Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

Handle URI:
http://hdl.handle.net/10754/597587
Title:
Antibody structural modeling with prediction of immunoglobulin structure (PIGS)
Authors:
Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna; Tramontano, Anna
Abstract:
© 2014 Nature America, Inc. All rights reserved. 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.
Citation:
Marcatili P, Olimpieri PP, Chailyan A, Tramontano A (2014) Antibody structural modeling with prediction of immunoglobulin structure (PIGS). Nature Protocols 9: 2771–2783. Available: http://dx.doi.org/10.1038/nprot.2014.189.
Publisher:
Springer Nature
Journal:
Nature Protocols
Issue Date:
6-Nov-2014
DOI:
10.1038/nprot.2014.189
PubMed ID:
25375991
Type:
Article
ISSN:
1754-2189; 1750-2799
Sponsors:
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.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMarcatili, Paoloen
dc.contributor.authorOlimpieri, Pier Paoloen
dc.contributor.authorChailyan, Annaen
dc.contributor.authorTramontano, Annaen
dc.date.accessioned2016-02-25T12:42:36Zen
dc.date.available2016-02-25T12:42:36Zen
dc.date.issued2014-11-06en
dc.identifier.citationMarcatili P, Olimpieri PP, Chailyan A, Tramontano A (2014) Antibody structural modeling with prediction of immunoglobulin structure (PIGS). Nature Protocols 9: 2771–2783. Available: http://dx.doi.org/10.1038/nprot.2014.189.en
dc.identifier.issn1754-2189en
dc.identifier.issn1750-2799en
dc.identifier.pmid25375991en
dc.identifier.doi10.1038/nprot.2014.189en
dc.identifier.urihttp://hdl.handle.net/10754/597587en
dc.description.abstract© 2014 Nature America, Inc. All rights reserved. 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.en
dc.description.sponsorshipThe 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.en
dc.publisherSpringer Natureen
dc.titleAntibody structural modeling with prediction of immunoglobulin structure (PIGS)en
dc.typeArticleen
dc.identifier.journalNature Protocolsen
dc.contributor.institutionUniversita degli Studi di Roma La Sapienza, Rome, Italyen
dc.contributor.institutionDanmarks Tekniske Universitet, Lyngby, Denmarken
dc.contributor.institutionSyddansk Universitet, Odense, Denmarken

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