Literature DB >> 24381156

Computational design of a pH-sensitive IgG binding protein.

Eva-Maria Strauch1, Sarel J Fleishman, David Baker.   

Abstract

Computational design provides the opportunity to program protein-protein interactions for desired applications. We used de novo protein interface design to generate a pH-dependent Fc domain binding protein that buries immunoglobulin G (IgG) His-433. Using next-generation sequencing of naïve and selected pools of a library of design variants, we generated a molecular footprint of the designed binding surface, confirming the binding mode and guiding further optimization of the balance between affinity and pH sensitivity. In biolayer interferometry experiments, the optimized design binds IgG with a Kd of ∼ 4 nM at pH 8.2, and approximately 500-fold more weakly at pH 5.5. The protein is extremely stable, heat-resistant and highly expressed in bacteria, and allows pH-based control of binding for IgG affinity purification and diagnostic devices.

Entities:  

Keywords:  antibody purification; computational interface design; pH-sensitivity

Mesh:

Substances:

Year:  2013        PMID: 24381156      PMCID: PMC3896196          DOI: 10.1073/pnas.1313605111

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  28 in total

1.  The thermal stability of immunoglobulin: unfolding and aggregation of a multi-domain protein.

Authors:  A W Vermeer; W Norde
Journal:  Biophys J       Date:  2000-01       Impact factor: 4.033

2.  Electrostatics of nanosystems: application to microtubules and the ribosome.

Authors:  N A Baker; D Sept; S Joseph; M J Holst; J A McCammon
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3.  Crystal structure at 2.8 A of an FcRn/heterodimeric Fc complex: mechanism of pH-dependent binding.

Authors:  W L Martin; A P West; L Gan; P J Bjorkman
Journal:  Mol Cell       Date:  2001-04       Impact factor: 17.970

4.  Design of a novel globular protein fold with atomic-level accuracy.

Authors:  Brian Kuhlman; Gautam Dantas; Gregory C Ireton; Gabriele Varani; Barry L Stoddard; David Baker
Journal:  Science       Date:  2003-11-21       Impact factor: 47.728

5.  Elution of antibodies from a Protein-A column by aqueous arginine solutions.

Authors:  Tsutomu Arakawa; John S Philo; Kouhei Tsumoto; Ryosuke Yumioka; Daisuke Ejima
Journal:  Protein Expr Purif       Date:  2004-08       Impact factor: 1.650

6.  Crystal structure and mechanism of catalysis of a pyrazinamidase from Pyrococcus horikoshii.

Authors:  X Du; W Wang; R Kim; H Yakota; H Nguyen; S H Kim
Journal:  Biochemistry       Date:  2001-11-27       Impact factor: 3.162

7.  Computational design of proteins targeting the conserved stem region of influenza hemagglutinin.

Authors:  Sarel J Fleishman; Timothy A Whitehead; Damian C Ekiert; Cyrille Dreyfus; Jacob E Corn; Eva-Maria Strauch; Ian A Wilson; David Baker
Journal:  Science       Date:  2011-05-13       Impact factor: 47.728

8.  Shape complementarity at protein/protein interfaces.

Authors:  M C Lawrence; P M Colman
Journal:  J Mol Biol       Date:  1993-12-20       Impact factor: 5.469

9.  pH on-off switching of antibody-hapten binding by site-specific chemical modification of tyrosine.

Authors:  D S Tawfik; R Chap; Z Eshhar; B S Green
Journal:  Protein Eng       Date:  1994-03

10.  Variability in the pKa of histidine side-chains correlates with burial within proteins.

Authors:  Stephen P Edgcomb; Kenneth P Murphy
Journal:  Proteins       Date:  2002-10-01
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  31 in total

1.  High-throughput characterization of protein-protein interactions by reprogramming yeast mating.

Authors:  David Younger; Stephanie Berger; David Baker; Eric Klavins
Journal:  Proc Natl Acad Sci U S A       Date:  2017-10-31       Impact factor: 11.205

Review 2.  A designed repeat protein as an affinity capture reagent.

Authors:  Elizabeth B Speltz; Rebecca S H Brown; Holly S Hajare; Christian Schlieker; Lynne Regan
Journal:  Biochem Soc Trans       Date:  2015-10       Impact factor: 5.407

3.  Computational design of membrane proteins using RosettaMembrane.

Authors:  Amanda M Duran; Jens Meiler
Journal:  Protein Sci       Date:  2017-11-15       Impact factor: 6.725

Review 4.  Deep sequencing methods for protein engineering and design.

Authors:  Emily E Wrenbeck; Matthew S Faber; Timothy A Whitehead
Journal:  Curr Opin Struct Biol       Date:  2016-11-22       Impact factor: 6.809

5.  Applications for an engineered Protein-G variant with a pH controllable affinity to antibody fragments.

Authors:  Lucas J Bailey; Kimberly M Sheehy; Robert J Hoey; Zachary P Schaefer; Marcin Ura; Anthony A Kossiakoff
Journal:  J Immunol Methods       Date:  2014-10-22       Impact factor: 2.303

Review 6.  The neonatal Fc receptor, FcRn, as a target for drug delivery and therapy.

Authors:  Jonathan T Sockolosky; Francis C Szoka
Journal:  Adv Drug Deliv Rev       Date:  2015-02-19       Impact factor: 15.470

7.  De novo design of tunable, pH-driven conformational changes.

Authors:  Scott E Boyken; Mark A Benhaim; Florian Busch; Mengxuan Jia; Matthew J Bick; Heejun Choi; Jason C Klima; Zibo Chen; Carl Walkey; Alexander Mileant; Aniruddha Sahasrabuddhe; Kathy Y Wei; Edgar A Hodge; Sarah Byron; Alfredo Quijano-Rubio; Banumathi Sankaran; Neil P King; Jennifer Lippincott-Schwartz; Vicki H Wysocki; Kelly K Lee; David Baker
Journal:  Science       Date:  2019-05-17       Impact factor: 47.728

8.  Enriching Peptide Libraries for Binding Affinity and Specificity Through Computationally Directed Library Design.

Authors:  Glenna Wink Foight; T Scott Chen; Daniel Richman; Amy E Keating
Journal:  Methods Mol Biol       Date:  2017

Review 9.  Targeting FcRn for the modulation of antibody dynamics.

Authors:  E Sally Ward; Siva Charan Devanaboyina; Raimund J Ober
Journal:  Mol Immunol       Date:  2015-03-09       Impact factor: 4.407

10.  Isolation of a pH-Sensitive IgNAR Variable Domain from a Yeast-Displayed, Histidine-Doped Master Library.

Authors:  Doreen Könning; Stefan Zielonka; Carolin Sellmann; Christian Schröter; Julius Grzeschik; Stefan Becker; Harald Kolmar
Journal:  Mar Biotechnol (NY)       Date:  2016-02-02       Impact factor: 3.619

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