Opportunities and challenges in design and optimization of protein function

Nature Reviews Molecular Cell Biology, Journal Year: 2024, Volume and Issue: 25(8), P. 639 - 653

Published: April 2, 2024

Language: Английский

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Navigating the landscape of enzyme design: from molecular simulations to machine learning

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: 53(16), P. 8202 - 8239

Published: Jan. 1, 2024

Global environmental issues and sustainable development call for new technologies fine chemical synthesis waste valorization. Biocatalysis has attracted great attention as the alternative to traditional organic synthesis. However, it is challenging navigate vast sequence space identify those proteins with admirable biocatalytic functions. The recent of deep-learning based structure prediction methods such AlphaFold2 reinforced by different computational simulations or multiscale calculations largely expanded 3D databases enabled structure-based design. While approaches shed light on site-specific enzyme engineering, they are not suitable large-scale screening potential biocatalysts. Effective utilization big data using machine learning techniques opens up a era accelerated predictions. Here, we review applications machine-learning guided We also provide our view challenges perspectives effectively employing design integrating molecular learning, importance database construction algorithm in attaining predictive ML models explore fitness landscape

Language: Английский

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Multistate and functional protein design using RoseTTAFold sequence space diffusion

Nature Biotechnology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Protein denoising diffusion probabilistic models are used for the de novo generation of protein backbones but limited in their ability to guide proteins with sequence-specific attributes and functional properties. To overcome this limitation, we developed ProteinGenerator (PG), a sequence space model based on RoseTTAFold that simultaneously generates sequences structures. Beginning from noised representation, PG structure pairs by iterative denoising, guided desired structural attributes. We designed thermostable varying amino acid compositions internal repeats cage bioactive peptides, such as melittin. By averaging logits between trajectories distinct constraints, multistate parent-child triples which same folds different supersecondary structures when intact parent versus split into two child domains. design can be experimental sequence-activity data, providing general approach integrated computational optimization function.

Language: Английский

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Rapid in silico directed evolution by a protein language model with EVOLVEpro

Science, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 21, 2024

Directed protein evolution is central to biomedical applications but faces challenges like experimental complexity, inefficient multi-property optimization, and local maxima traps. While in silico methods using language models (PLMs) can provide modeled fitness landscape guidance, they struggle generalize across diverse families map activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs regression rapidly improve EVOLVEpro surpasses current methods, yielding up 100-fold improvements desired properties. demonstrate its effectiveness six proteins RNA production, genome editing, antibody binding applications. These results highlight the advantages of with minimal data over zero-shot predictions. opens new possibilities for AI-guided engineering biology medicine.

Language: Английский

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Artificial intelligence in drug development

Nature Medicine, Journal Year: 2025, Volume and Issue: 31(1), P. 45 - 59

Published: Jan. 1, 2025

Language: Английский

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Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2025, Volume and Issue: 24(1)

Published: Jan. 1, 2025

Abstract The food flavor science, traditionally reliant on experimental methods, is now entering a promising era with the help of artificial intelligence (AI). By integrating existing technologies AI, researchers can explore and develop new substances in digital environment, saving time resources. More more research will use AI big data to enhance product flavor, improve quality, meet consumer needs, drive industry toward smarter sustainable future. In this review, we elaborate mechanisms recognition their potential impact nutritional regulation. With increase accumulation development internet information technology, databases ingredient have made great progress. These provide detailed content, molecules, chemical properties various compounds, providing valuable support for rapid evaluation components construction screening technology. popularization fields, field has also ushered opportunities. This review explores role enhancing analysis through high‐throughput omics technologies. algorithms offer pathway scientifically formulations, thereby customized meals. Furthermore, it discusses safety challenges into industry.

Language: Английский

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Protein Engineering for Industrial Biocatalysis: Principles, Approaches, and Lessons from Engineered PETases

Catalysts, Journal Year: 2025, Volume and Issue: 15(2), P. 147 - 147

Published: Feb. 4, 2025

Protein engineering has emerged as a transformative field in industrial biotechnology, enabling the optimization of enzymes to meet stringent demands for stability, specificity, and efficiency. This review explores principles methodologies protein engineering, emphasizing rational design, directed evolution, semi-rational approaches, recent integration machine learning. These strategies have significantly enhanced enzyme performance, even rendering engineered PETase industrially relevant. Insights from PETases underscore potential tackle environmental challenges, such advancing sustainable plastic recycling, paving way innovative solutions biocatalysis. Future directions point interdisciplinary collaborations emerging learning technologies revolutionize design.

Language: Английский

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Machine learning for synthetic gene circuit engineering

Current Opinion in Biotechnology, Journal Year: 2025, Volume and Issue: 92, P. 103263 - 103263

Published: Jan. 27, 2025

Language: Английский

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The genetic architecture of protein stability

Nature, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 25, 2024

Language: Английский

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Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: March 24, 2024

Abstract Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization often hindered by rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary experimental data design diverse variant libraries, employ it improve the catalytic activity nuclease enzyme degrades biofilms accumulate on chronic wounds. After multiple rounds high-throughput experiments using both TeleProt standard directed evolution (DE) approaches parallel, find our approach found significantly better top-performing than DE, had hit rate at finding diverse, high-activity variants, was even able high-performance initial library no prior data. We have released dataset 55K one most extensive genotype-phenotype landscapes date, drive further progress ML-guided design.

Language: Английский

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