mBER

What is mBER?

mBER (Manifold Binder Engineering and Refinement) is an open-source computational framework for designing VHH nanobody binders that target specific protein surfaces. Developed by Manifold Bio and released in 2024, mBER uses AlphaFold-Multimer's structure prediction capabilities in reverse—optimizing antibody sequences until the model predicts confident binding to a target epitope. The system has demonstrated experimental success rates as high as 38% for certain targets, validated through million-scale screening experiments.

VHH nanobodies are single-domain antibodies derived from camelid heavy-chain-only antibodies, first discovered in 1989 by Professor Raymond Hamers at Vrije Universiteit Brussel. Unlike conventional antibodies that contain both heavy and light chains, VHH domains consist of a single variable domain (12–14 kDa, approximately one-tenth the size of traditional antibodies). VHH nanobodies offer therapeutic advantages including higher solubility, thermal stability, tissue penetration, and lower production costs. The FDA has approved several nanobody therapeutics, including Caplacizumab for blood disorders (2019) and CARVYKTI for multiple myeloma.

How to use mBER online

ProteinIQ provides a web-based interface for running mBER without GPU infrastructure or command-line installation. Upload a target protein structure, specify binding regions, adjust design parameters, and receive ranked VHH designs with 3D visualization.

Inputs

Settings

Target settings

Design parameters

Quality thresholds

Results

The output consists of a ranked list of VHH nanobody designs with an interactive 3D structure viewer. Each design includes a novel amino acid sequence and predicted binding complex.

Interpreting confidence metrics

iPTM (interface prediction)

• > 0.85 — Exceptional interface confidence; high probability of experimental binding
• 0.75–0.85 — High confidence; suitable for experimental validation
• 0.65–0.75 — Moderate confidence; consider additional validation
• < 0.65 — Low confidence; may require redesign or alternative approaches

pLDDT (structure quality)

• > 0.90 — Very high confidence; near-atomic accuracy expected
• 0.70–0.90 — High confidence; well-structured regions
• 0.50–0.70 — Moderate confidence; some flexible or uncertain regions
• < 0.50 — Low confidence; disordered or poorly modeled regions

Designs with both high iPTM (> 0.75) and high pLDDT (> 0.70) have the greatest likelihood of experimental success. Manifold Bio's validation experiments demonstrated per-binder success rates up to 38% for optimized epitopes.

How does mBER work?

mBER employs gradient-based optimization through AlphaFold-Multimer, effectively "hallucinating" nanobody sequences that fold into favorable binding configurations with target proteins. The approach inverts the traditional protein folding problem: instead of predicting structure from sequence, mBER optimizes sequences to achieve desired structural interactions.

Structure-guided sequence optimization

mBER leverages AlphaFold-Multimer as a differentiable scoring function. The algorithm initializes a random VHH sequence, combines it with the target protein structure, and runs AlphaFold-Multimer to predict the complex structure. By backpropagating gradients through the neural network, mBER updates the sequence to maximize binding interface confidence metrics (iPTM) and structural quality (pLDDT).

The system builds upon the ColabDesign framework, which pioneered backpropagation-based protein design through structure prediction models. mBER extends this approach with nanobody-specific templates and sequence constraints.

VHH framework templates

To ensure designs adopt authentic nanobody architecture, mBER conditions generation on VHH structural templates. The framework uses NanoBodyBuilder2 to construct initial nanobody structures from template sequences. These templates enforce the characteristic immunoglobulin fold while allowing complementarity-determining region (CDR) loops to vary for target binding.

The CDR3 loop, which forms the primary antigen-binding surface in VHH nanobodies, receives particular attention during optimization. mBER allows this region to explore diverse conformations while maintaining structurally plausible geometries.

Sequence conditioning with ESM-2

To bias designs toward naturally occurring antibody sequences, mBER incorporates ESM-2, a protein language model trained on millions of protein sequences. ESM-2 generates position-specific amino acid probabilities for masked regions of the VHH framework. These probabilities are converted to logits and sampled to produce sequences that resemble human antibody repertoires.

This dual conditioning—structural templates for geometry and sequence priors for naturalness—helps mBER avoid generating antibodies with unusual or developability-limiting sequences while exploring the design space for target-specific binding.

Trajectory-based filtering

mBER generates multiple design trajectories in parallel, each starting from different random initializations. The algorithm runs optimization for a fixed number of iterations per trajectory, evaluating iPTM and pLDDT at each step. Trajectories that fail to meet minimum quality thresholds are discarded; successful trajectories contribute designs to the final output.

The Maximum trajectories parameter controls computational budget. For easily designable epitopes, 1000 trajectories typically yield sufficient high-quality designs. Challenging targets (buried epitopes, constrained surfaces) may require 3000–5000 trajectories to identify successful binders.

Hotspot targeting

When specific residues are designated as hotspots, mBER biases the optimization to favor interactions with those positions. The algorithm increases weighting for inter-residue contacts between VHH CDR regions and hotspot residues, guiding designs toward epitope-specific binding modes.

Hotspot specification enables rational design based on prior experimental data (mutagenesis studies, known binding sites) or computational predictions (conservation analysis, druggability assessments). This feature distinguishes mBER from purely de novo approaches that lack epitope control.

Limitations

• Experimental validation required — Computational predictions do not guarantee experimental binding. While mBER achieves industry-leading success rates (up to 38% for optimized epitopes), wet-lab validation remains necessary for therapeutic development.
• VHH format only — The current implementation specializes in VHH single-domain antibodies. Design of conventional antibodies (with light chains), scFvs, or alternative scaffolds requires different frameworks.
• Target structure dependency — Accurate target structures are essential. Low-resolution structures, missing loops, or conformational flexibility may reduce design accuracy. Consider using AlphaFold2 to generate high-quality target structures when experimental structures are unavailable.
• Epitope accessibility — Buried or geometrically constrained epitopes may prove difficult for VHH binding due to steric constraints. Surface-exposed regions with concave or pocket-like geometry typically yield better results.
• Computational cost — Generating 10 designs with 1000 trajectories requires substantial GPU memory (16–32 GB VRAM) and computation time (30 minutes to 2 hours depending on target size and parameter settings).
• No affinity prediction — mBER predicts binding modes and structural confidence but does not estimate binding affinity (KD values). Designed binders may exhibit weak affinities requiring subsequent affinity maturation.

Related tools

• BindCraft — De novo protein binder design using AlphaFold2 backpropagation and ProteinMPNN, supporting custom scaffolds beyond VHH format
• AlphaFold2 — Structure prediction for generating high-quality target structures when experimental PDB files are unavailable
• ESM-IF1 — Inverse folding for redesigning existing antibody sequences given fixed backbone structures
• ProteinMPNN — Sequence design for fixed backbone structures, useful for optimizing designed VHH scaffolds
• RFdiffusion — Generative diffusion model for de novo protein binder design with alternative algorithmic approach
• DynamicBind — Protein-ligand binding prediction with conformational flexibility modeling

Applications

• Therapeutic antibody discovery — Designing VHH nanobodies against disease-relevant targets (receptors, cytokines, enzymes) for drug development. The small size and stability of VHHs enable delivery routes unavailable to conventional antibodies.
• Diagnostic reagent development — Creating high-affinity binders for immunoassays, biosensors, and imaging applications. VHH stability at elevated temperatures and in non-physiological buffers improves reagent shelf life.
• Epitope-specific targeting — Generating binders selective for particular epitopes or protein conformations. Hotspot targeting enables discrimination between closely related protein family members or disease-associated protein variants.
• Blocking antibody design — Developing competitive inhibitors for protein-protein interactions by designing VHHs targeting functional interfaces. High specificity and small size allow VHHs to access cryptic epitopes inaccessible to larger antibodies.
• Structural biology tools — Engineering crystallization chaperones or cryo-EM fiducial markers. VHH rigidity and defined geometry can stabilize flexible proteins or provide alignment references for structure determination.