The Experts below are selected from a list of 11796 Experts worldwide ranked by ideXlab platform
Anna Tramontano - One of the best experts on this subject based on the ideXlab platform.
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Erratum: Antibody modeling using the Prediction of Immunoglobulin Structure (PIGS) web server
Nature Protocols, 2015Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Nat. Protoc. 9, 2771–2783 (2014); doi:10.1038/nprot.2014.189; published online 6 November 2014; corrected online 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'.
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antibody modeling using the prediction of Immunoglobulin Structure pigs web server
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Predicting the Structure of antibodies on the basis of their sequences is a key objective in medical and biotechnology research. Marcatili et al. describe the use of their online system PIGS for the automated modeling of the 3D Structure of antibodies. 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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antibody structural modeling with prediction of Immunoglobulin Structure pigs
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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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antibody modeling using the prediction of Immunoglobulin Structure pigs web server corrected
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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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PIGS: a resource for Immunoglobulin Structure prediction
2008Co-Authors: Paolo Marcatili, Alessandra Rossi, Anna TramontanoAbstract:Pigs (http://arianna.bio.uniroma1.it/pigs) is a web server for the automatic modeling of Immunoglobulin variable domains based on the canonical Structure method. It has a user-friendly and flexible interface, that allows the user to choose templates (for the frameworks and the loops) and modeling strategies in an automatic or manual fashion. Its final output is a complete three-dimensional model of the target antibody that can be downloaded or displayed on-line. The server is freely accessible to academic users, with no restriction on the number of submitted sequences.
Paolo Marcatili - One of the best experts on this subject based on the ideXlab platform.
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Erratum: Antibody modeling using the Prediction of Immunoglobulin Structure (PIGS) web server
Nature Protocols, 2015Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Nat. Protoc. 9, 2771–2783 (2014); doi:10.1038/nprot.2014.189; published online 6 November 2014; corrected online 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'.
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antibody modeling using the prediction of Immunoglobulin Structure pigs web server
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Predicting the Structure of antibodies on the basis of their sequences is a key objective in medical and biotechnology research. Marcatili et al. describe the use of their online system PIGS for the automated modeling of the 3D Structure of antibodies. 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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antibody structural modeling with prediction of Immunoglobulin Structure pigs
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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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antibody modeling using the prediction of Immunoglobulin Structure pigs web server corrected
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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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PIGS: a resource for Immunoglobulin Structure prediction
2008Co-Authors: Paolo Marcatili, Alessandra Rossi, Anna TramontanoAbstract:Pigs (http://arianna.bio.uniroma1.it/pigs) is a web server for the automatic modeling of Immunoglobulin variable domains based on the canonical Structure method. It has a user-friendly and flexible interface, that allows the user to choose templates (for the frameworks and the loops) and modeling strategies in an automatic or manual fashion. Its final output is a complete three-dimensional model of the target antibody that can be downloaded or displayed on-line. The server is freely accessible to academic users, with no restriction on the number of submitted sequences.
Anna Chailyan - One of the best experts on this subject based on the ideXlab platform.
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Erratum: Antibody modeling using the Prediction of Immunoglobulin Structure (PIGS) web server
Nature Protocols, 2015Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Nat. Protoc. 9, 2771–2783 (2014); doi:10.1038/nprot.2014.189; published online 6 November 2014; corrected online 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'.
-
antibody modeling using the prediction of Immunoglobulin Structure pigs web server
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Predicting the Structure of antibodies on the basis of their sequences is a key objective in medical and biotechnology research. Marcatili et al. describe the use of their online system PIGS for the automated modeling of the 3D Structure of antibodies. 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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antibody structural modeling with prediction of Immunoglobulin Structure pigs
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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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antibody modeling using the prediction of Immunoglobulin Structure pigs web server corrected
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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.
Pier Paolo Olimpieri - One of the best experts on this subject based on the ideXlab platform.
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Erratum: Antibody modeling using the Prediction of Immunoglobulin Structure (PIGS) web server
Nature Protocols, 2015Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Nat. Protoc. 9, 2771–2783 (2014); doi:10.1038/nprot.2014.189; published online 6 November 2014; corrected online 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'.
-
antibody modeling using the prediction of Immunoglobulin Structure pigs web server
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract:Predicting the Structure of antibodies on the basis of their sequences is a key objective in medical and biotechnology research. Marcatili et al. describe the use of their online system PIGS for the automated modeling of the 3D Structure of antibodies. 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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antibody structural modeling with prediction of Immunoglobulin Structure pigs
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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.
-
antibody modeling using the prediction of Immunoglobulin Structure pigs web server corrected
Nature Protocols, 2014Co-Authors: Paolo Marcatili, Pier Paolo Olimpieri, Anna Chailyan, Anna TramontanoAbstract: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.
Roald Nezlin - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Aspects of the Immunoglobulin Structure.
Immunological investigations, 2019Co-Authors: Roald NezlinAbstract:ABSTRACTImmunoglobulin (Ig) molecules are composed of Fab and Fc portions tethered by a hinge region that enables them to rotate and flex, relative to each other. Variable (V) and constant (C) doma...
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The Immunoglobulins - CHAPTER 1 – General Characteristics of Immunoglobulin Molecules
The Immunoglobulins, 1998Co-Authors: Roald NezlinAbstract:This chapter describes the general characteristics and Structure of Immunoglobulin (Ig) molecules. All Immunoglobulin molecules are heterodimers and composed from four polypeptide chains—two identical large or heavy (H) chains of about 50-60 kDa and two small or light (L) chains of about 23 kDa, linked by interchain disulfide bridges. All Immunoglobulin molecules are glycoproteins and contain two or more carbohydrate chains, usually linked to heavy chains. In humans and rodents, there are five Immunoglobulin classes or isotypes, which differ in the primary Structure, carbohydrate content, and antigenic properties of their heavy chains: Immunoglobulin G (IgG), IgA, IgM, IgD, and IgM. By contrast, the light chain types are the same for all Immunoglobulin classes. Each Immunoglobulin molecule contains light chains of one of two types, either lambda (λ) or kappa (κ). The chapter explains the characteristics of the five classes of Immunoglobulins. It also discusses the studies on Immunoglobulin Structure and localization. Immunoglobulin molecules can be digested under mild conditions by proteolytic enzymes, such as papain, trypsin, and pepsin of different origin into large fragments.
