| NSF Org: |
CCF Division of Computing and Communication Foundations |
| Recipient: |
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| Initial Amendment Date: | July 11, 2019 |
| Latest Amendment Date: | August 18, 2022 |
| Award Number: | 1909536 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Stephanie Gage
sgage@nsf.gov (703)292-4748 CCF Division of Computing and Communication Foundations CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2019 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $515,790.00 |
| Total Awarded Amount to Date: | $631,393.00 |
| Funds Obligated to Date: |
FY 2020 = $16,000.00 FY 2022 = $99,603.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
80 GEORGE ST MEDFORD MA US 02155-5519 (617)627-3696 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
200 College Ave Medford MA US 02155-5530 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | FET-Fndtns of Emerging Tech |
| Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The engineering of microbial cells through synthetic biology promises to advance the production of high-volume commodity chemicals such as biopolymers, fuels, nutraceuticals, therapeutics, and other specialty products. Increasing understanding of metabolic and regulatory networks underlying microbial physiology hinges on developing metabolic models that capture enzymatic cellular activity. Despite significant progress in sequencing technology and model reconstruction, there are many cellular enzymatic activities that remain unknown. The hypothesis underlying this project is that undocumented promiscuous activity of enzymes results in the formation of unexpected reaction byproducts. This phenomenon is frequently observed by synthetic biologists, and sometimes exploited during design through ad hoc experimental efforts. However, there are currently limited ways to predict, analyze, or mitigate the effects of enzyme promiscuity.
This project develops tools to predict stoichiometrically balanced reactions that reflect undocumented promiscuous enzymatic cellular activities. These reactions can be utilized to augment existing metabolic models and improve design tools. Further, the project experimentally validates the augmented models by engineering a microbial host to synthesize desirable chemicals and then analyzing host-pathway interactions. The proposed work is at the intersection of computer science and biology and will advance the ability to predict and assess the impact of enzyme promiscuity on biological systems. Training will be provided to graduate and undergraduate students and the research will be incorporated into classroom teaching. Underrepresented minorities in computing will be recruited to participate in the research through various established national and local programs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Although traditionally assumed specific, transforming a single substrate, many enzymes, if not all, have promiscuous activities such that they act on substrates other than those they evolved to transform. As current knowledge about enzyme promiscuity is limited, the objective of this proposal is to advance computational tools to analyze the promiscuity of enzymes within natural and engineered biological systems. The impact of these tools is to advance our understanding of cellular metabolism and guide experimental works, thus advancing applications related to energy production and biological discovery. Several computational models were developed. A deep-learning model, termed ELP - Enzymatic Link Prediction, is developed to predict an enzymatic link between two compounds. A deep-learning hierarchical classifier, termed EPP -- Enzyme Promiscuity Prediction, is designed to predict enzyme classes that can act on a query molecule. A deep-learning recommender systems, termed Boost-RS, is developed to find enzymes that act on query substrates, or substrates that interact with query enzymes. A multi-view contrastive learning model, termed Contrastive Data Stratification for Interaction Prediction (CSI), is used to predict substrate-enzyme prediction likelihood. A deep-learning method, SIGMA, is developed to align molecule graphs, a task needed to extract transformation rules between substrate and products. A graph neural network model, termed GNN-SOM, is developed to predict site of metabolism. A promiscuity prediction pipeline, called PROXIMAL2, was developed to enhance the prediction of products of promiscuous enzymes.
Several of the tools are applied to analyze the impact of enzyme promiscuity on cellular systems. A computational workflow, termed Metabolic Disruption Workflow (MDFlow), is developed for discovering interactions and network disruption arising from enzyme promiscuity. A method for creating Extended Metabolic (EMM) models based on enzyme promiscuity is developed. Using biotransformation rules, we developed a technique, BAM, to identify promiscuous novel molecules within molecular networks based on their spectra signature.
The project resulted in several tools, ELP, EPP, Boost-RS, CSI, SIGMA, GNN-SOM, PROXIMAL2, MDFlow, EMM, and BAM. All such tools are supported by peer-reviewed publications and are available in the public domain through the lab’s GitHub repository, https://github.com/HassounLab/. Further, several graduate and undergraduate students were trained in the development of computational methods that advance state of the art in biological engineering.
Last Modified: 03/12/2025
Modified by: Soha Hassoun
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