The quest for designing effective and specific therapeutic antibodies (Abs) has been a cornerstone of modern biotechnology and pharmaceutical research. Antibodies, with their unique ability to bind to specific antigens, play a crucial role in the treatment of various diseases, including cancers, autoimmune disorders, and infectious diseases. However, the traditional methods of antibody development are often time-consuming and costly, requiring extensive experimental validation and optimization.
In recent years, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to streamline and enhance this process. By leveraging vast datasets and advanced algorithms, AI-driven models can predict antibody sequences and structures with remarkable accuracy, thus accelerating the development pipeline. One such breakthrough in this domain is the introduction of AntiFold, an antibody-specific model designed to harness the capabilities of the ESM-IF1 inverse folding framework for optimized antibody sequence design. This blog delves into the intricate workings of AntiFold and its potential to revolutionize therapeutic antibody development.
Høie et al. recently introduced AntiFold, an antibody-specific model for structure-based sequence design based on the ESM-IF1 inverse folding model. ESM-IF1 is a general-purpose protein sequence design model for given protein structures, built by Meta by training on experimentally determined protein structures and over 12 million AlphaFold2-predicted structures.
Antibodies are an important class of therapeutic agents with broad applications in treatments ranging from cancer to autoimmune disorders and viral infections. However, the development of therapeutic Abs is a challenging protein design problem as it involves not only sequence selection but also the optimization of multiple objectives, including efficacy, ease of manufacturing, safety, and more. Computational design is an ideal approach with huge potential to generate target-specific Abs. Although AI-based computational protein design has seen significant progress recently, with models like ProteinMPNN and ESM-IF1 achieving highly accurate sequence design for a wide range of proteins, the accurate design of Abs remains elusive.
Several machine learning methods have demonstrated the ability to address some of the steps in the Ab development process, including reducing immunogenicity and aggregation. Other important tasks include the selection of sequence mutations that fit target structures and the optimization of structure-based properties such as stability and antigen binding.
The AntiFold model was built by fine-tuning ESM-IF1 on datasets of experimental Ab structures (2,074 complexes from SAbDab) augmented with predicted Ab structures (147,458 ABodyBuilder2-modeled structures from OAS). Given a target Ab structure, the model predicts optimized sequences that would fold into the target structure. Sequences designed by AntiFold, when refolded using a structure prediction model, showed high structural similarity to the expected structure.
In benchmarking tests against existing inverse folding models, including ProteinMPNN, ESM-IF1, and AbMPNN, AntiFold showed the highest sequence recovery of Ab complementarity-determining regions. When evaluated in a zero-shot antibody-antigen binding affinity test, AntiFold demonstrated better correlations than other methods. The accuracy of AntiFold was further improved when information about the target antigen was provided. AntiFold also shows promise in Ab optimization as it assigns low probabilities to residue mutations that ablate Ab-Ag binding.
It remains to be seen how such a powerful model could be integrated into experimental settings for therapeutic Ab discovery as well as multi-objective optimization using reinforcement and active learning.
Paper: AntiFold: Improved antibody structure-based design using inverse folding
GitHub: https://github.com/oxpig/AntiFold
Webserver: https://opig.stats.ox.ac.uk/webapps/antifold/
Colab Notebook: https://colab.research.google.com/drive/1TTfgjoZx3mzF5u4e9b4Un9Y7b_rqXc_4
