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Protein Engineering

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Codon optimization with deep learning to enhance protein expression.

Scientific reports
Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon...

Machine learning-assisted enzyme engineering.

Methods in enzymology
Directed evolution and rational design are powerful strategies in protein engineering to tailor enzyme properties to meet the demands in academia and industry. Traditional approaches for enzyme engineering and directed evolution are often experimenta...

Deep Dive into Machine Learning Models for Protein Engineering.

Journal of chemical information and modeling
Protein redesign and engineering has become an important task in pharmaceutical research and development. Recent advances in technology have enabled efficient protein redesign by mimicking natural evolutionary mutation, selection, and amplification s...

ProDCoNN: Protein design using a convolutional neural network.

Proteins
Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this...

Sounds interesting: can sonification help us design new proteins?

Expert review of proteomics
: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music ha...

Function2Form Bridge-Toward synthetic protein holistic performance prediction.

Proteins
Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on indiv...

Unified rational protein engineering with sequence-based deep representation learning.

Nature methods
Rational protein engineering requires a holistic understanding of protein function. Here, we apply deep learning to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically...

Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

Nature methods
We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene deli...

Machine-learning-guided directed evolution for protein engineering.

Nature methods
Protein engineering through machine-learning-guided directed evolution enables the optimization of protein functions. Machine-learning approaches predict how sequence maps to function in a data-driven manner without requiring a detailed model of the ...