Abstract
Antibodies are proteins of the immune system that are able to bind to a huge variety of different substances, making them attractive candidates for therapeutic applications. Antibody structures have the potential to be useful during drug development, allowing the implementation of rational design procedures. The most challenging part of the antibody structure to experimentally determine or model is the H3 loop, which in addition is often the most important region in an antibody's binding site. This review summarises the approaches used so far in the pursuit of accurate computational H3 structure prediction.
Highlights
Antibodies are proteins that bind to foreign objects that find their way into an organism, preventing them from causing harm and marking them for removal
While the accuracy of H3 structure prediction has improved in recent years, as evidenced by the results of the two Antibody Modelling Assessments [72,73], the modelling of H3 loops remains the biggest challenge in producing accurate and useful antibody models
There remains a marked difference between the accuracy of H3 prediction compared to that of the canonical CDRs: these five loops are regularly predicted with sub-ångström accuracy while H3 prediction accuracy is much more variable, typically with an root mean square deviation (RMSD) of between 1.5 and 3 Å, but often worse, in particular in the nonnative environment
Summary
Antibodies are proteins that bind to foreign objects that find their way into an organism, preventing them from causing harm and marking them for removal. Knowledge of an antibody’s structure is extremely useful when developing a novel therapeutic, allowing it to be engineered more rationally This knowledge can be used to increase binding affinity by guiding residues to be mutated, through the use of computational techniques such as binding affinity prediction [e.g. Ref. 9], epitope and paratope prediction [10,11], stability measurements [e.g. Ref. 12], and docking [e.g. Ref. 13]. Since experimental structure determination is time-consuming and expensive, the ability to computationally build accurate models of antibody structures (in particular their antigen-binding sites) from their sequences is highly desirable. This has become even more important as next-generation sequencing (NGS) data for antibodies has become available [1, 19]
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