How to Use ABodyBuilder3 Online
Try ABodyBuilder3
Commercially Available Online Web Server
ABodyBuilder3: Improved and Scalable Antibody Structure Prediction
Scientists have introduced ABodyBuilder3, an improved and scalable deep learning model for antibody structure prediction, which achieves state-of-the-art accuracy in modeling CDR loops. The model builds upon its predecessor, ABodyBuilder2, by integrating powerful new techniques to enhance both the accuracy and efficiency of antibody structure prediction. This advancement enables the scalable and precise assessment of large numbers of therapeutic candidates, which is a central task in the design and development of monoclonal antibodies.
How ABodyBuilder3 Works
ABodyBuilder3 is an advanced model that incorporates several key enhancements over previous methods:
Language Model Embeddings: The model replaces the traditional one-hot encoding of amino acid residues with embeddings from a protein language model (pLM) like ProtT5, which is pre-trained on billions of protein sequences. This provides a richer representation of the antibody sequence, leading to a measurable reduction in prediction error, particularly for challenging regions like the CDRH3 and CDRL3 loops.
Scalable Architecture: The model's implementation has been optimized for modern GPU architectures, leveraging techniques like vectorization and mixed-precision training (bfloat16). This makes ABodyBuilder3 more than three times faster than ABodyBuilder2 and allows it to be scaled easily across multiple GPUs.
Accurate Uncertainty Estimation: ABodyBuilder3 features a predicted Local Distance Difference Test (pLDDT) to estimate the uncertainty of its predictions. This method, inspired by AlphaFold2, provides a per-residue confidence score from a single model run, which is more computationally efficient than the ensemble-based approach of its predecessor and correlates well with the accuracy of the predicted structures.
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 ABodyBuilder3 on Tamarind Bio
Using ABodyBuilder3 on a platform like Tamarind could dramatically accelerate therapeutic antibody design and discovery by providing a highly accurate, scalable, and user-friendly workflow.
High-Throughput Screening: The model's speed and efficiency make it ideal for predicting the structures of large numbers of antibody candidates. On Tamarind, researchers could screen massive libraries of therapeutic candidates to quickly assess their developability and potential binding properties.
Improved Design and Optimization: The high accuracy of ABodyBuilder3, especially in predicting CDR loop conformations, provides a more reliable foundation for in silico design and optimization efforts. Researchers can use the predicted structures to guide further modifications and improve the properties of their antibody candidates.
Confidence in Predictions: The pLDDT confidence scores allow researchers to quickly identify which parts of their predicted structures are most reliable, enabling them to make more informed decisions about which candidates to pursue for experimental validation.
How to Use ABodyBuilder3 on Tamarind Bio
To leverage ABodyBuilder3's power, a researcher could follow this streamlined workflow on Tamarind:
Access the Platform: Begin by logging in to the tamarind.bio website.
Select ABodyBuilder3: From the list of available computational models, choose the ABodyBuilder3 tool.
Input an Antibody Sequence: Provide the amino acid sequence of the antibody's variable region.
Run ABodyBuilder3: The platform would run the ABodyBuilder3 model to predict the 3D structure of the antibody.
Analyze the Predicted Structure: The output provides a detailed full-atom structure of the antibody, including the CDR loops and a pLDDT score for each residue. You can use these metrics to evaluate the quality of the prediction and assess the structural properties of your antibody.
Refine and Optimize: The predicted structure can be used as a starting point for other downstream applications on the platform, such as molecular dynamics simulations or further design and optimization using other models.