How to Use EvoNB Online
Try EvoNB
Commercially Available Online Web Server
EvoNB: A Protein Language Model Workflow for Nanobody Optimization
EvoNB, an integrated workflow based on protein language models (pLMs) designed for the efficient mutation prediction and affinity optimization of nanobodies (VHH). Nanobodies, or single-domain antibodies, are highly desirable therapeutic molecules, but rationally optimizing their binding affinity often requires extensive experimental or computationally demanding screens. EvoNB solves this by providing a pipeline that achieves high accuracy and efficiency in predicting beneficial mutations.
How EvoNB Works
EvoNB functions as an integrated pipeline that intelligently combines machine learning with biophysical simulation for high-confidence prediction:
pLM-Based Mutation Prediction: The workflow uses a pre-trained protein language model to rapidly screen the mutation landscape of a nanobody sequence. This pLM provides initial predictions of which single-point mutations are most likely to be beneficial.
Molecular Dynamics (MD) Simulation: For high-confidence prediction and optimization, the candidates selected by the pLM undergo rigorous molecular dynamics (MD) simulation.
Binding Affinity Prediction: Finally, the changes in binding affinity are computationally evaluated using highly accurate methods like MM/PBSA or MM/GBSA to confirm the predicted affinity improvement of the final nanobody variant.
This integrated approach successfully predicts mutations that significantly improve the binding affinity of nanobodies against specific targets.
What is Tamarind Bio?
Tamarind Bio is a pioneering no-code bioinformatics platform built to democratize access to powerful computational tools for life scientists and researchers. Recognizing that many cutting-edge machine learning models are often difficult to deploy and use, Tamarind provides an intuitive, web-based environment that completely abstracts away the complexities of high-performance computing, software dependencies, and command-line interfaces.
The platform is designed to provide easy access to biologists, chemists, and other researchers who may not have a background in programming or cloud infrastructure but want to run experimental models with their data. Key features include a user-friendly graphical interface for setting up and launching experiments, a robust API for integration into existing research pipelines, and an automated system for managing and scaling computational resources. By handling the technical heavy lifting, Tamarind empowers researchers to concentrate on their scientific questions and accelerate the pace of discovery.
Accelerating Discovery with EvoNB on Tamarind Bio
Using EvoNB on a platform like Tamarind Bio would drastically accelerate nanobody engineering and therapeutic development:
Efficient Lead Optimization: Researchers can use the pLM component for fast, initial screening of thousands of possible mutations, filtering the vast sequence space down to a small, viable set.
High-Confidence Leads: The platform manages the computationally intensive molecular dynamics simulations and binding affinity calculations, ensuring that the final optimized sequences are predicted with high confidence before moving to the costly experimental validation stage.
Streamlined Nanobody Design: Tamarind Bio would automate the entire multi-step EvoNB workflow, allowing researchers to quickly input a parental VHH sequence and receive an optimized variant with improved binding affinity.
How to Use EvoNB on Tamarind Bio
To leverage EvoNB's power, a researcher could follow this streamlined workflow on Tamarind Bio:
Access our Platform: Begin by logging in to the tamarind.bio website.
Select EvoNB: From the list of tiles, either search or select EvoNB.
Input a Nanobody Sequence: Provide the amino acid sequence of the nanobody (VHH) you wish to optimize.
Run EvoNB Workflow: The platform executes the integrated workflow: pLM prediction, structural modeling, and MD simulation.
Acquire Optimized Sequences: The output provides a list of predicted nanobody variants, ranked by their predicted improvement in binding affinity, ready for experimental synthesis.