Chapter 10 : Antibodies: Computer-Aided Prediction of Structure and Design of Function

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The central role antibodies play in our immune system makes them important targets for computation-based structural modeling. Antibodies consist of a “constant” and a “variable” region ( Fig. 1 ). The constant region is virtually identical in all antibodies of the same isotype, while the variable region differs from one B-cell-derived antibody to the next. The variable region of an antibody is the “business end,” the region that recognizes its antigen via so-called complementarity-determining regions (CDRs). Their large size (∼150 kDa) and inherent variability, in particular in the CDRs, make antibodies a formidable challenge for molecular modeling. Before we begin to model antibodies, it is useful to briefly review their overall structure.

Citation: Sevy A, Meiler J. 2015. Antibodies: Computer-Aided Prediction of Structure and Design of Function, p 173-190. In Crowe J, Boraschi D, Rappuoli R (ed), Antibodies for Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.AID-0024-2014
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Figure 1

Challenges in antibody modeling. Though all antibodies share a common core structure (center panel, PDB ID 1IGT [ ]; heavy chains in magenta, light chains in yellow), slight differences in variable regions and especially CDR loops can have a great effect on function. The vast sequence space generated by genetic recombination in V, D, and J genes (A) results in many different CDR loop conformations. Modeling of CDR loops from sequence information alone is a necessary computational task for accurate structure prediction (B). The ability to simulate the affinity maturation process is another important task that can be used to generate an antibody with either increased higher affinity for its native target, or for a completely novel target (C) (matured residues shown in cyan). Accurate antibody modeling requires not only the ability to model an antibody alone, but also the ability to model its interaction with a given antigen. Computational docking techniques achieve this by sampling different positions of an antibody on its target to find the most favorable position (D).

Citation: Sevy A, Meiler J. 2015. Antibodies: Computer-Aided Prediction of Structure and Design of Function, p 173-190. In Crowe J, Boraschi D, Rappuoli R (ed), Antibodies for Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.AID-0024-2014
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Figure 2

Exponential growth of structurally determined antibodies. Total antibody structures in the Protein Data Bank (PDB) are shown by year. The increase in structures enables more accurate computational approaches to antibody modeling and engineering.

Citation: Sevy A, Meiler J. 2015. Antibodies: Computer-Aided Prediction of Structure and Design of Function, p 173-190. In Crowe J, Boraschi D, Rappuoli R (ed), Antibodies for Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.AID-0024-2014
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Figure 3

Canonical CDR loop conformations. Pictured above are median loop structures representing the largest cluster of (A) CDR L1, (B) L2, (C) L3, (D) H1, and (E) H2. Light and heavy chain loop variability varies widely between the CDR loops, with heavy chain loops tending to be more variable.

Citation: Sevy A, Meiler J. 2015. Antibodies: Computer-Aided Prediction of Structure and Design of Function, p 173-190. In Crowe J, Boraschi D, Rappuoli R (ed), Antibodies for Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.AID-0024-2014
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Figure 4

Integrating experimental data to aid computational modeling. (A) Low-resolution cryo-EM maps, (A and B) combined with hydrogen-deuterium exchange (DXMS) data and site-directed mutagenesis, were used to generate a docked model of a potent anti-influenza antibody. Reprinted from ( ) with permission from the publisher.

Citation: Sevy A, Meiler J. 2015. Antibodies: Computer-Aided Prediction of Structure and Design of Function, p 173-190. In Crowe J, Boraschi D, Rappuoli R (ed), Antibodies for Infectious Diseases. ASM Press, Washington, DC. doi: 10.1128/microbiolspec.AID-0024-2014
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