7 • Antibody Structure-Function 181

The conformational stability of an antibody not only depends on intramolecular interactions but also on interactions between the protein and its surrounding solvent. Formulation is one way to stabilize antibodies and prevent them from aggregation or degradation (Narayanan etal., 2021b; Strickley & Lambert, 2021). Antibodies have also been engineered to have binding afnity conditional to changes in pH. Two applications exist: either binding is strengthened at mildly acidic pH relative to neutral pH, or high afnity is maintained at neutral pH but lowered at acidic pH. Antibodies engineered to have stronger binding at acidic pH are more selective for acidied environments, such as the tumor microenvironment (Johnston etal., 2019). In contrast, lower afnity at acidic pH increases the release of antigen during the endosome within the antibody recycling pathway, which can confer more than one neutralization cycle per antibody (Igawa etal., 2013). As the histidine amino acid residue naturally experiences a change in charge within the biological pH range relevant to these applications, it has been used for engineering pH switches into antibody CDR loops using in vitro display technolo‑ gies (Schröter etal., 2015). pH‑dependent structural changes that affect antibody bio‑ physical properties have been reported. A histidine located in the antibody light chain was presumed to be important in the trans‑isomerization of a proline residue in the CDRH3loop of an anti‑HIV antibody. A comparison of structures determined in neu‑ tral and acidic pH environments revealed that the lower afnity was due to a change inCDRH3 conformation at acidic pH (Masiero etal., 2020). Additionally, the change in the environment of a pair of aspartate residues was hypothesized as being important in affecting the CDRH3loop dynamics of a therapeutic antibody (Lan etal., 2020) at dif‑ ferent pHs, although the effect on afnity at acidic pH wasn’t measured. Determinants outside of the paratope that affect CDRH3 conformational dynamics may be a consid‑ eration in the design of antibodies with pH‑dependent binding properties, in addition to the epitope–paratope interface.

Mutational scanning is transforming the prediction of binding afnity and stability for the ag‑ab complexes. It helps generate a large amount of data, which leads to more accurate AI prediction. Assessing all the mutations at once also accelerates the analysis of the interactions for the protein–protein complex. Mutational scanning could be done by both computational and experimental methods. The experimental deep mutational scanning study by Greaney etal. assessed all possible single amino acid variants in the spike protein of SARS‑CoV‑2 and provided immune escape maps for mutations in the presence of antibodies (Greaney etal., 2021). Computationally scanning the antibody– antigen interface residues for all 20 amino acid mutations for change in binding afnity has led to the identication of antibody escape mutations for the SARS‑CoV‑2 spike protein (Greaney etal., 2021; Sharma etal., 2022). Moreover, mutations that increase the binding afnity and stability of the SARS‑CoV‑2 to the human cell receptor ACE‑2 could be identied by mutational scanning (Teng etal., 2021). Furthermore, analyzing each residue for all the amino acid mutations can help identify its role in the binding and stability of the complex and assist in deriving physiological mechanisms. It will assist in antibody engineering, leading to better antibody design, which in turn may lead to the development of better therapeutics for diseases such as COVID‑19 and cancer.

In this chapter, we have summarized the databases for the antibody sequence and structure; different tools for predicting antibody–antigen complex structure and bind‑ ing afnity; LMs; and MD simulations for antibody structure‑function prediction. The recent computational resources for the above‑mentioned tasks have increasingly used ML or DL approaches and have shown much better performance than the conventional methods (Wilman et al., 2022). However, the limited availability of data has been a major challenge for the robustness, transferability, and generalization of such mod‑ els. To counter these challenges, especially in the case of antibodies, the paradigm is shifting toward: (i) either developing models on large‑scale synthetic data, which can represent real‑world data (Robert etal., 2022) or (ii) training models on large‑scale protein–protein interaction data and utilizing transfer learning for antibody–antigen interaction data (Pittala & Bailey‑Kellogg, 2020). Recently, signicant advances in the eld of protein/antibody structure prediction have opened the possibility of reimagin‑ ing epitope/paratope prediction. However, several challenges, including cross‑reactivity, polyspecicity, and sensitivity toward minor changes in sequence/structure/pH, are yet to be answered. Understanding the complex rules of antigen and antibody interactions can be the starting point for the end‑to‑end dry lab‑based early‑stage development of antibody therapeutics.

in‑silico proteins binding afnity prediction using sequence information. BioData Mining, 13(1), 20.

Akbar, R., Bashour, H., Rawat, P., Robert, P. A., Smorodina, E., Cotet, T.‑S., Flem‑Karlsen, K.,

An, J., & Weng, X. (2022). Collectively encoding protein properties enriches protein language

models. BMC Bioinformatics, 23(1), 467.

Audie, J., & Scarlata, S. (2007). A novel empirical free energy function that explains and predicts

protein‑protein binding afnities. Biophysical Chemistry, 129(2–3), 198–211.

Bachas, S., Rakocevic, G., Spencer, D., Sastry, A. V., Haile, R., Sutton, J. M., Kasun, G., Stachyra,

7 • Antibody Structure-Function 187