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

# mBER

> Manifold Binder Engineering and Refinement for antibody design

mBER is an open-source protein design framework specifically engineered for **antibody-format binder design**. By leveraging structural templates and sequence conditioning within the ColabDesign framework, mBER enables backpropagation-based design through AlphaFold-Multimer to produce high-affinity VHH (nanobody) binders.

### Design Philosophy

Unlike "minibinder" tools that generate entirely novel structures, mBER focuses on **format-specific design**. It maintains the highly conserved framework regions of antibodies while optimizing the variable regions (CDRs) for target binding.

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### Experimental Validation

mBER is one of the first open-source methods to demonstrate **double-digit percentage experimental success rates** for antibody design.

* **Scale:** Validated through the screening of over 1 million VHH designs against 145 targets.
* **Success Rate:** Achieved specific binding success against 45% of tested targets.
* **Precision:** After filtering for high-confidence designs, hit rates for specific epitopes reached as high as **38%**.

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### Methods

The mBER pipeline is built around three modular steps:

1. **Template Generation:** Prepares the target structure (with optional truncation) and uses **NanoBodyBuilder2** to create a structural VHH template.
2. **Trajectory Optimization:** Designs binder sequences by iteratively optimizing a continuous sequence representation against AlphaFold-Multimer loss functions, guided by sequence priors from **ESM2** or **AbLang2**.
3. **Evaluation:** Final designs are re-folded and scored using AlphaFold-Multimer confidence metrics to identify the most promising candidates.

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### Filtering & Best Practices

The authors highlight that AlphaFold-Multimer's **ipTM** score is the most reliable predictor of experimental success:

* **Recommended Filter:** `ipTM > 0.7` for a significant increase in hit rates.
* **Strict Filter:** `ipTM > 0.8` for the highest probability of finding specific binders.
* **Note:** While `pLDDT` reflects the quality of the monomeric fold, it is a weaker predictor of actual binding success than `ipTM`.

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### Configuration (mber-vhh)

When running mBER on Tamarind, you can configure the following settings:

| Setting              | Type     | Description                                                             |
| :------------------- | :------- | :---------------------------------------------------------------------- |
| **Target Protein**   | `PDB`    | The structure of the protein you wish to bind to.                       |
| **Target Chains**    | `String` | Chain IDs of the target (e.g., `A, B`).                                 |
| **Hotspot Residues** | `List`   | Specific epitope residues to target. If empty, mBER auto-detects sites. |
| **VHH Template**     | `Seq`    | Framework sequence with `*` marking CDR positions for design.           |
| **PLM Model**        | `Enum`   | The language model used for guidance (`ESM` or `Ablang2`).              |
| **Temperature**      | `Float`  | Controls sequence sampling diversity (0.1 to 2.0).                      |

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### Target Selection Tips

mBER performance is highly sensitive to the chosen **epitope**.

* Success rates vary significantly between different hotspots on the same target.
* Because mBER provides prospective epitope information, it is highly useful for designing binders that block specific protein-protein interactions.

[Try mber](https://app.tamarind.bio/tools/mber)