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Antibody Evolution: Efficient Evolution of Antibodies with General Protein Language Models
Scientists have developed a highly efficient method for the evolution of human antibodies using general protein language models (pLMs). This approach, called Antibody Evolution, guides artificial evolution by suggesting mutations that are "evolutionarily plausible," without needing any explicit information about the antibody's target antigen, binding specificity, or protein structure. This strategy dramatically reduces the experimental burden of traditional directed evolution, which often requires screening a vast number of non-functional variants to find a single viable one.
The method successfully improved the binding affinities of seven antibodies, including four that were already highly mature and three that were unmatured. For example, the unmatured mAb114 UCA antibody saw a 160-fold improvement in binding affinity for ebolavirus glycoprotein (GP) after only two rounds of evolution.
How Antibody Evolution Works
The core of the Antibody Evolution method relies on the power of general pLMs to act as a universal guide for mutation.
Plausible Mutation Suggestions: The method uses large-scale pLMs, such as ESM-1b and ESM-1v, that are trained on millions of natural protein sequences. These models learn general evolutionary rules, enabling them to predict which single-residue substitutions are most likely to be evolutionarily plausible.
Targeted Screening: Instead of random mutagenesis, the algorithm provides a small, manageable set of high-likelihood variants for experimental screening. In a two-round process, researchers first test single-residue substitutions and then recombine the most successful mutations to test for further improvements in the second round.
Framework Mutations: The pLMs frequently recommend affinity-enhancing substitutions in framework regions, which are typically less mutated during conventional affinity maturation. This demonstrates that the models learn complex evolutionary rules that go beyond the typical complementarity-determining regions (CDRs).
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 Antibody Evolution on Tamarind Bio
Using the Antibody Evolution method on a platform like Tamarind would accelerate therapeutic antibody discovery by providing an efficient, unsupervised, and cost-effective workflow.
Rapid Lead Optimization: The model can be used to quickly identify improved variants for a parental antibody. By recommending a small number of high-potential substitutions, the method reduces the time and cost associated with traditional brute-force screening methods.
High-Throughput and Automation: The computational pipeline is highly efficient, taking less than one second per antibody on GPU-accelerated hardware. This efficiency would allow researchers to run large-scale evolutionary campaigns on a platform like Tamarind, rapidly generating a diverse set of candidates and automating the selection process.
Broad Applicability: The paper shows that the same models that guide antibody evolution can also enrich for high-fitness substitutions in diverse protein families, including those related to antibiotic resistance and enzyme activity. This suggests the method's potential for a wide range of protein engineering tasks.
How to Use Antibody Evolution on Tamarind Bio
To leverage the power of Antibody Evolution, a researcher could follow this streamlined workflow on Tamarind:
Access the Platform: Begin by logging in to the tamarind.bio website.
Select Antibody Evolution: From the list of available computational models, choose the Antibody Evolution tool.
Input a Wild-Type Sequence: Provide a single wild-type antibody sequence to the platform. No other information, such as binding data or structural models, is needed.
Generate Mutations: The platform would use an ensemble of general pLMs to compute the likelihoods of all single-residue substitutions and select a small, manageable set of variants with higher evolutionary likelihood than the wild-type.
Test and Recombine: The generated variants can be experimentally measured for binding affinity. The results can be fed back into the platform to guide the generation of a second round of variants with combined mutations, further optimizing the antibody's properties.