Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.
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The authors are grateful to all the members of the Sapienza University of Rome Biocomputing Group for their help and assistance. We are thankful to the King Abdullah University of Science and Technology (KAUST) for financial support.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 RMSD value versus sequence similarity after cross-validation of the PIGS dataset.
The Cα atoms RMSD value for each of the 689 antibody structures in the PIGS dataset with the corresponding models obtained using a leave-one-out procedure is plotted against target–template sequence similarity.
Supplementary Figure 2 RMSD values for all the hypervariable loops except H3 after cross-validation of the PIGS dataset.
Distribution of the Cα RMSD calculated on all the hypervariable loops except H3 for the 689 antibody structures in the PIGS dataset with the corresponding models obtained using a leave-one-out procedure.
Distribution of the Cα RMSD calculated on loop H3 alone for the 689 antibody structures in the PIGS dataset with the corresponding models obtained using a leave-one-out procedure.
Supplementary Figure 4 Superimposition of the models and the experimentally solved structure of 13PL antibody obtained following two different modeling strategies.
The cartoon representations of the hypervariable loops of the heavy (H) and light (L) chains obtained using the Same antibody and the Same canonical structure options are respectively blue- and red-colored. The solved structure is shown in grey. The figure was produced with PyMOL (The PyMOL Molecular Graphics System, Version 220.127.116.11 Schrödinger, LLC. http://www.pymol.org/).
a) The top templates obtained for monoclonal antibody ED-10 before the manual alignment; b) Automatic alignment generated for the heavy chain sequence; c) Manual alignment for the heavy chain; d) the top templates obtained for monoclonal antibody ED-10 after the manual alignment.
RMSD value versus sequence similarity after cross-validation of the PIGS dataset. (PDF 413 kb)
RMSD values for all the hypervariable loops except H3 after cross-validation of the PIGS dataset. (PDF 272 kb)
RMSD values for loop H3 alone after cross-validation of the PIGS dataset. (PDF 271 kb)
Superimposition of the models and the experimentally solved structure of 13PL antibody obtained following two different modeling strategies. (PDF 288 kb)
Results of the modeling of the structure of antibody ED-10. (PDF 2355 kb)
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Marcatili, P., Olimpieri, P., Chailyan, A. et al. Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server. Nat Protoc 9, 2771–2783 (2014). https://doi.org/10.1038/nprot.2014.189
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