Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Current insights into protein solubility: A review of its importance for alternative proteins
L Grossmann, DJ McClements - Food Hydrocolloids, 2023 - Elsevier
The solubility of proteins plays a key role in determining the physicochemical properties,
processing, sensory attributes, shelf life, and nutritional profile of foods formulated with …
processing, sensory attributes, shelf life, and nutritional profile of foods formulated with …
Proteingym: Large-scale benchmarks for protein fitness prediction and design
Predicting the effects of mutations in proteins is critical to many applications, from
understanding genetic disease to designing novel proteins to address our most pressing …
understanding genetic disease to designing novel proteins to address our most pressing …
Machine learning-enabled retrobiosynthesis of molecules
Retrobiosynthesis provides an effective and sustainable approach to producing functional
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …
Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE
Background Protein solubility is a precondition for efficient heterologous protein expression
at the basis of most industrial applications and for functional interpretation in basic research …
at the basis of most industrial applications and for functional interpretation in basic research …
Accelerating biocatalysis discovery with machine learning: a paradigm shift in enzyme engineering, discovery, and design
B Markus, K Andreas, K Arkadij, L Stefan, O Gustav… - ACS …, 2023 - ACS Publications
Emerging computational tools promise to revolutionize protein engineering for biocatalytic
applications and accelerate the development timelines previously needed to optimize an …
applications and accelerate the development timelines previously needed to optimize an …
Peptidebert: A language model based on transformers for peptide property prediction
Recent advances in language models have enabled the protein modeling community with a
powerful tool that uses transformers to represent protein sequences as text. This …
powerful tool that uses transformers to represent protein sequences as text. This …
DSResSol: A sequence-based solubility predictor created with Dilated Squeeze Excitation Residual Networks
M Madani, K Lin, A Tarakanova - International Journal of Molecular …, 2021 - mdpi.com
Protein solubility is an important thermodynamic parameter that is critical for the
characterization of a protein's function, and a key determinant for the production yield of a …
characterization of a protein's function, and a key determinant for the production yield of a …
Exploring new galaxies: Perspectives on the discovery of novel PET-degrading enzymes
J Mican, J Da'san MM, W Liu, G Weber… - Applied Catalysis B …, 2024 - Elsevier
Polyethylene terephthalate (PET) is a widely used polyester due to its beneficial material
properties and low cost. However, PET contributes significantly to the growing problem of …
properties and low cost. However, PET contributes significantly to the growing problem of …
Strategies to overcome the challenges of low or no expression of heterologous proteins in Escherichia coli
R Jiang, S Yuan, Y Zhou, Y Wei, F Li, M Wang… - Biotechnology …, 2024 - Elsevier
Protein expression is a critical process in diverse biological systems. For Escherichia coli, a
widely employed microbial host in industrial catalysis and healthcare, researchers often face …
widely employed microbial host in industrial catalysis and healthcare, researchers often face …