James J Collins
Massachusetts Institute of Technology
H-index: 169
North America-United States
Description
James J Collins, With an exceptional h-index of 169 and a recent h-index of 114 (since 2020), a distinguished researcher at Massachusetts Institute of Technology, specializes in the field of synthetic biology, antibiotics, biological physics, bionanotechnology.
His recent articles reflect a diverse array of research interests and contributions to the field:
Machine learning for antimicrobial peptide identification and design
Discovery of a structural class of antibiotics with explainable deep learning
Discovery of antibiotics that selectively kill metabolically dormant bacteria
Rapid, multiplexed, and enzyme-free nucleic acid detection using programmable aptamer-based RNA switches
Engineering microbial division of labor for plastic upcycling
Autocatalytic base editing for RNA-responsive translational control
A self‐propagating, barcoded transposon system for the dynamic rewiring of genomic networks
Engineering synthetic phosphorylation signaling networks in human cells
Professor Information
University | Massachusetts Institute of Technology |
---|---|
Position | ; Wyss Institute Harvard; Broad Institute of & Harvard |
Citations(all) | 123425 |
Citations(since 2020) | 52140 |
Cited By | 90955 |
hIndex(all) | 169 |
hIndex(since 2020) | 114 |
i10Index(all) | 352 |
i10Index(since 2020) | 286 |
University Profile Page | Massachusetts Institute of Technology |
Research & Interests List
synthetic biology
antibiotics
biological physics
bionanotechnology
Top articles of James J Collins
Machine learning for antimicrobial peptide identification and design
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 …
Authors
Fangping Wan,Felix Wong,James J Collins,Cesar de la Fuente-Nunez
Published Date
2024/2/26
Discovery of a structural class of antibiotics with explainable deep learning
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 …
Authors
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,James J Collins
Journal
Nature
Published Date
2024/2/1
Discovery of antibiotics that selectively kill metabolically dormant bacteria
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 …
Authors
Erica J Zheng,Jacqueline A Valeri,Ian W Andrews,Aarti Krishnan,Parijat Bandyopadhyay,Alice Herneisen,Fabian Schulte,Brooke Linnehan,Felix Wong,Jonathan M Stokes,Lars D Renner,Sebastian Lourido,James J Collins
Journal
Cell Chemical Biology
Published Date
2023/11/15
Rapid, multiplexed, and enzyme-free nucleic acid detection using programmable aptamer-based RNA switches
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 …
Authors
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,Alexander A Green
Journal
Chem
Published Date
2024/4/12
Engineering microbial division of labor for plastic upcycling
Plastic pollution is rapidly increasing worldwide, causing adverse impacts on the environment, wildlife and human health. One tempting solution to this crisis is upcycling plastics into products with engineered microorganisms; however, this remains challenging due to complexity in conversion. Here we present a synthetic microbial consortium that efficiently degrades polyethylene terephthalate hydrolysate and subsequently produces desired chemicals through division of labor. The consortium involves two Pseudomonas putida strains, specializing in terephthalic acid and ethylene glycol utilization respectively, to achieve complete substrate assimilation. Compared with its monoculture counterpart, the consortium exhibits reduced catabolic crosstalk and faster deconstruction, particularly when substrate concentrations are high or crude hydrolysate is used. It also outperforms monoculture when …
Authors
Teng Bao,Yuanchao Qian,Yongping Xin,James J Collins,Ting Lu
Journal
Nature communications
Published Date
2023/9/26
Autocatalytic base editing for RNA-responsive translational control
Genetic circuits that control transgene expression in response to pre-defined transcriptional cues would enable the development of smart therapeutics. To this end, here we engineer programmable single-transcript RNA sensors in which adenosine deaminases acting on RNA (ADARs) autocatalytically convert target hybridization into a translational output. Dubbed DART VADAR (Detection and Amplification of RNA Triggers via ADAR), our system amplifies the signal from editing by endogenous ADAR through a positive feedback loop. Amplification is mediated by the expression of a hyperactive, minimal ADAR variant and its recruitment to the edit site via an orthogonal RNA targeting mechanism. This topology confers high dynamic range, low background, minimal off-target effects, and a small genetic footprint. We leverage DART VADAR to detect single nucleotide polymorphisms and modulate translation in …
Authors
Raphaël V Gayet,Katherine Ilia,Shiva Razavi,Nathaniel D Tippens,Makoto A Lalwani,Kehan Zhang,Jack X Chen,Jonathan C Chen,Jose Vargas-Asencio,James J Collins
Journal
Nature Communications
Published Date
2023/3/11
A self‐propagating, barcoded transposon system for the dynamic rewiring of genomic networks
In bacteria, natural transposon mobilization can drive adaptive genomic rearrangements. Here, we build on this capability and develop an inducible, self‐propagating transposon platform for continuous genome‐wide mutagenesis and the dynamic rewiring of gene networks in bacteria. We first use the platform to study the impact of transposon functionalization on the evolution of parallel Escherichia coli populations toward diverse carbon source utilization and antibiotic resistance phenotypes. We then develop a modular, combinatorial assembly pipeline for the functionalization of transposons with synthetic or endogenous gene regulatory elements (e.g., inducible promoters) as well as DNA barcodes. We compare parallel evolutions across alternating carbon sources and demonstrate the emergence of inducible, multigenic phenotypes and the ease with which barcoded transposons can be tracked longitudinally to …
Authors
Max A English,Miguel A Alcantar,James J Collins
Journal
Molecular Systems Biology
Published Date
2023/6/12
Engineering synthetic phosphorylation signaling networks in human cells
Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which “push-pull” motifs—reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities—are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense …
Authors
Xiaoyu Yang,Jason W Rocks,Kaiyi Jiang,Andrew J Walters,Kshitij Rai,Jing Liu,Jason Nguyen,Scott D Olson,Pankaj Mehta,James J Collins,Nichole M Daringer,Caleb J Bashor
Journal
bioRxiv
Published Date
2023/9/11
Professor FAQs
What is James J Collins's h-index at Massachusetts Institute of Technology?
The h-index of James J Collins has been 114 since 2020 and 169 in total.
What are James J Collins's top articles?
The articles with the titles of
Machine learning for antimicrobial peptide identification and design
Discovery of a structural class of antibiotics with explainable deep learning
Discovery of antibiotics that selectively kill metabolically dormant bacteria
Rapid, multiplexed, and enzyme-free nucleic acid detection using programmable aptamer-based RNA switches
Engineering microbial division of labor for plastic upcycling
Autocatalytic base editing for RNA-responsive translational control
A self‐propagating, barcoded transposon system for the dynamic rewiring of genomic networks
Engineering synthetic phosphorylation signaling networks in human cells
...
are the top articles of James J Collins at Massachusetts Institute of Technology.
What are James J Collins's research interests?
The research interests of James J Collins are: synthetic biology, antibiotics, biological physics, bionanotechnology
What is James J Collins's total number of citations?
James J Collins has 123,425 citations in total.
What are the co-authors of James J Collins?
The co-authors of James J Collins are George Church, George Q. Daley MD, PhD, Graham C Walker, Timothy Lu, Omar O. Abudayyeh, Farren Isaacs.