> ## Documentation Index
> Fetch the complete documentation index at: https://docs.purna.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Enzyme Design

> Design novel enzymes and ligand-binding proteins from a SMILES string — sequence, 3D structure, and automatic refolding validation.

MIP can design a novel protein from scratch for a target small molecule. Give it a SMILES string, and it generates an amino acid sequence together with a co-folded 3D structure, then independently refolds the design as a sanity check — all inside your chat.

Enzyme design is powered by **DISCO** (DIffusion for Sequence-structure CO-design), a diffusion model that jointly generates sequence and structure conditioned on a ligand. Designs are refolded with **NVIDIA Boltz-2** and rendered with **Mol\***, the same viewer used by RCSB PDB.

## Starting a design

Ask MIP to design a protein for a substrate or ligand:

* "Design an enzyme that binds caffeine"
* "Design a protein that catalyses cyclopropanation of styrene — use ethyl diazoacetate as the carbene source"
* "Design 3 scaffolds for this SMILES: CN1C=NC2=C1C(=O)N(C)C(=O)N2C"

MIP resolves the SMILES (pulling it from PubChem or ChEMBL if you give a common name), confirms parameters with you, estimates the GPU cost, and submits. A result card appears in chat while the design runs.

### Input options

| Parameter         | What it does                                                                 | Default                          |
| ----------------- | ---------------------------------------------------------------------------- | -------------------------------- |
| `ligandSmiles`    | SMILES of the target substrate or ligand. Required.                          | —                                |
| `proteinSequence` | Partial sequence with `-` at positions to design. Enables motif scaffolding. | Fully masked 150-residue protein |
| `dnaTarget`       | ACGT sequence for DNA-binding designs                                        | None                             |
| `rnaTarget`       | ACGU sequence for RNA-binding designs                                        | None                             |
| `effort`          | `fast` (100 diffusion steps, 2 recycles) or `max` (200 steps, 4 recycles)    | `max`                            |
| `numDesigns`      | Independent designs to generate (1–5)                                        | 3                                |

### Motif scaffolding

If you already know the catalytic residues you want to preserve, pass them inside `proteinSequence`. Fixed positions hold their amino acid, `-` positions are designed by DISCO.

* `"----------S----------H----------D----------"` pins a Ser/His/Asp triad and lets DISCO scaffold the rest.
* `"MKGH----------------------------GGHM"` fixes terminal residues and designs everything between.

## The result card

When the job completes, a result card opens inline:

### Design target

The ligand's 2D structure, rendered from its canonical SMILES. Click **Show SMILES** to copy the string.

### 3D structure

An interactive Mol\* viewer with the designed protein backbone and the co-folded ligand. Residues whose Cα lies within 5 Å of the ligand are highlighted. Multi-design jobs get a tab strip at the top so you can switch between seeds.

<Note>
  DISCO outputs backbone-only structures (N, Cα, C, O) — no sidechains. Highlighted residues are positional candidates near the ligand, not confirmed catalytic residues. Experimental characterisation is always required.
</Note>

### Structure composition

A compact bar chart of helix, sheet, and coil fractions computed from the backbone with Biotite.

### Physical and chemical properties

Four sequence-derived numbers: molecular weight, isoelectric point, hydropathicity (GRAVY), and Guruprasad instability index.

A collapsible **Sequence-derived heuristics** section underneath shows charge at pH 7, extinction coefficient, aromatic fraction, and rough suggestions for ion-exchange buffer conditions. These are heuristics from the amino acid sequence, not predictions of expression behaviour.

### Model confidence

DISCO's native confidence scores — ranking score, inter-chain ipTM, and steric-clash flag when present.

### Boltz-2 validation

Every design is automatically refolded with Boltz-2 to independently predict the structure from sequence and ligand. MIP then computes the backbone RMSD between DISCO's structure and Boltz-2's prediction.

| RMSD      | Verdict      | Meaning                                                                   |
| --------- | ------------ | ------------------------------------------------------------------------- |
| \< 2.0 Å  | **PASS**     | The sequence encodes the intended fold (paper co-designability threshold) |
| 2.0–3.0 Å | **Marginal** | Worth visual inspection; may be a loop difference                         |
| > 3.0 Å   | **Fail**     | The sequence refolds to something different — consider regenerating       |

Click **Compare with Boltz-2 refold** to open a side-by-side viewer with synchronised cameras. Rotating one structure rotates the other, which makes spotting real conformational differences much faster than reading the number alone.

### Designed sequence

The amino acid sequence in monospace, color-coded by property group (hydrophobic, positive, negative, polar, Cys, Gly, Tyr). Residues with their Cα within 5 Å of the ligand are highlighted in emerald. Hover any residue to see its position and near-ligand status.

## Downloading results

The result card toolbar provides:

* **FASTA** — the designed protein sequence as FASTA, ready for gene synthesis
* **CIF / PDB** — the full co-folded structure, openable in Mol\*, PyMOL, or ChimeraX

Each design is also saved to your **Datasets** library so it is accessible from any future chat.

## How it works

1. **Validation** — MIP parses your SMILES with openchemlib, rejects invalid input before any GPU spins up, and uses the canonical form so equivalent SMILES hit the dedup cache.
2. **Submission** — The DISCO input JSON is sent to a dedicated Cerebrium GPU (NVIDIA L40). Identical jobs submitted within 10 minutes return the existing result.
3. **Design** — DISCO runs `effort` diffusion steps with the configured number of recycles. Typical runtime is 15–60 minutes for `effort=max` on a 150–250 residue protein, scaling linearly with `numDesigns`.
4. **Storage** — Each design (CIF and FASTA) is uploaded to cloud storage.
5. **Validation** — A Boltz-2 refolding job is dispatched automatically. Results are joined back to the DISCO design card as they complete.
6. **Billing** — Charged at 10 credits per second of actual GPU time (including cold start), rounded up to the nearest minute.

## Limits and guardrails

| Parameter                       | Limit                                                                            |
| ------------------------------- | -------------------------------------------------------------------------------- |
| Protein sequence length         | 1,000 residues (L40 GPU ceiling)                                                 |
| SMILES length                   | 5,000 characters                                                                 |
| DNA / RNA target length         | 10,000 bases                                                                     |
| Concurrent GPU jobs per user    | 3                                                                                |
| Minimum credit balance to start | 5,000 credits                                                                    |
| Boltz-2 validation cost         | 1,000 credits per design (skipped with a notification if the balance is too low) |

MIP refuses, before any GPU is used, to design for chemical-weapon agents or scheduled precursors, controlled substances without a clear research context, known toxin scaffolds, or gain-of-function targets on select agents. If your request is legitimate research and MIP refuses, reach out to your institution's biosafety officer and contact us.

## When to use enzyme design vs structure prediction

| Scenario                                                         | Recommended approach                                               |
| ---------------------------------------------------------------- | ------------------------------------------------------------------ |
| You have a target protein and want its 3D structure              | Use [structure prediction](/chat-experiences/structure-prediction) |
| You have a small molecule and want a protein built for it        | Use enzyme design                                                  |
| You have a known scaffold and want to alter a few residues       | Use structure prediction with mutant comparison                    |
| You have a known active-site motif and want a new fold around it | Use enzyme design with motif scaffolding via `proteinSequence`     |

<Tip>
  Enzyme design is iterative. Generate 3–5 designs, inspect the Boltz-2 RMSD per design, and prioritise the co-designable ones for experimental expression. Designs with RMSD above 3 Å are not reliable starting points — regenerate with a different seed or adjust the scaffold.
</Tip>

<Note>
  Designed enzymes are computational predictions, not proven catalysts. Plan for experimental characterisation — expression, purification, and activity assays — before treating a design as a working enzyme. The DISCO paper reports that one round of directed evolution on a designed enzyme can deliver several-fold activity improvements.
</Note>