A window into the human immune system: comprehensive characterization of the complexity of antibody complementary-determining regions in functional antibodies

The human immune system uses antibodies to neutralize foreign antigens. They are composed of heavy and light chains, both with constant and variable regions. The variable region has six hypervariable loops, also known as complementary-determining regions (CDRs) that determine antibody diversity and antigen specificity. Knowledge of their significance, and certain residues present in these areas, is vital for antibody therapeutics development. This study includes an analysis of more than 11,000 human antibody sequences from the International Immunogenetics information system (IMGT). The analysis included parameters such as length distribution, overall amino acid diversity, amino acid frequency per CDR and residue position within antibody chains. Overall, our findings confirm existing knowledge, such as CDRH3's high length diversity and amino acid variability, increased aromatic residue usage, particularly tyrosine, charged and polar residues like aspartic acid, serine, and the flexible residue glycine. Specific residue positions within each CDR influence these occurrences, implying a unique amino acid type distribution pattern. We compared amino acid type usage in CDRs and non-CDR regions, both in globular and transmembrane proteins, which revealed distinguishing features, such as increased frequency of tyrosine, serine, aspartic acid, and arginine. These findings should prove useful for future optimization, improvement of affinity, synthetic antibody library design, or the creation of antibodies de-novo in silico.

Computational docking of antibody-antigen complexes, opportunities and pitfalls illustrated by influenza hemagglutinin

Antibodies play an increasingly important role in both basic research and the pharmaceutical industry. Since their efficiency depends, in ultimate analysis, on their atomic interactions with an antigen, studying such interactions is important to understand how they function and, in the long run, to design new molecules with desired properties. Computational docking, the process of predicting the conformation of a complex from its separated components, is emerging as a fast and affordable technique for the structural characterization of antibody-antigen complexes. In this manuscript, we first describe the different computational strategies for the modeling of antibodies and docking of their complexes, and then predict the binding of two antibodies to the stalk region of influenza hemagglutinin, an important pharmaceutical target. The purpose is two-fold: on a general note, we want to illustrate the advantages and pitfalls of computational docking with a practical example, using different approaches and comparing the results to known experimental structures. On a more specific note, we want to assess if docking can be successful in characterizing the binding to the same influenza epitope of other antibodies with unknown structure, which has practical relevance for pharmaceutical and biological research. The paper clearly shows that some of the computational docking predictions can be very accurate, but the algorithm often fails to discriminate them from inaccurate solutions. It is of paramount importance, therefore, to use rapidly obtained experimental data to validate the computational results.