AI-assisted food enzymes design and engineering: a critical review

Wang, Xinglong; Yang, Penghui; Zhao, Beichen; Liu, Song

doi:10.1007/s43393-022-00138-z

Cited by 9 publications

(9 citation statements)

References 110 publications

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“…Naturally existing proteins usually show limitations during practical uses as a result of unsatisfactory activity or stability [ 58 , 59 ], which has motivated researchers to find solutions. During food processing, the conditions of high salt, high concentrations of organic solvents or high temperatures can be harsh for food enzymes.…”

Section: Challenges For Food Enzyme Engineeringmentioning

confidence: 99%

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Wang

Tan

et al. 2023

IJMS

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Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme–substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.

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Section: Challenges For Food Enzyme Engineeringmentioning

confidence: 99%

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Wang

Tan

et al. 2023

IJMS

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“…In this review, we touched upon a few topics that focus on generative modeling of enzyme sequences. For a broader perspective on machine learning in protein studies, we recommend readers explore other reviews [ 12–26 ].…”

Section: Summary and Perspectivementioning

confidence: 99%

“…Machine learning has recently demonstrated its ability to bolster both the analysis and engineering of proteins, including enzymes (as discussed in refs [ 12–26 ]). Machine learning algorithms are broadly classified into two groups: discriminative models and generative models.…”

Section: Introductionmentioning

confidence: 99%

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering

Xie,

Warshel

2023

National Science Review

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Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity, and stability, rationalizing the laboratory evolution of de novo enzymes, decoding protein sequence semantics, and its applications in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzyme using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

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“…[ 8 , 9 ] The development of structural biology has led to the discovery of protein or peptide drugs entering a new era. [ 10 , 11 ] Designing peptide drugs is a top-down process, the first step is to build a peptide library, followed by peptide docking to the receptor to filter strong binding peptides. Finally, active peptides can be recognized by in vitro tests.…”

Section: Introductionmentioning

confidence: 99%

Discovering peptide inhibitors of thrombin as a strategy for anticoagulation

Zhen,

Wang,

et al. 2024

Medicine

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Unusual blood clots can cause serious health problems, such as lung embolism, stroke, and heart attack. Inhibiting thrombin activity was adopted as an effective strategy for preventing blood clots. In this study, we explored computational-based method for designing peptide inhibitors of human thrombin therapeutic peptides to prevent platelet aggregation. The random peptides and their 3-dimentional structures were generated to build a virtual peptide library. The generated peptides were docked into the binding pocket of human thrombin. The designed strong binding peptides were aligned with the native binder by comparative study, and we showed the top 5 peptide binders display strong binding affinity against human thrombin. The 5 peptides were synthesized and validated their inhibitory activity. Our result showed the 5-mer peptide AEGYA, EVVNQ, and FASRW with inhibitory activity against thrombin, range from 0.53 to 4.35 μM. In vitro anti-platelet aggregation assay was carried out, suggesting the 3 peptides can inhibit the platelet aggregation induced by thrombin. This study showed computer-aided peptide inhibitor design can be a robust method for finding potential binders for thrombin, which provided solutions for anticoagulation.

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AI-assisted food enzymes design and engineering: a critical review

Cited by 9 publications

References 110 publications

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering

Discovering peptide inhibitors of thrombin as a strategy for anticoagulation

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