How to Use AlphaBind Model Finetuning Online

AlphaBind: A New Model for Antibody-Antigen Binding Optimization

AlphaBind, a domain-specific deep learning model designed to predict and optimize antibody-antigen binding affinity. The model achieves state-of-the-art performance by utilizing protein language model embeddings and pre-training on millions of quantitative measurements of antibody-antigen binding strength from unrelated systems. This enables a guided optimization pipeline that can deliver candidates with substantially improved binding affinity, even for antibodies that were already affinity-matured.

How AlphaBind Works

The AlphaBind model uses a transformer architecture that takes as input ESM-2nv embeddings of both an antibody and a target sequence. Its effectiveness comes from a two-stage training process:

1. Pre-training: The model is first pre-trained on approximately 7.5 million quantitative affinity measurements from unrelated antibody-antigen systems. This allows AlphaBind to learn general sequence-function relationships that can be applied to new contexts.

2. Fine-tuning: For a specific optimization campaign, the pre-trained model is then fine-tuned on a smaller, local dataset of antibody variants from a parental antibody. This process generates an antibody-specific regressor for predicting affinity.

This fine-tuning and optimization process can generate thousands of high-affinity antibody derivatives, with some candidates showing affinity improvements ranging from 2x to 74x over their parental antibodies.

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 AlphaBind on Tamarind Bio

Using AlphaBind on a platform like Tamarind would streamline antibody engineering and lead optimization by:

• Guided Optimization: The platform could use a fine-tuned AlphaBind model to guide a search for thousands of antibody variants with improved binding affinity. In one campaign, 10 out of 10 top candidates expressed and had superior binding affinity than their parental antibodies, demonstrating a 100% success rate.

• Multi-Objective Engineering: A fine-tuned AlphaBind model can also be used to guide sequence optimization for predicted developability and immunogenicity while maintaining affinity. This allows for the design of optimized variants with no predicted sequence liabilities and improved expression.

• High-Throughput and Scalability: AlphaBind's ability to be fine-tuned in approximately one hour per parental antibody on a single H100 GPU makes it highly efficient. A platform like Tamarind could leverage this efficiency to generate and screen millions of candidates, providing thousands of high-quality variants in a single round of data generation.

How to Use AlphaBind on Tamarind Bio

To leverage AlphaBind's power, a researcher could follow this streamlined workflow on Tamarind:

1. Access the Platform: Begin by logging in to the tamarind.bio website.

2. Select AlphaBind Model Finetuning: From the list of available computational models, choose the AlphaBind Model Finetuning tool.

3. Provide a Parental Antibody: Start with a parental antibody sequence and a dataset of its variants with quantitative affinity measurements.

4. Fine-Tune the Model: The platform would use the parental antibody data to fine-tune a pre-trained AlphaBind model, creating a custom affinity prediction model for your specific antibody-antigen system.

5. Generate Candidates: The fine-tuned model would then be used to generate a pool of new candidate sequences optimized for predicted binding affinity.

6. Filter and Validate: These candidates can be screened for predicted developability and immunogenicity issues, and the top candidates can be selected for in vitro validation to confirm their improved properties.

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