# Enzyme design Build protein scaffolds around catalytic geometry, redesign ligand-aware sequences, and connect computation to biochemical testing. [Open workflow](https://proteiniq.io/app/workflows/templates?template=enzyme-active-site-scaffold-design)[Contact sales](https://proteiniq.io/contact) ## More protein design workflows ## Tools for enzyme design Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow. [![RFdiffusion 2](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Frfdiffusion2.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RFdiffusion 2 Generate active-site scaffolds with atomic context](https://proteiniq.io/app/rfdiffusion-2)[![RFdiffusion3](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Frfdiffusion3.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### RFdiffusion3 Generate enzyme-oriented all-atom backbones](https://proteiniq.io/app/rfdiffusion3)[![LigandMPNN](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fligand-mpnn-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### LigandMPNN Assign ligand-aware protein sequences](https://proteiniq.io/app/ligand-mpnn)[![ProteinMPNN](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2FproteinMPNN-featured.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ProteinMPNN Assign alternative scaffold sequences](https://proteiniq.io/app/protein-mpnn)[![EvoPro](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fevopro.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### EvoPro Optimize sequence and structure objectives](https://proteiniq.io/app/evopro)[![ESMFold](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fesmfold.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### ESMFold Refold designed sequences for structural comparison](https://proteiniq.io/app/esmfold)[![fpocket](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Ffpocket.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### fpocket Inspect designed pocket geometry](https://proteiniq.io/app/fpocket)[![PROPKA 3](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fpropka-3.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### PROPKA 3 Estimate structure-dependent ionization context](https://proteiniq.io/app/propka)[![MolProbity](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fmolprobity.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### MolProbity Review clashes and stereochemical geometry](https://proteiniq.io/app/molprobity)[![Protein stability prediction](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fprotein-stability.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### Protein stability prediction Estimate sequence-level stability signals](https://proteiniq.io/app/protein-stability)[![NetSolP-1.0](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fnetsolp.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### NetSolP-1.0 Estimate sequence-level solubility](https://proteiniq.io/app/netsolp-1-0)[![SASA calculator](https://proteiniq.io/_next/image?url=%2Fimages%2Ftools%2Fsasa-calculator.webp&w=96&q=75&dpl=dpl_J8tu6aF3F41kEvMFgYtuMXes1ynJ) ### SASA calculator Measure solvent-accessible surface area](https://proteiniq.io/app/sasa-calculator) ## What is enzyme design? Enzyme design is the process of creating or modifying proteins to catalyze a specified chemical transformation. Computational workflows represent catalytic residues, transition-state or substrate geometry, cofactors, and surrounding structural constraints, then generate scaffolds and sequences intended to support that arrangement. Success requires more than a folded model: catalytic rate, selectivity, stability, and the proposed reaction mechanism must be measured experimentally. De novo enzyme design starts from reaction geometry rather than from an existing enzyme sequence. Other projects redesign a natural scaffold while preserving catalytic residues. In either case, ligand and cofactor coordinates must remain chemically meaningful throughout motif scaffolding and ligand-aware sequence design. The computational funnel should reject candidates that distort catalytic geometry, bury unsatisfied polar atoms, clash with substrates, or fail to refold. Laboratory assays then distinguish true catalysis from background reaction, nonspecific binding, contamination, or an unintended mechanism. ### When to use enzyme design Enzyme design needs an explicit reaction and active-site geometry: - A catalytic motif must be placed in a new protein scaffold. - A ligand or cofactor should influence sequence choice around a pocket. - You can assay reaction rate and relevant background controls. ### Strengths and limitations Atomic motif constraints let scaffold generation preserve proposed catalytic geometry instead of optimizing overall fold alone. Ligand-aware sequence design can use the positioned substrate, metal or cofactor context, and refolding checks whether candidate sequences still support the intended scaffold. Correct motif placement is only one requirement for catalysis. Protonation, solvent access, dynamics and transition-state stabilization may remain wrong even in a well folded design. A model score cannot establish rate enhancement or substrate selectivity; those claims need quantitative assays and controls. ## Enzyme design methods Motif-scaffolding methods place catalytic atoms within generated protein geometry. The motif is only as meaningful as its chemical model, so protonation, cofactors, substrate pose, and geometric tolerances should be documented. Ligand-aware sequence design assigns residues around both the backbone and non-protein atoms. It can preserve a pocket context but cannot prove that the designed electrostatics, dynamics, or reaction pathway will produce catalysis. ## Applications of enzyme design Active-site scaffolding can place catalytic residues around a substrate or cofactor when no natural enzyme presents the desired geometry. That structural proposal can guide a focused set of variants for biochemical testing. Ligand-aware sequence redesign may tune the surrounding pocket while keeping key motif atoms fixed. Compare designed structures against the intended reaction geometry, but judge catalysis by measured product formation and kinetic controls rather than by pocket appearance. ## How to run enzyme design online The featured workflow uses an atomic motif PDB as the design constraint. Check non-protein atoms before generation because the next model uses their positions directly. 1. **Define the chemistry.** Specify the proposed reaction, catalytic atoms, substrate or cofactor and the assay that will distinguish catalysis from background. 2. **Prepare the motif.** Supply a PDB whose catalytic residues and positioned non-protein atoms have correct names, bonds and coordinates. 3. **Generate scaffolds.** Run [RFdiffusion 2](https://proteiniq.io/app/rfdiffusion-2) around the atomic motif and reject candidates that distort the required geometry. 4. **Assign sequences.** Pass accepted scaffold PDBs with positioned ligand or cofactor context to [LigandMPNN](https://proteiniq.io/app/ligand-mpnn); it designs sequence, not ligand placement. 5. **Refold and inspect.** Use [ESMFold](https://proteiniq.io/app/esmfold) on designed sequences and compare the predicted pocket with the motif and generated scaffold. 6. **Test catalysis.** Keep unsuccessful designs and measure rate, background and selectivity under controlled reaction conditions. ## How to interpret enzyme design results Inspect motif RMSD, catalytic distances and angles, pocket accessibility, clashes, sequence confidence, refold agreement, and alternate ligand poses. Keep native enzyme and no-enzyme controls when available. Experimental evaluation should report expression, folding, turnover, catalytic efficiency, substrate scope, and selectivity. Controls must rule out spontaneous reaction and adventitious catalysis. ## Experimental validation and handoff Preserve motif atoms, ligand/cofactor coordinates, geometric constraints, sequence-design settings, and rejected candidates. Validate activity with quantitative assays and controls. Preserve the input motif, scaffold coordinates and ligand context for each candidate. Report reaction rates with negative controls so background chemistry is not attributed to a designed active site. ## How enzyme design works Generate scaffolds around an active-site motif with RFdiffusion 2, assign ligand-aware sequences with LigandMPNN, and refold with ESMFold. 1. **Define reaction.** Specify the chemical transformation, catalytic residues, ligand or transition-state model, and assay controls. 2. **Encode active site.** Prepare a motif PDB whose atom names, bonds, cofactors, and HETATM context are correct. 3. **Generate scaffolds.** Generate scaffolds that preserve the required active-site geometry and reject distorted motifs. 4. **Design sequences.** Assign ligand-aware sequences, refold candidates, and inspect pockets, clashes, and stability. 5. **Test catalysis.** Express selected designs and measure rate, background, selectivity, and mechanism-relevant controls. ## Inputs and outputs Check formats before running, then inspect and download the result from every workflow step. ### Inputs #### Design input `PDB``FASTA``JSON``TXT` An active-site motif PDB with catalytic residues and ligand or cofactor HETATM records. ### Outputs #### Design and review outputs `PDB``FASTA``CSV``JSON` Scaffold PDB files, ligand-aware FASTA sequences, refolded models, design scores, and validation files. ## Frequently asked questions AI answers from our docs. 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