AIMC Topic: Protein Engineering

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Data and AI-driven synthetic binding protein discovery.

Trends in pharmacological sciences Jan 3, 2025
Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. ...
Animals Drug Discovery Humans Artificial Intelligence Proteins Models, Molecular Protein Engineering Protein Binding Computational Biology
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LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning.

ACS synthetic biology Dec 24, 2024
Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barc...
Protein Engineering Nanopore Sequencing Software Directed Molecular Evolution Machine Learning
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A synthetic protein-level neural network in mammalian cells.

Science (New York, N.Y.) Dec 12, 2024
Artificial neural networks provide a powerful paradigm for nonbiological information processing. To understand whether similar principles could enable computation within living cells, we combined de novo-designed protein heterodimers and engineered v...
Proteolysis Recombinant Fusion Proteins Synthetic Biology HEK293 Cells Viral Proteases Protein Engineering Protein Multimerization Humans Neural Networks, Computer Protein Interaction Maps Cell Death
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Interpretable and explainable predictive machine learning models for data-driven protein engineering.

Biotechnology advances Dec 5, 2024
Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing and enhancing proteins with desired properties. The integration of artificial intelligence methods has further accelerated protein ...
Machine Learning Proteins Biotechnology Protein Engineering
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Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.

Chembiochem : a European journal of chemical biology Dec 5, 2024
Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolutio...
Mutation Molecular Dynamics Simulation Machine Learning Protein Engineering Iron Lysine Dioxygenases
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Deep learning for NAD/NADP cofactor prediction and engineering using transformer attention analysis in enzymes.

Metabolic engineering Nov 20, 2024
Understanding and manipulating the cofactor preferences of NAD(P)-dependent oxidoreductases, the most widely distributed enzyme group in nature, is increasingly crucial in bioengineering. However, large-scale identification of the cofactor preference...
Deep Learning Protein Engineering NADP NAD
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Exploring the potential of structure-based deep learning approaches for T cell receptor design.

PLoS computational biology Sep 30, 2024
Deep learning methods, trained on the increasing set of available protein 3D structures and sequences, have substantially impacted the protein modeling and design field. These advancements have facilitated the creation of novel proteins, or the optim...
Protein Binding Computational Biology Protein Conformation Protein Engineering Models, Molecular Deep Learning Humans Receptors, Antigen, T-Cell
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Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.

Molecules (Basel, Switzerland) Sep 29, 2024
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computati...
Protein Engineering Humans Artificial Intelligence Computational Biology Machine Learning Drug Design Proteins Models, Molecular
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On synergy between ultrahigh throughput screening and machine learning in biocatalyst engineering.

Faraday discussions Sep 11, 2024
Protein design and directed evolution have separately contributed enormously to protein engineering. Without being mutually exclusive, the former relies on computation from first principles, while the latter is a combinatorial approach based on chanc...
Biocatalysis Protein Engineering Machine Learning High-Throughput Screening Assays
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Navigating the landscape of enzyme design: from molecular simulations to machine learning.

Chemical Society reviews Aug 12, 2024
Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is c...
Protein Engineering Biocatalysis Animals Enzymes Protein Conformation Molecular Dynamics Simulation Humans Enzyme Activation Machine Learning Substrate Specificity
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