EC2Vec: A Machine Learning Method to Embed Enzyme Commission (EC) Numbers into Vector Representations DOI Creative Commons
Mengmeng Liu,

Xialong Ni,

J. Ramanujam

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Enzyme commission (EC) numbers play a vital role in classifying enzymes and understanding their functions enzyme-related research. Although accurate informative encoding of EC is essential for enhancing the effectiveness machine learning applications, simple approaches suffer from limitations such as false numerical order high sparsity. To address these issues, we developed EC2Vec, multimodal autoencoder that preserves categorical nature leverages hierarchical relationships, resulting more meaningful representations. EC2Vec encodes each digit number token then processes embeddings through 1D convolutional layer to capture relationships. Comprehensive benchmarking against large collection indicates outperforms methods. The t-SNE visualization revealed distinct clusters corresponding different enzyme classes, demonstrating structure effectively captured. In downstream outperformed other methods reaction-EC pair classification task, underscoring its robustness utility research bioinformatics applications.

Language: Английский

Navigating the landscape of enzyme design: from molecular simulations to machine learning DOI Creative Commons
Jiahui Zhou, Meilan Huang

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

Citations

19
Learning from Protein Engineering by Deconvolution of Multi‐Mutational Variants DOI Creative Commons
Frank Hollmann,

Joaquin Sanchis,

Manfred T. Reetz

et al.

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

Citations

17
Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering DOI Creative Commons
Kerr Ding, M. A. Chin, Yunlong Zhao

et al.

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

Citations

16
Active learning-assisted directed evolution DOI Creative Commons
Jason Yang, Ravi Lal,

James C. Bowden

et al.

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

Citations

4
Practical Machine Learning-Assisted Design Protocol for Protein Engineering: Transaminase Engineering for the Conversion of Bulky Substrates DOI
Marian J. Menke, Yu‐Fei Ao, Uwe T. Bornscheuer

et al.

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

Citations

11
Lipases for targeted industrial applications, focusing on the development of biotechnologically significant aspects: A comprehensive review of recent trends in protein engineering DOI
Nurcan Vardar-Yel, Havva Esra Tütüncü, Yusuf Sürmeli

et al.

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

Published: June 3, 2024

Language: Английский

Citations

10
Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products DOI Creative Commons
Christine Mae F. Ancajas, Abiodun S. Oyedele, Caitlin M. Butt

et al.

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

Citations

9
Enzymes Produced by the Genus Aspergillus Integrated into the Biofuels Industry Using Sustainable Raw Materials DOI Creative Commons

Fernando Enrique Rosas-Vega,

Roberta Pozzan,

Walter José Martínez-Burgos

et al.

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

Citations

1
Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering DOI Open Access
Maximilian Gantz, Simon V. Mathis, Friederike E. H. Nintzel

et al.

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

Published: April 8, 2024

Abstract Engineering enzyme biocatalysts for higher efficiency is key to enabling sustainable, ‘green’ production processes the chemical and pharmaceutical industry. This challenge can be tackled from two angles: by directed evolution, based on labor-intensive experimental testing of variant libraries, or computational methods, where sequence-function data are used predict biocatalyst improvements. Here, we combine both approaches into a two-week workflow, ultra-high throughput screening library imine reductases (IREDs) in microfluidic devices provides not only selected ‘hits’, but also long-read sequence linked fitness scores >17 thousand variants. We demonstrate engineering an IRED chiral amine synthesis mapping functional information one go, ready interpretation extrapolation protein engineers with help machine learning (ML). calculate position-dependent mutability combinability mutations comprehensively illuminate complex interplay driven synergistic, often positively epistatic effects. Interpreted easy-to-use regression tree-based ML algorithms designed suit evaluation random whole-gene mutagenesis data, 3-fold improved ‘hits’ obtained extrapolated further give up 23-fold improvements catalytic rate after handful mutants. Our campaign paradigmatic future that will rely access large maps as profiles way responds mutation. These chart function exploiting synergy rapid combined extrapolation.

Language: Английский

Citations

6
Enhancing enzymatic activity with nanoparticle display – an updated compendium and engineering outlook DOI Creative Commons
Shelby L. Hooe, Joyce C. Breger, Igor L. Medintz

et al.

Molecular Systems Design & Engineering, Journal Year: 2024, Volume and Issue: 9(7), P. 679 - 704

Published: Jan. 1, 2024

Schematic depicting enzyme kinetic enhancement when displayed on a nanoparticle surface. We provide state of the art review this phenomenon describing what is known about how it arises along with examples grouped by nanomaterials.

Language: Английский

Citations

6