplicate. (c) Growth inhibition of B. longum by abaucin. Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted. (d) Non-validated (see Fig. 2e) growth inhibition of E. lenta by abaucin. Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted.

Extended Data Fig. 3 Abaucin mechanism of action.

(a–h) Growth inhibition of wildtype A. baumannii (WT) and the four independent abaucin-resistant mutants by a collection of diverse antibiotics. From left to right for each plot, the mutants are: A362T variant 1, Y394F, intergenic, and A362T variant 2. Experiments were conducted in biological duplicate. Note that the abaucin-resistant mutants do not display cross-resistance to other antibiotics. (i) Structural prediction of wildtype A. baumannii LolE using RoseTTAFold (bottom), with the structural error estimate of each amino acid (top). Position 362 is highlighted orange and resides in a disordered region of the protein. (j) same as (i), except with the Y362T abaucin-resistant mutant of LolE. (k) RNA sequencing of wildtype A. baumannii treated with 5x MIC abaucin for 4.5 hr (top) or 6 hr (bottom). Data are the mean of biological duplicates. Transcript abundance is normalized to no-drug control cultures grown in identical conditions. Vertical black lines show statistical significance cut-off values. Note the highly significant downregulation of genes involved in the electron transport chain and transmembrane ion transport. (l) Growth inhibition of A. baumannii harboring an empty CRISPRi vector (red), or three distinct sgRNAs targeting lolE (blue, teal, and green). All strains were grown in LB medium without induction. Experiments were conducted in biological duplicate. Individual replicates with means connected are plotted. (m) qPCR quantifying the expression of lolE relative to the housekeeping gene gltA (left) and gyrB (right) in all four abaucin resistant mutants, normalized to wildtype A. baumannii. Experiments were conducted in biological duplicate with technical triplicates. Bar height represents mean expression.

Supplementary information

Supplementary Information

Supplementary Tables 1–7 and Note.

Reporting Summary

Supplementary Data

Supplementary Data 1: Growth inhibition data against A. baumannii for model training. Supplementary Data 2: Model prediction scores of compounds in the Drug Repurposing Hub. Supplementary Data 3: Experimental validation of (prioritized/poorest/top 240) predictions from the Drug Repurposing Hub. Supplementary Data 4: GO enrichment for up- and down-regulated transcripts in A. baumannii treated with 5x MIC abaucin.

Source data

Source Data Fig. 4a

Raw data of measured bacterial load from mouse wound infection models.

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Liu, G., Catacutan, D.B., Rathod, K. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol 19, 1342–1350 (2023). https://doi.org/10.1038/s41589-023-01349-8

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