AIMC Topic: Protein Engineering

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Toward deep learning sequence-structure co-generation for protein design.

Current opinion in structural biology Feb 20, 2025
Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions. While the majority of today's models focus on generating either sequences or structures...
Protein Conformation Proteins Protein Engineering Deep Learning Models, Molecular
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Deep learning-driven semi-rational design in phenylalanine ammonia-lyase for enhanced catalytic efficiency.

International journal of biological macromolecules Feb 19, 2025
Phenylalanine ammonia-lyase (PAL) possesses significant potential in agriculture, industry, and the treatment of various diseases, including cancer. In particular, PAL derived from Anabaena variabilis (AvPAL) has been successfully utilized in clinica...
Mutation Anabaena variabilis Molecular Dynamics Simulation Protein Engineering Kinetics Deep Learning Biocatalysis Phenylalanine Ammonia-Lyase Catalysis
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AI-enabled alkaline-resistant evolution of protein to apply in mass production.

eLife Feb 19, 2025
Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in indu...
Alkalies Protein Stability Protein Engineering Artificial Intelligence Hydrogen-Ion Concentration
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Deep Learning-Assisted Discovery of Protein Entangling Motifs.

Biomacromolecules Feb 12, 2025
Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design an...
Amino Acid Sequence Protein Engineering Deep Learning Proteins Amino Acid Motifs
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Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants.

Science advances Feb 12, 2025
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and standardiz...
Supervised Machine Learning Protein Engineering Models, Molecular Mutation Software Algorithms Computational Biology Proteins Machine Learning
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Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design.

Journal of chemical information and modeling Feb 10, 2025
Computational enzyme design is a promising technique for producing novel enzymes for industrial and clinical needs. A key challenge that this technique faces is to consistently achieve the desired activity. Fundamental studies of natural enzymes reve...
Protein Engineering Biocatalysis Catalytic Domain Serine Proteases Static Electricity Models, Molecular Quantum Theory Machine Learning
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Unveiling the new chapter in nanobody engineering: advances in traditional construction and AI-driven optimization.

Journal of nanobiotechnology Feb 6, 2025
Nanobodies (Nbs), miniature antibodies consisting solely of the variable region of heavy chains, exhibit unique properties such as small size, high stability, and strong specificity, making them highly promising for disease diagnosis and treatment. T...
Artificial Intelligence Animals Humans Protein Engineering Cell Surface Display Techniques Peptide Library Single-Domain Antibodies
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Accelerated enzyme engineering by machine-learning guided cell-free expression.

Nature communications Jan 20, 2025
Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell...
Protein Engineering Cell-Free System Machine Learning Substrate Specificity
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Tailoring industrial enzymes for thermostability and activity evolution by the machine learning-based iCASE strategy.

Nature communications Jan 11, 2025
The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (i...
Enzymes Directed Molecular Evolution Protein Engineering Machine Learning Evolution, Molecular Enzyme Stability
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Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the S2 Step.

Journal of the American Chemical Society Jan 10, 2025
Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential u...
Artificial Intelligence Hydrolases Protein Engineering Kinetics Molecular Dynamics Simulation
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