{"id":15268,"date":"2024-05-18T12:32:12","date_gmt":"2024-05-18T07:02:12","guid":{"rendered":"https:\/\/cbirt.net\/staging\/?p=15268"},"modified":"2026-10-22T14:08:38","modified_gmt":"2026-10-22T08:38:38","slug":"revolutionizing-antibody-structure-based-design-with-antifold-a-breakthrough-in-inverse-folding-technology","status":"publish","type":"post","link":"https:\/\/cbirt.net\/staging\/revolutionizing-antibody-structure-based-design-with-antifold-a-breakthrough-in-inverse-folding-technology\/","title":{"rendered":"Revolutionizing Antibody Structure-based Design with AntiFold: A Breakthrough in Inverse Folding Technology"},"content":{"rendered":"\n

Scientists from the University of Oxford<\/em> along with the Technical University of Denmark<\/em> have introduced AntiFold<\/strong>: a specialized model for antibody design that will help in improving sequence recovery in the most important regions and, at the same time, will aid in the zero-shot affinity prediction. It is the antibody structure that is the input, and by its means, we can know the mutation tolerance without changing the backbone structure. The structurally stable sequences are designed to maintain the structural integrity, thus helping the antibodies to be targeted effectively, which in turn would lead to better therapeutics.<\/em><\/p>\n\n\n\n

Antibodies are a type of therapeutic that can be used to cure many diseases. However, they can be difficult to design because they require the optimization of several properties such as efficacy, manufacturability, and safety. Machine learning methods have turned out to be the tools that are very valuable in this process, thereby facilitating the antibody development in such a way that it addresses the issues related to immunogenicity and aggregation and at the same time it increases the desirable properties like binding affinity. However, the issue is picking changes that preserve the antibody structure while increasing its characteristics.<\/p>\n\n\n\n

To solve this issue, Researchers present AntiFold, a specialty of the antibodies-specific inverse folding model, which is the result of ESM-IF1. Unlike the other tools, AntiFold greatly improves the sequence recovery in the hypervariable CDR regions and provides the ability to predict the affinity without any samples. The method assesses the tolerance of mutations without the need to change the backbone structure and the probability of each amino acid position. Users can, if they want, set the regions of the sequences and tune the temperature parameter to regulate the diversity of the sequences. AntiFold designer sequences have a high structural similarity to the original structure upon re-folding, which is very important.<\/p>\n\n\n\n

AntiFold, along with other property prediction tools, gives a strong weapon for directing the antibody optimization process by reducing the range of options for experimental testing. The combined way of working could make the development process of antibodies simpler and more efficient. Thus, the discovery of new and better therapies could be increased.<\/p>\n\n\n\n

Data Collection<\/h2>\n\n\n\n

The advantage that AntiFold has is its ESM-IF1 pre-training on the over 12 million structures, which enables it to be trained further on both solved and predicted antibody structures. For a fair comparison with AbMPNN, the researchers utilized the same dataset: Filling the gap of 147,458 sequences from the Observed Antibody Space (OAS) paired database, the ABodyBuilder2 model solved 2,074 cases from the Structural Antibody Database (SAbDab). The data was divided into training, validation, and testing sets using a 90% split of the sequence identity on the concatenated CDR sequence based on the 80\/10\/10 cutoff. However, since they could not directly compare it with IgMPNN because of the unavailability of the model weights and training data, AntiFold’s performance is still remarkable in this context.<\/p>\n\n\n\n

Training of AntiFold<\/h2>\n\n\n\n

In the process of fine-tuning, Researchers came up with different methods to improve the performance in the validation set, especially in the CDRH3 loop, which is the amino acids recovery, i.e., essential for antigen binding. These strategies included:<\/p>\n\n\n\n