Accelerate Nanobody Engineering with EvoNB on Vecura

What is EvoNB?

EvoNB is a specialized protein language model engineered specifically for nanobody (VHH) optimization. By fine-tuning the powerful ESM2 (esm2_t33_650M_UR50D) architecture on a massive dataset of approximately 7.66 million nanobody sequences, it captures the unique structural and evolutionary constraints inherent to the VHH scaffold. It is particularly useful for protein engineers looking to improve the stability, expressibility, or binding affinity of their nanobody candidates through targeted point mutations and structural quality assessments.

What can users do with EvoNB on Vecura?

With EvoNB on Vecura, users can:

• Optimize Nanobody Sequences: Identify beneficial point mutations by predicting which amino acid substitutions are more likely to be tolerated or improved compared to the wildtype.
• Analyze Regional Stability: Use IMGT-defined segmentation to score specific framework (FR) and complementarity-determining regions (CDR) for structural consistency.
• Ensemble-Based Prediction: Leverage an ensemble of five model checkpoints to reduce variance and increase the confidence of mutation probability estimates.
• Automated Workflow Integration: Process nanobody sequences directly without the need for manual infrastructure management or complex local environment setup.

What the output means

The output provides probabilistic scores and mutation recommendations. Mutation results indicate specific residue positions and potential amino acid substitutions that align better with the learned distribution of successful nanobody sequences. Region probabilities assign a value to each IMGT segment; higher values suggest consistency with known nanobody patterns, while lower values may highlight areas for potential engineering or stability concerns.

This output should be used to support scientific decision making. It does not replace experimental validation.

Why this matters

Nanobody engineering often faces challenges in balancing stability and binding performance. Traditional methods can be time-consuming and may lack the domain-specific insight required to navigate the complex sequence space of VHH scaffolds. EvoNB bridges this gap by providing a data-driven, scalable approach to candidate prioritization.

By incorporating high-scale evolutionary patterns learned from millions of sequences, EvoNB allows researchers to filter out suboptimal candidates early in the design process. This accelerates the R&D pipeline, helping scientists focus their experimental efforts on the variants most likely to succeed in the lab.

• Developed by: DynaX-C
• Source: Official GitHub repository
• Reference: EvoNB paper (Journal of Pharmaceutical Analysis, 2025)