Literature DB >> 9820517

Improving the affinity and the fine specificity of an anti-cortisol antibody by parsimonious mutagenesis and phage display.

P Chames1, S Coulon, D Baty.   

Abstract

Immunoassays are widely used to determine steroid concentrations. However, they are limited by the specificity of antisteroid mAbs. We used the phage display system combined with molecular modeling and site-specific randomization to improve the affinity and the fine specificity of an anti-cortisol mAb. Using parsimonious mutagenesis, we have generated a library of mutant Ab fragments (scFv) derived from this Ab by randomizing five amino acids chosen by molecular modeling and Ab-hapten contact structural analysis. Anti-cortisol Ab fragments were selected from the library in the presence of steroid analogues to block cross-reacting binders. Specific elution with free cortisol allowed the recovery of clones with up to eightfold better affinity and fivefold less cross-reactivity than the wild-type scFv. This approach can be applied to any anti-hapten Ab and represents a useful approach for obtaining highly specific Abs for use in steroid immunoassays.

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Year:  1998        PMID: 9820517

Source DB:  PubMed          Journal:  J Immunol        ISSN: 0022-1767            Impact factor:   5.422


  12 in total

Review 1.  Molecular engineering of antibodies for therapeutic and diagnostic purposes.

Authors:  Frédéric Ducancel; Bruno H Muller
Journal:  MAbs       Date:  2012-07-01       Impact factor: 5.857

2.  A general method for greatly improving the affinity of antibodies by using combinatorial libraries.

Authors:  Arvind Rajpal; Nurten Beyaz; Lauric Haber; Guido Cappuccilli; Helena Yee; Ramesh R Bhatt; Toshihiko Takeuchi; Richard A Lerner; Roberto Crea
Journal:  Proc Natl Acad Sci U S A       Date:  2005-06-06       Impact factor: 11.205

3.  Quantitative specificity-based display library screening identifies determinants of antibody-epitope binding specificity.

Authors:  Sejal S Hall; Patrick S Daugherty
Journal:  Protein Sci       Date:  2009-09       Impact factor: 6.725

Review 4.  Engineering the variable region of therapeutic IgG antibodies.

Authors:  Tomoyuki Igawa; Hiroyuki Tsunoda; Taichi Kuramochi; Zenjiro Sampei; Shinya Ishii; Kunihiro Hattori
Journal:  MAbs       Date:  2011-05-01       Impact factor: 5.857

5.  Tuning the specificity of a Two-in-One Fab against three angiogenic antigens by fully utilizing the information of deep mutational scanning.

Authors:  Patrick Koenig; Sarah Sanowar; Chingwei V Lee; Germaine Fuh
Journal:  MAbs       Date:  2017-06-06       Impact factor: 5.857

6.  Beyond natural antibodies: the power of in vitro display technologies.

Authors:  Andrew R M Bradbury; Sachdev Sidhu; Stefan Dübel; John McCafferty
Journal:  Nat Biotechnol       Date:  2011-03       Impact factor: 54.908

7.  Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design.

Authors:  Daniel A Cannon; Lu Shan; Qun Du; Lena Shirinian; Keith W Rickert; Kim L Rosenthal; Martin Korade; Lilian E van Vlerken-Ysla; Andrew Buchanan; Tristan J Vaughan; Melissa M Damschroder; Bojana Popovic
Journal:  PLoS Comput Biol       Date:  2019-05-01       Impact factor: 4.475

8.  Human monoclonal antibodies broadly neutralizing against influenza B virus.

Authors:  Mayo Yasugi; Ritsuko Kubota-Koketsu; Akifumi Yamashita; Norihito Kawashita; Anariwa Du; Tadahiro Sasaki; Mitsuhiro Nishimura; Ryo Misaki; Motoki Kuhara; Naphatsawan Boonsathorn; Kazuhito Fujiyama; Yoshinobu Okuno; Takaaki Nakaya; Kazuyoshi Ikuta
Journal:  PLoS Pathog       Date:  2013-02-07       Impact factor: 6.823

9.  Antibody-mediated prevention of Fusarium mycotoxins in the field.

Authors:  Zu-Quan Hu; He-Ping Li; Jing-Bo Zhang; Elena Glinka; Yu-Cai Liao
Journal:  Int J Mol Sci       Date:  2008-10-09       Impact factor: 6.208

10.  Directed Evolution of a Highly Specific FN3 Monobody to the SH3 Domain of Human Lyn Tyrosine Kinase.

Authors:  Renhua Huang; Pete Fang; Zengping Hao; Brian K Kay
Journal:  PLoS One       Date:  2016-01-05       Impact factor: 3.240

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