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Protein Engineering

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Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network.

Metabolic engineering
Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent...

Improving de novo protein binder design with deep learning.

Nature communications
Recently it has become possible to de novo design high affinity protein binding proteins from target structural information alone. There is, however, considerable room for improvement as the overall design success rate is low. Here, we explore the au...

Top-down design of protein architectures with reinforcement learning.

Science (New York, N.Y.)
As a result of evolutionary selection, the subunits of naturally occurring protein assemblies often fit together with substantial shape complementarity to generate architectures optimal for function in a manner not achievable by current design approa...

Generating new protein sequences by using dense network and attention mechanism.

Mathematical biosciences and engineering : MBE
Protein engineering uses de novo protein design technology to change the protein gene sequence, and then improve the physical and chemical properties of proteins. These newly generated proteins will meet the needs of research better in properties and...

Development and characterization of an α-l-rhamnosidase mutant with improved thermostability and a higher efficiency for debittering orange juice.

Food chemistry
The glycoside hydrolase, α-l-rhamnosidase, could remove the bitter taste of naringin from citrus juices. However, most α-l-rhamnosidases are easily deactivated at high temperatures, limiting the practice in debittering citrus juices. The V529A mutant...

Tackling a textbook example of multistep enzyme catalysis with deep learning-driven design.

Molecular cell
Enzyme design has struggled to emulate the complexity and catalytic proficiency of natural enzymes. Lauko et al. show that with the help of deep learning, the design of serine hydrolases that rival nature's ingenuity is possible.

[Intelligent mining, engineering, and design of proteins].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Natural components serve the survival instincts of cells that are obtained through long-term evolution, while they often fail to meet the demands of engineered cells for efficiently performing biological functions in special industrial environments. ...

[ protein design in the age of artificial intelligence].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent ...

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.

Biotechnology journal
Through iterative rounds of mutation and selection, proteins can be engineered to enhance their desired biological functions. Nevertheless, identifying optimal mutation sites for directed evolution remains challenging due to the vastness of the prote...

AI-based IsAb2.0 for antibody design.

Briefings in bioinformatics
Therapeutic antibody design has garnered widespread attention, highlighting its interdisciplinary importance. Advancements in technology emphasize the critical role of designing nanobodies and humanized antibodies in antibody engineering. However, cu...