AIMC Topic: Protein Engineering

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Machine learning-guided multi-site combinatorial mutagenesis enhances the thermostability of pectin lyase.

International journal of biological macromolecules Aug 5, 2024
Enhancing the thermostability of enzymes is crucial for industrial applications. Methods such as directed evolution are often limited by the huge sequence space and combinatorial explosion, making it difficult to obtain optimal mutants. In recent yea...
Bacillus licheniformis Protein Engineering Polysaccharide-Lyases Molecular Dynamics Simulation Enzyme Stability Mutagenesis Mutagenesis, Site-Directed Temperature Mutation Machine Learning
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Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort.

Journal of chemical information and modeling Aug 1, 2024
Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited scr...
Machine Learning Proteins Probability Models, Molecular Protein Engineering
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Neural network extrapolation to distant regions of the protein fitness landscape.

Nature communications Jul 30, 2024
Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-functi...
Protein Binding Models, Molecular Bacterial Proteins Protein Engineering Machine Learning Immunoglobulin G Neural Networks, Computer
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Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering.

Nature communications Jul 29, 2024
The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering of useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (...
Directed Molecular Evolution Mutation Protein Engineering Gene Library Biocatalysis Machine Learning Enzymes Algorithms
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Context-aware geometric deep learning for protein sequence design.

Nature communications Jul 25, 2024
Protein design and engineering are evolving at an unprecedented pace leveraging the advances in deep learning. Current models nonetheless cannot natively consider non-protein entities within the design process. Here, we introduce a deep learning appr...
Amino Acid Sequence Protein Conformation Protein Engineering Proteins Models, Molecular Deep Learning
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EnzyACT: A Novel Deep Learning Method to Predict the Impacts of Single and Multiple Mutations on Enzyme Activity.

Journal of chemical information and modeling Jul 22, 2024
Enzyme engineering involves the customization of enzymes by introducing mutations to expand the application scope of natural enzymes. One limitation of that is the complex interaction between two key properties, activity and stability, where the enha...
Protein Engineering Enzymes Models, Molecular Mutation Deep Learning
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Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.

Cell research Jul 5, 2024
Mutations in amino acid sequences can provoke changes in protein function. Accurate and unsupervised prediction of mutation effects is critical in biotechnology and biomedicine, but remains a fundamental challenge. To resolve this challenge, here we ...
Mutation Gene Editing Humans Deep Learning Protein Engineering
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Machine learning-aided engineering of a cytochrome P450 for optimal bioconversion of lignin fragments.

Physical chemistry chemical physics : PCCP Jun 26, 2024
Using machine learning, molecular dynamics simulations, and density functional theory calculations we gain insight into the selectivity patterns of substrate activation by the cytochromes P450. In nature, the reactions catalyzed by the P450s lead to ...
Machine Learning Cytochrome P-450 Enzyme System Molecular Dynamics Simulation Density Functional Theory Protein Engineering Lignin
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Advancing microbial production through artificial intelligence-aided biology.

Biotechnology advances Jun 24, 2024
Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer mic...
Synthetic Biology Metabolic Engineering Artificial Intelligence Protein Engineering
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Machine learning in biological physics: From biomolecular prediction to design.

Proceedings of the National Academy of Sciences of the United States of America Jun 24, 2024
Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodo...
Molecular Dynamics Simulation Neural Networks, Computer Protein Engineering Machine Learning Proteins
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