Accelerate Enzyme Engineering with EnzyGen2, Now Available on Vecura
This update empowers protein engineers to design novel, catalytically active enzymes with preserved functional motifs through an intuitive workflow on Vecura, bypassing the need for complex local infrastructure.
What is EnzyGen2?
EnzyGen2 is a 730M-parameter generative protein foundation model designed to tackle the complexities of enzyme engineering. By simultaneously co-designing amino-acid sequences and Cα backbone structures, it ensures that critical functional motifs are preserved while adapting the protein scaffold for specific host organisms. It offers a streamlined approach for enzyme family adaptation, eliminating the need for multi-step pipelines that often fail to maintain active-site geometry.
What can users do with EnzyGen2 on Vecura?
With EnzyGen2 on Vecura, users can:
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Perform Co-design: Generate novel enzyme sequences and their corresponding Cα backbones in a single autoregressive pass.
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Preserve Functionality: Specify critical active-site or binding-site residues to be strictly maintained during the design process.
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Adapt to Hosts: Incorporate NCBI taxonomy identifiers to tailor protein scaffolds to specific host organisms.
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Leverage Specialized Checkpoints: Utilize pretrained models for broad coverage or family-specific checkpoints (ChlR, AadA, TPMT) for improved performance on known enzyme classes.
What the output means
The output provides the designed amino-acid sequence, a predicted Cα backbone structure in PDB format, and per-residue log-likelihood scores.
This output should be used to support scientific decision making. It does not replace experimental validation.
Why this matters
Enzyme engineering is foundational to biotechnology, yet traditionally difficult due to the requirement of maintaining precise active-site geometry while modifying the surrounding scaffold. By providing a generative model that treats sequence and structure as a unified design problem, EnzyGen2 significantly accelerates the creation of enzymes optimized for specific environmental or industrial contexts.
This model enables researchers to move beyond traditional trial-and-error mutagenesis, providing a computationally guided path toward novel biocatalysts with defined functional requirements.
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Developed by: LeiLiLab
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Source: Official GitHub Repository
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Reference: Original Paper (bioRxiv 2026)
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