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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Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: July 29, 2024

Abstract The effective design of combinatorial libraries to balance fitness and diversity facilitates the engineering useful enzyme functions, particularly those that are poorly characterized or unknown in biology. We introduce MODIFY, a machine learning (ML) algorithm learns from natural protein sequences infer evolutionarily plausible mutations predict fitness. MODIFY co-optimizes predicted sequence starting libraries, prioritizing high-fitness variants while ensuring broad coverage. In silico evaluation shows outperforms state-of-the-art unsupervised methods zero-shot prediction enables ML-guided directed evolution with enhanced efficiency. Using we engineer generalist biocatalysts derived thermostable cytochrome c achieve enantioselective C-B C-Si bond formation via new-to-nature carbene transfer mechanism, leading six away previously developed enzymes exhibiting superior comparable activities. These results demonstrate MODIFY’s potential solving challenging problems beyond reach classic evolution.

Language: Английский

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Learning from Protein Engineering by Deconvolution of Multi‐Mutational Variants

Angewandte Chemie International Edition, Journal Year: 2024, Volume and Issue: 63(36)

Published: June 17, 2024

Abstract This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as surprise. Following the establishment of directed evolution stereoselective enzymes organic chemistry, concept partial or complete deconvolution selective multi‐mutational variants was introduced. Early experiments led to finding mutations can interact cooperatively antagonistically with one another, not just additively. During past decade, this phenomenon shown be general. In some studies, molecular dynamics (MD) quantum mechanics/molecular mechanics (QM/MM) computations were performed order shed light on origin non‐additivity at all stages an evolutionary upward climb. Data used construct unique multi‐dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional landscapes. Along related line, biochemists have long tested result introducing two point enzyme for reasons, followed by comparison respective double mutant so‐called cycles, showed only additive effects, but more recently also uncovered cooperative antagonistic non‐additive effects. We conclude suggestions future work, call unified overall picture epistasis.

Language: Английский

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Active learning-assisted directed evolution

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Jan. 16, 2025

Abstract Directed evolution (DE) is a powerful tool to optimize protein fitness for specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Evolution (ALDE), an iterative machine learning-assisted workflow that leverages uncertainty quantification explore the search space of proteins more efficiently than current methods. We apply ALDE engineering landscape challenging DE: optimization five epistatic residues in active site enzyme. In three rounds wet-lab experimentation, improve yield desired product non-native cyclopropanation reaction from 12% 93%. also perform computational simulations on existing sequence-fitness datasets support our argument effective DE. Overall, practical and broadly applicable strategy unlock improved outcomes.

Language: Английский

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A Practical Guide to Computational Tools for Engineering Biocatalytic Properties

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 980 - 980

Published: Jan. 24, 2025

The growing demand for efficient, selective, and stable enzymes has fueled advancements in computational enzyme engineering, a field that complements experimental methods to accelerate discovery. With plethora of software tools available, researchers from different disciplines often face challenges selecting the most suitable method meets their requirements available starting data. This review categorizes engineering based on capacity enhance following specific biocatalytic properties biotechnological interest: (i) protein–ligand affinity/selectivity, (ii) catalytic efficiency, (iii) thermostability, (iv) solubility recombinant production. By aligning with respective scoring functions, we aim guide researchers, particularly those new methods, appropriate design protein campaigns. De novo design, involving creation novel proteins, is beyond this review’s scope. Instead, focus practical strategies fine-tuning enzymatic performance within an established reference framework natural proteins.

Language: Английский

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Practical Machine Learning-Assisted Design Protocol for Protein Engineering: Transaminase Engineering for the Conversion of Bulky Substrates

ACS Catalysis, Journal Year: 2024, Volume and Issue: 14(9), P. 6462 - 6469

Published: April 12, 2024

Protein engineering is essential for improving the catalytic performance of enzymes applications in biocatalysis, which machine learning provides an emerging approach variant design. Transaminases are powerful biocatalysts stereoselective synthesis chiral amines but one major challenge their limited substrate scope. We present a general and practical design protocol protein to combine advantages three strategies, including directed evolution, rational design, learning, demonstrate application transaminases with higher activity toward bulky substrates. A high-quality data set was obtained by selected key positions, then applied create model transaminase activity. This data-assisted optimized variants, showed improved (up 3-fold over parent) substrates, maintaining enantioselectivity starting enzyme scaffold as well enantiomeric excess >99%ee).

Language: Английский

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Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: 273, P. 132853 - 132853

Published: June 3, 2024

Language: Английский

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Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Natural Product Reports, Journal Year: 2024, Volume and Issue: 41(10), P. 1543 - 1578

Published: Jan. 1, 2024

This review highlights methods for studying structure activity relationships of natural products and proposes that these are complementary could be used to build an iterative computational-experimental workflow.

Language: Английский

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Enzymes Produced by the Genus Aspergillus Integrated into the Biofuels Industry Using Sustainable Raw Materials

Fermentation, Journal Year: 2025, Volume and Issue: 11(2), P. 62 - 62

Published: Feb. 1, 2025

Renewable energy sources, such as biofuels, represent promising alternatives to reduce dependence on fossil fuels and mitigate climate change. Their production through enzymatic hydrolysis has gained relevance by converting agro-industrial waste into fermentable sugars residual oils, which are essential for the generation of bioethanol biodiesel. The fungus Aspergillus stands out a key source enzymes, including cellulases, xylanases, amylases, lipases, crucial breakdown biomass oils produce fatty acid methyl esters (FAME). This review examines current state these technologies, highlighting significance in conversion energy-rich materials. While process holds significant potential, it faces challenges high costs associated with final processing stages. Agro-industrial is proposed an resource support circular economy, thereby eliminating reliance non-renewable resources processes. Furthermore, advanced pretreatment technologies—including biological, physical, physicochemical methods, well use ionic liquids—are explored enhance efficiency. Innovative genetic engineering strains enzyme encapsulation, promise optimize sustainable biofuel addressing advancing this technology towards large-scale implementation.

Language: Английский

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Integrating protein language models and automatic biofoundry for enhanced protein evolution

Nature Communications, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 11, 2025

Language: Английский

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