Resource Center
Case Studies
Case Studies
Novel Serum Proteins to Catalyze Cultivated Meat Production
One of the major challenges of the cultivated meat industry is the cost of growth factors, unique protein molecules that stimulate the growth and maintain the health of cell tissue. Growth factors play an essential role in promoting cellular differentiation and cell division. However, deriving from mammalian cells, they generally cannot be produced through microbial fermentation, resulting in unfeasible production expenses that hinder cost reduction and customer adoption.
To address the financial and developmental challenges of cost-effective insulin manufacturing, Enzymit has designed multiple novel insulin-like proteins, a crucial growth factor in cell growth, but also an important therapeutic. These new molecules, some with sequence identity completely different from native insulin, can be expressed in bacterial cells, making large-scale production significantly more cost-effective.
Novel Protein-Based Biosensing Solution
Biosensors utilize proteins that can sense and detect the presence of particular substances, or measure specific properties of a sample, for a range of applications. These may include environmental monitoring, safety monitoring, and industrial process control.
Novel biosensors for ground contaminants may be driven by a need to monitor and control the presence of harmful substances to protect public health and safety, or to ensure compliance with regulatory standards. They offer several advantages over traditional sensors, such as higher sensitivity, greater specificity, and more rapid response times, providing an ideal solution for detecting and measuring contaminants in the ground.
Enzymit is designing multiple novel biosensors that can identify trace amounts of harmful substances with far higher specificity and minimal signal-to-noise ratio compared to traditional detection methods.
There i currently no commercially available enzyme for polyolefin degradation, and traditional chemical degradation methods are expensive and can only be used for a limited time.
Enzymit is developing novel enzymatic solutions that address the specific challenges of plastic degradation to help realize a more sustainable and environmentally responsible future.
Plastic Degradation
Almost 200 million tons of single-use polyolefins are produced every year. The proliferation of this environmentally resistant waste poses a growing environmental threat, while storage and disposal methods of plastic waste are still limited
There i currently no commercially available enzyme for polyolefin degradation, and traditional chemical degradation methods are expensive and can only be used for a limited time.
Enzymit is developing novel enzymatic solutions that address the specific challenges of plastic degradation to help realize a more sustainable and environmentally responsible future.
Drop-in Biofuels
Enzymit is developing a novel enzymatic process for converting waste vegetable oils and other fat wastes into drop-in biofuels.
The foundation of modern society is hydrocarbons, mostly obtained from fossil fuels. However, as these resources are becoming scarcer and their excessive Use negatively impacts the environment. Drop-in biofuels, which provide functionality to traditional fuels without the environmental drawbacks of earlier-generation biofuels (such as ethanol and biodiesel) are proving to be a more viable solution.
The current technology for drop-in biofuel production relies on chemical hydrogenation, which requires vast investments in infrastructure and limits geographical distribution. Enzymit’s technology allows us to build smaller factories that can be erected faster and more cost-effectively where they are needed, dramatically increasing the market potential.
Novel Serum Proteins to Catalyze Cultivated Meat Production
One of the major challenges of the cultivated meat industry is the cost of growth factors, unique protein molecules that stimulate the growth and maintain the health of cell tissue. Growth factors play an essential role in promoting cellular differentiation and cell division. However, deriving from mammalian cells, they generally cannot be produced through microbial fermentation, resulting in unfeasible production expenses that hinder cost reduction and customer adoption.
To address the financial and developmental challenges of cost-effective insulin manufacturing, Enzymit has designed multiple novel insulin-like proteins, a crucial growth factor in cell growth, but also an important therapeutic. These new molecules, some with sequence identity completely different from native insulin, can be expressed in bacterial cells, making large-scale production significantly more cost-effective.
Novel Protein-Based Biosensing Solution
Biosensors utilize proteins that can sense and detect the presence of particular substances, or measure specific properties of a sample, for a range of applications. These may include environmental monitoring, safety monitoring, and industrial process control.
Novel biosensors for ground contaminants may be driven by a need to monitor and control the presence of harmful substances to protect public health and safety, or to ensure compliance with regulatory standards. They offer several advantages over traditional sensors, such as higher sensitivity, greater specificity, and more rapid response times, providing an ideal solution for detecting and measuring contaminants in the ground.
Enzymit is designing multiple novel biosensors that can identify trace amounts of harmful substances with far higher specificity and minimal signal-to-noise ratio compared to traditional detection methods.
There i currently no commercially available enzyme for polyolefin degradation, and traditional chemical degradation methods are expensive and can only be used for a limited time.
Enzymit is developing novel enzymatic solutions that address the specific challenges of plastic degradation to help realize a more sustainable and environmentally responsible future.
Plastic Degradation
Almost 200 million tons of single-use polyolefins are produced every year. The proliferation of this environmentally resistant waste poses a growing environmental threat, while storage and disposal methods of plastic waste are still limited
There i currently no commercially available enzyme for polyolefin degradation, and traditional chemical degradation methods are expensive and can only be used for a limited time.
Enzymit is developing novel enzymatic solutions that address the specific challenges of plastic degradation to help realize a more sustainable and environmentally responsible future.
Drop-in Biofuels
Enzymit is developing a novel enzymatic process for converting waste vegetable oils and other fat wastes into drop-in biofuels.
The foundation of modern society is hydrocarbons, mostly obtained from fossil fuels. However, as these resources are becoming scarcer and their excessive Use negatively impacts the environment. Drop-in biofuels, which provide functionality to traditional fuels without the environmental drawbacks of earlier-generation biofuels (such as ethanol and biodiesel) are proving to be a more viable solution.
The current technology for drop-in biofuel production relies on chemical hydrogenation, which requires vast investments in infrastructure and limits geographical distribution. Enzymit’s technology allows us to build smaller factories that can be erected faster and more cost-effectively where they are needed, dramatically increasing the market potential.
Novel Serum Proteins to Catalyze Cultivated Meat Production
One of the major challenges of the cultivated meat industry is the cost of growth factors, unique protein molecules that stimulate the growth and maintain the health of cell tissue. Growth factors play an essential role in promoting cellular differentiation and cell division. However, deriving from mammalian cells, they generally cannot be produced through microbial fermentation, resulting in unfeasible production expenses that hinder cost reduction and customer adoption.
To address the financial and developmental challenges of cost-effective insulin manufacturing, Enzymit has designed multiple novel insulin-like proteins, a crucial growth factor in cell growth, but also an important therapeutic. These new molecules, some with sequence identity completely different from native insulin, can be expressed in bacterial cells, making large-scale production significantly more cost-effective.
Novel Protein-Based Biosensing Solution
Biosensors utilize proteins that can sense and detect the presence of particular substances, or measure specific properties of a sample, for a range of applications. These may include environmental monitoring, safety monitoring, and industrial process control.
Novel biosensors for ground contaminants may be driven by a need to monitor and control the presence of harmful substances to protect public health and safety, or to ensure compliance with regulatory standards. They offer several advantages over traditional sensors, such as higher sensitivity, greater specificity, and more rapid response times, providing an ideal solution for detecting and measuring contaminants in the ground.
Enzymit is designing multiple novel biosensors that can identify trace amounts of harmful substances with far higher specificity and minimal signal-to-noise ratio compared to traditional detection methods.
There i currently no commercially available enzyme for polyolefin degradation, and traditional chemical degradation methods are expensive and can only be used for a limited time.
Enzymit is developing novel enzymatic solutions that address the specific challenges of plastic degradation to help realize a more sustainable and environmentally responsible future.
Plastic Degradation
Almost 200 million tons of single-use polyolefins are produced every year. The proliferation of this environmentally resistant waste poses a growing environmental threat, while storage and disposal methods of plastic waste are still limited
There i currently no commercially available enzyme for polyolefin degradation, and traditional chemical degradation methods are expensive and can only be used for a limited time.
Enzymit is developing novel enzymatic solutions that address the specific challenges of plastic degradation to help realize a more sustainable and environmentally responsible future.
Drop-in Biofuels
Enzymit is developing a novel enzymatic process for converting waste vegetable oils and other fat wastes into drop-in biofuels.
The foundation of modern society is hydrocarbons, mostly obtained from fossil fuels. However, as these resources are becoming scarcer and their excessive Use negatively impacts the environment. Drop-in biofuels, which provide functionality to traditional fuels without the environmental drawbacks of earlier-generation biofuels (such as ethanol and biodiesel) are proving to be a more viable solution.
The current technology for drop-in biofuel production relies on chemical hydrogenation, which requires vast investments in infrastructure and limits geographical distribution. Enzymit’s technology allows us to build smaller factories that can be erected faster and more cost-effectively where they are needed, dramatically increasing the market potential.
Scientific Studies
Scientific Studies
Context-Dependent Design of Induced-Fit Enzymes Using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes.
Author
Zimmerman L, et al.
Published
BioRxiv
Year of Release
2023
Abstract
The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters.
To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes.
Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme.
Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.
Performance Upgrade of a Microbial Explosives’ Sensor Strain by Screening a High Throughput Saturation Library of a Transcriptional Regulator
Author
Zimmerman L, et al.
Published
BioRxiv
Year of Release
2023
Context-Dependent Design of Induced-Fit Enzymes Using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes.
Author
Zimmerman L, et al.
Published
BioRxiv
Year of Release
2023
Abstract
The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters.
To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes.
Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme.
Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.
Performance Upgrade of a Microbial Explosives’ Sensor Strain by Screening a High Throughput Saturation Library of a Transcriptional Regulator
Author
Zimmerman L, et al.
Published
BioRxiv
Year of Release
2023
Abstract
The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters.
To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes.
Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme.
Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.
Context-Dependent Design of Induced-Fit Enzymes Using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes.
Author
Zimmerman L, et al.
Published
BioRxiv
Year of Release
2023
Abstract
The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters.
To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes.
Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme.
Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.
Performance Upgrade of a Microbial Explosives’ Sensor Strain by Screening a High Throughput Saturation Library of a Transcriptional Regulator
Author
Zimmerman L, et al.
Published
BioRxiv
Year of Release
2023
Abstract
The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters.
To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes.
Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme.
Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.
Research Data
Enzymit creates Novel Enzymes capable of using inorganic phosphates instead of ATP
Enzymit is developing novel cell-free production pathways that do not require expensive co-factors, such as adenosine triphosphate (ATP), replacing them with affordable alternatives, like polyphosphates. Polyphosphate presents significant advantages over ATP for bioproduction of valuable chemicals using cell-free systems. Beyond its economic benefits, polyphosphate can be produced through environmentally friendly processes, which help to reduce the carbon footprint of the bioproduction process.
Being more stable than ATP, its phosphate bonds can be stored over longer periods of time, allowing for a more extended period of use. This stability also allows for the storage of polyphosphate under various environmental conditions, providing a more practical and convenient option for cell-free bioproduction processes. Additionally, polyphosphate can be easily regenerated and used as a continuous substrate, increasing the efficiency of the bioproduction process.
Cell- Free
Input material
Output material