Skip to main content
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.

​
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%.

​
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.

​
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.

​
Configuration (mber-vhh)

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

​
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