MIT Researchers Propose AbMAP: A Personalized Protein Language Model (PLM) for Antibodies

Some of the most promising drug candidates in current therapies have been antibodies. The incredible structural diversity of antibodies, which allows them to recognize an incredibly wide range of potential targets, is to thank for this therapeutic success. Its hypervariable sections, which are essential for the functional specificity of antibodies, is where this variety arises. In the past, methods such as immunization or directed evolutionary methods such as phage display selection have been used to experimentally develop an antibody against a target of interest. However, the creation and selection procedure requires a lot of time and money. The potential structure space needs to be thoroughly explored, which can provide candidates with unfavorable binding properties.

Since hypervariable sections of antibody structures show structurally distinctive evolutionary patterns, general protein structure prediction methods may have difficulty in predicting them. In addition, it is difficult to easily take into account subsequent problems. Therefore, there is a need for computational techniques that more effectively refine a small number of experimentally determined candidates or that develop a new antibody from scratch for a specific target. Modeling the 3D structure of the whole antibody or its CDRs has been a step in this approach, but the accuracy of these models could be better. You cannot perform large-scale computational scanning or analyze a person’s antibody repertoire, which can comprise millions of sequences because they are slow and take many minutes per antibody structure.

Recently, high-dimensional protein representations have been created using machine learning methods employed in natural language processing. Protein language models allow the prediction of protein properties while implicitly capturing structural features. One approach is to hire PLMs trained in the corpus of all proteins when talking about antibodies. We refer to these as “foundational” PLM, which is the machine learning language for large multi-use models. However, sequence diversity in CDRs is not evolutionarily constrained, which means that antibody CDRs directly violate the distribution premise behind fundamental PLMs. One of the main reasons why AlphaFold 2 works less efficiently on antibodies than on ordinary proteins is the need for higher quality multiple sequence alignments.

Because of this, researchers at MIT and Sanofi R&D Cambridge have suggested a different set of methods known as IgLM. These methods train the PLM only on antibody and B-cell receptor sequence repertoires. These methods are more effective in addressing the hypervariability of CDRs. Still, they need the diverse corpus of all protein sequences to base their training on, which prevents them from accessing the deep understanding provided by basic PLMs. Furthermore, current methods such as AntiBERTa spend significant explanatory power on modeling the non-CDRs of the antibody, which are considerably less varied and less important for antibody binding specificity.

His main conceptual contribution is to use supervised learning techniques trained on antibody structure and binding specificity profiling to solve the deficiency of fundamental PLMs in antibody hypervariable regions. Specifically, they introduce three important advances:

1. We are maximizing the use of available data by restricting the learning task to hypervariable antibody regions.
2. They are refining the hypervariable region embeddings of the reference PLM to better capture antibody structure and function.
3. He is developing a multi-task supervised learning formulation that considers binding specificity and protein structure of the antibody to monitor rendering.

Therefore, this approach can help to evaluate potential antibody sequences for pharmacological ability before expensive in vitro and preclinical studies.

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