Synthetic Biology
We are employing engineering principles to model, design and build synthetic gene circuits and programmable cells, in order to create novel classes of diagnostics & therapeutics. We are also using deep learning approaches to discover new genetic parts and enhance the synthetic biology design process.
Antibiotics & AI
As part of the Antibiotics-AI Project, we are harnessing the power of artificial intelligence (AI) to discover novel classes of antibiotics and rapidly understand how they work. We are also using deep learning approaches for the de novo design of new antibiotics and the development of combination treatments.
The Collins Lab is part of the Institute for Medical Engineering and Science (IMES) and the Department of Biological Engineering at MIT, the Harvard-MIT Program in Health Sciences and Technology (HST), the Broad Institute of MIT and Harvard, and the Wyss Institute for Biologically Inspired Engineering at Harvard. At MIT, our lab is part of the Synthetic Biology Center, the Computational and Systems Biology Initiative, and the Microbiology Graduate Program.
RECENT PUBLICATIONS
A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation
Miguel A. Alcantar, Max A. English, Jacqueline A. Valeri, and James J. Collins
Molecular Cell (2024)
The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.
Discovery of antibiotics that selectively kill MEtabolically DORMANT BACTERIA
Erica J. Zheng, Jacqueline A. Valeri, Ian W. Andrews, Aarti Krishnan, Parijat Bandyopadhyay, Melis N. Anahtar, Alice Herneisen, Fabian Schulte, Brooke Linnehan, Felix Wong, Jonathan M. Stokes, Lars D. Renner, Sebastian Lourido and James J. Collins
Cell Chemical Biology (2024)
There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
Rapid, multiplexed, and enzyme-free nucleic acid detection using programmable aptamer-based RNA switches
Zhaoqing Yan, Amit Eshed, Anli A. Tang, Nery R. Arevalos, Zackary M. Ticktin, Soma Chaudhary, Duo Ma, Griffin McCutcheon, Yudan Li, Kaiyue Wu, Sanchari Saha, Jonathan Alcantar-Fernandez, Jose L. Moreno-Camacho, Abraham Campos-Romero, James J. Collins, Peng Yin and Alexander A. Green
Chem (2024)
Rapid, simple, and low-cost diagnostic technologies are crucial tools for combating infectious disease. We describe a class of aptamer-based RNA switches, or aptaswitches, that recognize target nucleic acid molecules and initiate the folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide an intense fluorescent readout without intervening enzymes, generating signals in as little as 5 min and enabling detection by eye with minimal equipment. Aptaswitches can be used to regulate the folding of seven fluorogenic aptamers, providing a general means of controlling aptamers and an array of multiplexable reporter colors. By coupling isothermal amplification reactions with aptaswitches, we reach sensitivities down to 1 RNA copy/μL in one-pot reactions. Application of multiplexed all-in-one reactions against RNA from clinical saliva samples yields an overall accuracy of 96.67% for detection of SARS-CoV-2 in 30 min. Aptaswitches are thus versatile tools for nucleic acid detection that are readily integrated into rapid diagnostic assays.
Machine learning for antimicrobial peptide identification and design
Fangping Wan, Felix Wong, James J. Collins and Cesar de la Fuente-Nunez
Nature Reviews Bioengineering (2024)
Artificial intelligence (AI) and machine learning (ML) models are being deployed in many domains of society and have recently reached the field of drug discovery. Given the increasing prevalence of antimicrobial resistance, as well as the challenges intrinsic to antibiotic development, there is an urgent need to accelerate the design of new antimicrobial therapies. Antimicrobial peptides (AMPs) are therapeutic agents for treating bacterial infections, but their translation into the clinic has been slow owing to toxicity, poor stability, limited cellular penetration and high cost, among other issues. Recent advances in AI and ML have led to breakthroughs in our abilities to predict biomolecular properties and structures and to generate new molecules. The ML-based modelling of peptides may overcome some of the disadvantages associated with traditional drug discovery and aid the rapid development and translation of AMPs. Here, we provide an introduction to this emerging field and survey ML approaches that can be used to address issues currently hindering AMP development. We also outline important limitations that can be addressed for the broader adoption of AMPs in clinical practice, as well as new opportunities in data-driven peptide design.
Discovery of a structural class of antibiotics with explainable deep learning
Felix Wong, Erica J. Zheng, Jacqueline A. Valeri, Nina M. Donghia, Melis N. Anahtar, Satotaka Omori, Alicia Li, Andres Cubillos-Ruiz, Aarti Krishnan, Wengong Jin, Abigail L. Manson, Jens Friedrichs, Ralf Helbig, Behnoush Hajian, Dawid K. Fiejtek, Florence F. Wagner, Holly H. Soutter, Ashlee M. Earl, Jonathan M. Stokes, Lars D. Renner and James J. Collins
Nature (2024)
The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis. Deep learning approaches have aided in exploring chemical spaces; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights.