How to Use TNP (Therapeutic Nanobody Profiler) Online
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The Therapeutic Nanobody Profiler (TNP): A Tool for Nanobody Developability
Therapeutic Nanobody Profiler (TNP), an open-source computational tool for predicting nanobody developability. Unlike conventional antibodies, nanobodies have unique structural features that make existing computational tools for antibodies unsuitable for direct application. TNP is specifically tailored for nanobodies and accounts for these properties to improve the design of this novel therapeutic format.
How the TNP Works
TNP is a computational profiler that evaluates nanobody sequences and structures based on a set of six key metrics. These metrics, which are tailored to nanobodies' unique characteristics, include:
Surface Patches: Patches of surface hydrophobicity, positive charge, and negative charge across the CDR vicinity.
CDR Lengths: Total CDR length and the length of the CDR3 loop.
CDR3 Compactness: A measure of the CDR3 loop's reach relative to its length, which helps capture the diversity of nanobody paratope shapes.
The tool benchmarks a candidate nanobody's scores against a reference distribution derived from 36 currently available clinical-stage nanobody sequences. Based on these comparisons, it assigns a red flag if a property is outside the clinical range and an amber flag if it is within the lowest or highest 5% of the distribution.
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 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 TNP on Tamarind Bio
Using TNP on a platform like Tamarind Bio could accelerate the design and development of nanobody therapeutics by:
Early-Stage Developability Assessment: Researchers can quickly screen potential nanobody candidates and receive a developability profile before committing to costly and time-consuming experimental validation. This helps to identify and eliminate high-risk candidates early in the discovery process.
Targeted Optimization: The TNP metrics can guide protein engineering efforts by highlighting potential developability liabilities, such as unfavorable surface charge patches or CDR3 loop conformations.
High-Throughput Analysis: The availability of TNP as a web application and Python package allows for high-throughput analyses, which would be ideal on a scalable platform like Tamarind. Researchers could screen large libraries of nanobody candidates to identify those with the most favorable developability profiles.
How to Use TNP on Tamarind Bio
To leverage TNP's power, a researcher could follow this streamlined workflow:
Access the Platform: Begin by logging in to the tamarind.bio website.
Select TNP: From the list of available computational models, choose the TNP tool.
Provide Nanobody Sequence: Input a nanobody sequence into the platform.
Generate Profile: The platform uses TNP to calculate the six key developability metrics and provides a visual profile.
Benchmark Against Clinical Data: The candidate's profile is automatically benchmarked against the clinical-stage reference data, with flags assigned to highlight potential risks.
Visualize and Annotate: The platform also provides visualizations of the predicted nanobody structure with annotations for surface patches and key residues, aiding in the design and optimization process.