Literature DB >> 25641019

Prediction of VH-VL domain orientation for antibody variable domain modeling.

Alexander Bujotzek1, James Dunbar, Florian Lipsmeier, Wolfgang Schäfer, Iris Antes, Charlotte M Deane, Guy Georges.   

Abstract

The antigen-binding site of antibodies forms at the interface of their two variable domains, VH and VL, making VH-VL domain orientation a factor that codetermines antibody specificity and affinity. Preserving VH-VL domain orientation in the process of antibody engineering is important in order to retain the original antibody properties, and predicting the correct VH-VL orientation has also been recognized as an important factor in antibody homology modeling. In this article, we present a fast sequence-based predictor that predicts VH-VL domain orientation with Q(2) values ranging from 0.54 to 0.73 on the evaluation set. We describe VH-VL orientation in terms of the six absolute ABangle parameters that have recently been proposed as a means to separate the different degrees of freedom of VH-VL domain orientation. In order to assess the impact of adjusting VH-VL orientation according to our predictions, we use the set of antibody structures of the recently published Antibody Modeling Assessment (AMA) II study. In comparison to the original AMAII homology models, we find an improvement in the accuracy of VH-VL orientation modeling, which also translates into an improvement in the average root-mean-square deviation with regard to the crystal structures.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  antibody structure; homology modeling; protein domain orientation; protein engineering

Mesh:

Substances:

Year:  2015        PMID: 25641019     DOI: 10.1002/prot.24756

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  27 in total

1.  VH-VL orientation prediction for antibody humanization candidate selection: A case study.

Authors:  Alexander Bujotzek; Florian Lipsmeier; Seth F Harris; Jörg Benz; Andreas Kuglstatter; Guy Georges
Journal:  MAbs       Date:  2016       Impact factor: 5.857

2.  MoFvAb: Modeling the Fv region of antibodies.

Authors:  Alexander Bujotzek; Angelika Fuchs; Changtao Qu; Jörg Benz; Stefan Klostermann; Iris Antes; Guy Georges
Journal:  MAbs       Date:  2015       Impact factor: 5.857

3.  Improved prediction of antibody VL-VH orientation.

Authors:  Nicholas A Marze; Sergey Lyskov; Jeffrey J Gray
Journal:  Protein Eng Des Sel       Date:  2016-06-08       Impact factor: 1.650

4.  Development of tibulizumab, a tetravalent bispecific antibody targeting BAFF and IL-17A for the treatment of autoimmune disease.

Authors:  Robert J Benschop; Chi-Kin Chow; Yu Tian; James Nelson; Barbra Barmettler; Shane Atwell; David Clawson; Qing Chai; Bryan Jones; Jon Fitchett; Stacy Torgerson; Yan Ji; Holly Bina; Ningjie Hu; Mahmoud Ghanem; Joseph Manetta; Victor J Wroblewski; Jirong Lu; Barrett W Allan
Journal:  MAbs       Date:  2019-06-10       Impact factor: 5.857

5.  Accurate Structure Prediction of CDR H3 Loops Enabled by a Novel Structure-Based C-Terminal Constraint.

Authors:  Brian D Weitzner; Jeffrey J Gray
Journal:  J Immunol       Date:  2016-11-21       Impact factor: 5.422

Review 6.  How repertoire data are changing antibody science.

Authors:  Claire Marks; Charlotte M Deane
Journal:  J Biol Chem       Date:  2020-05-14       Impact factor: 5.157

7.  Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Authors:  Louis Papageorgiou; Dimitris Maroulis; George P Chrousos; Elias Eliopoulos; Dimitrios Vlachakis
Journal:  Adv Exp Med Biol       Date:  2020       Impact factor: 2.622

8.  Modeling and docking of antibody structures with Rosetta.

Authors:  Brian D Weitzner; Jeliazko R Jeliazkov; Sergey Lyskov; Nicholas Marze; Daisuke Kuroda; Rahel Frick; Jared Adolf-Bryfogle; Naireeta Biswas; Roland L Dunbrack; Jeffrey J Gray
Journal:  Nat Protoc       Date:  2017-01-26       Impact factor: 13.491

9.  Immune repertoire mining for rapid affinity optimization of mouse monoclonal antibodies.

Authors:  Yi-Chun Hsiao; Yonglei Shang; Danielle M DiCara; Angie Yee; Joyce Lai; Si Hyun Kim; Diego Ellerman; Racquel Corpuz; Yongmei Chen; Sharmila Rajan; Hao Cai; Yan Wu; Dhaya Seshasayee; Isidro Hötzel
Journal:  MAbs       Date:  2019-03-22       Impact factor: 5.857

10.  MIB-MIP is a mycoplasma system that captures and cleaves immunoglobulin G.

Authors:  Yonathan Arfi; Laetitia Minder; Carmelo Di Primo; Aline Le Roy; Christine Ebel; Laurent Coquet; Stephane Claverol; Sanjay Vashee; Joerg Jores; Alain Blanchard; Pascal Sirand-Pugnet
Journal:  Proc Natl Acad Sci U S A       Date:  2016-04-25       Impact factor: 11.205

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.