SelenzymeRF: updated enzyme suggestion software for unbalanced biochemical reactions

Stoney, Ruth A.; Hanko, Erik K.R.; Carbonell, Pablo; Breitling, Rainer

doi:10.1016/j.csbj.2023.11.039

Cited by 2 publications

(1 citation statement)

References 44 publications

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“…Enzyme searches typically fall into two main categories: (i) referencing reactions and their catalyzing enzymes in metabolic databases such as KEGG and MetaCyc, and (ii) addressing de novo reactions not found in these databases, which necessitates predictive algorithms. E-zyme, Selenzyme, , and BridgIT are three methods instrumental for retrieving enzyme sequences for de novo reactions. In brief, their strategies involve a two-step process: first, use the de novo “query” reaction to identify similar known reactions in a reference database; second, retrieve the sequences associated with the best-hit known reactions.…”

Section: Scoring Functionmentioning

confidence: 99%

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

Gricourt,

Meyer,

Duigou

et al. 2024

ACS Synth. Biol.

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Retrosynthesis aims to efficiently plan the synthesis of desirable chemicals by strategically breaking down molecules into readily available building block compounds. Having a long history in chemistry, retro-biosynthesis has also been used in the fields of biocatalysis and synthetic biology. Artificial intelligence (AI) is driving us toward new frontiers in synthesis planning and the exploration of chemical spaces, arriving at an opportune moment for promoting bioproduction that would better align with green chemistry, enhancing environmental practices. In this review, we summarize the recent advancements in the application of AI methods and models for retrosynthetic and retrobiosynthetic pathway design. These techniques can be based either on reaction templates or generative models and require scoring functions and planning strategies to navigate through the retrosynthetic graph of possibilities. We finally discuss limitations and promising research directions in this field.

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Section: Scoring Functionmentioning

confidence: 99%

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

Gricourt,

Meyer,

Duigou

et al. 2024

ACS Synth. Biol.

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Synthetic Biology of Natural Products Engineering: Recent Advances Across the Discover–Design–Build–Test–Learn Cycle

Foldi,

Connolly,

Takano

et al. 2024

ACS Synth. Biol.

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Advances in genome engineering and associated technologies have reinvigorated natural products research. Here we highlight the latest developments in the field across the discover− design−build−test−learn cycle of bioengineering, from recent progress in computational tools for AI-supported genome mining, enzyme and pathway engineering, and compound identification to novel host systems and new techniques for improving production levels, and place these trends in the context of responsible research and innovation, emphasizing the importance of anticipatory analysis at the early stages of process development.

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SelenzymeRF: updated enzyme suggestion software for unbalanced biochemical reactions

Cited by 2 publications

References 44 publications

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review

Synthetic Biology of Natural Products Engineering: Recent Advances Across the Discover–Design–Build–Test–Learn Cycle

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