PROJECT SUMMARY/ABSTRACT
The dissemination of antibiotic resistance and the drying-up of antibiotic discovery pipelines threaten to
increase morbidity from routine medical procedures and worsen the spread of infectious diseases. The main
objective of this project is to address the dual challenges of antibiotic resistance and discovery by (1)
pinpointing the cellular pathways involved in antibiotic-induced bacterial cell death and (2) leveraging these
mechanistic data to discover and develop novel structural classes of antibiotics from chemical libraries of >11
million compounds. The working hypothesis is that antibiotic mechanisms of action (MoAs) manifest through
physical changes to cellular structures, and that approaches to antibiotic discovery which integrate this MoA
information can more reliably discover novel classes of antibiotics than current discovery pipelines. This
hypothesis will be tested in two specific aims: (1) develop novel methods of probing and perturbing antibiotic
lethality at the single-cell level (Years 1 to 4) and (2) develop a computational platform for deep learning
classes of new antibiotics which exploits the mechanisms of action of known antibiotics (Years 2 to 5). During
the first phase of this award, microfluidic, optical, and fluorescence microscopy will be used to record changes
to physical properties of cytoplasmic and cell envelope components in Escherichia coli cells treated with
various bactericidal antibiotics. Additional targeted experiments based on chemical perturbations, physical
perturbations, and genetic overexpression and knockout will inform physical and mathematical models, based
on multiscale continuum mechanics, that classify the phenotypes associated with antibiotic-induced cell death.
During the second phase of this award, a deep learning platform which predicts both antibiotic leads and their
predicted MoAs in silico will be developed. This mechanism-guided approach will be used to identify antibiotic
leads and their MoAs from vast chemical spaces, and leads will be experimentally validated in vitro against
laboratory strains and multidrug-resistant clinical isolates. Leads will be further investigated using human cell
cytotoxicity, hit-to-lead, pharmacokinetic, and in vivo mouse bacterial infection experiments.
A better understanding of antibiotic MoAs and the development of novel drug discovery efforts with detailed
mechanistic underpinnings fit NIH’s public health mission and have direct implications for the prevention and
treatment of infectious diseases. This work will establish a quantitative, model-guided platform for better
characterizing and discovering antibiotics, one which promises to offer a fertile source of mechanistic
information and chemical diversity. By providing the applicant the opportunity to develop his research career,
acquire new wet-lab experimental skills, undertake translational coursework, and receive mentorship from
leading experts in antibiotics, machine learning, biotechnology, and infectious diseases at MIT, the support and
training provided by this award will enable the applicant’s development as an independent researcher.