Antibodies: Computer-Aided Prediction of Structure and Design of Function
- Authors: Alexander M. Sevy1, Jens Meiler2
- Editors: James E. Crowe Jr.3, Diana Boraschi4, Rino Rappuoli5
-
VIEW AFFILIATIONS HIDE AFFILIATIONSAffiliations: 1: Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37212; 2: Department of Chemistry and Center for Structural Biology, Vanderbilt University, Nashville, TN 37212; 3: Vanderbilt University School of Medicine, Nashville, TN; 4: National Research Council, Pisa, Italy; 5: Novartis Vaccines, Siena, Italy
-
Received 10 September 2014 Accepted 17 September 2014 Published 19 December 2014
- Correspondence: Jens Meiler, [email protected]

-
Abstract:
With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that—as a knowledge base—can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.
-
Citation: Sevy A, Meiler J. 2014. Antibodies: Computer-Aided Prediction of Structure and Design of Function. Microbiol Spectrum 2(6):AID-0024-2014. doi:10.1128/microbiolspec.AID-0024-2014.




Antibodies: Computer-Aided Prediction of Structure and Design of Function, Page 1 of 2
< Previous page | Next page > /docserver/preview/fulltext/microbiolspec/2/6/AID-0024-2014-1.gif /docserver/preview/fulltext/microbiolspec/2/6/AID-0024-2014-2.gif

References

Article metrics loading...
Abstract:
With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that—as a knowledge base—can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.

Full text loading...
Figures

Click to view
FIGURE 1
Challenges in antibody modeling. Though all antibodies share a common core structure (center panel, PDB ID 1IGT [ 1 ]; 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 in silico 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).

Click to view
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.

Click to view
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.

Click to view
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 Journal of Clinical Investigation ( 33 ) with permission from the publisher.
Supplemental Material
No supplementary material available for this content.