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Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server

Nature Protocols volume 9, pages 2771–2783 (2014)Cite this article

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An Erratum to this article was published on 26 March 2015

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Abstract

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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Figure 1: Antibody structure.
Figure 2: Global r.m.s.d. distribution after cross-validation of the PIGS antibody structural data set.
Figure 3: Antigen-binding site r.
Figure 4: PIGS protocol workflow for predicting the structure of immunoglobulins.
Figure 5: Screenshot of the 'Template Selection' page and 'Target Alignment'.
Figure 6: The 13PL structural model obtained after implementation of the present protocol.

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  • 21 November 2014

     In the version of this article initially published online, the title was incorrect and was changed to read as follows: ‘Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server’. The error has been corrected for the PDF and HTML versions of this article.

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Acknowledgements

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.

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Author notes

  1. Paolo Marcatili & Anna Chailyan

    Present address: Present addresses: Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark (P.M.); Department of Biochemistry and Molecular Biology (BMB), University of Southern Denmark, Odense Denmark (A.C.).,

Authors and Affiliations

  1. Physics Department, Sapienza University of Rome, Piazzale Aldo Moro, Rome, Italy

    Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan & Anna Tramontano

  2. Istituto Pasteur – Fondazione Cenci Bolognetti, Sapienza University of Rome, Rome, Italy

    Anna Tramontano

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Contributions

A.T. conceived the study, participated in its design and coordination, and helped to draft the manuscript. P.M. developed the software. A.C. and P.P.O. contributed to the analysis and interpretation of data and to test the system. All authors participated in drafting the article and revising it critically.

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Correspondence to Anna Tramontano.

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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.

Supplementary Figure 3 RMSD values for loop H3 alone after cross-validation of the PIGS dataset.

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 1.5.0.4 Schrödinger, LLC. http://www.pymol.org/).

Supplementary Figure 5 Results of the modeling of the structure of antibody ED-10.

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.

Supplementary information

Supplementary Figure 1

RMSD value versus sequence similarity after cross-validation of the PIGS dataset. (PDF 413 kb)

Supplementary Figure 2

RMSD values for all the hypervariable loops except H3 after cross-validation of the PIGS dataset. (PDF 272 kb)

Supplementary Figure 3

RMSD values for loop H3 alone after cross-validation of the PIGS dataset. (PDF 271 kb)

Supplementary Figure 4

Superimposition of the models and the experimentally solved structure of 13PL antibody obtained following two different modeling strategies. (PDF 288 kb)

Supplementary Figure 5

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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