Literature DB >> 22694284

Coarse-grained modeling of the self-association of therapeutic monoclonal antibodies.

Anuj Chaudhri1, Isidro E Zarraga, Tim J Kamerzell, J Paul Brandt, Thomas W Patapoff, Steven J Shire, Gregory A Voth.   

Abstract

Coarse-grained computational models of two therapeutic monoclonal antibodies are constructed to understand the effect of domain-level charge-charge electrostatics on the self-association phenomena at high protein concentrations. The coarse-grained representations of the individual antibodies are constructed using an elastic network normal-mode analysis. Two different models are constructed for each antibody for a compact Y-shaped and an extended Y-shaped configuration. The resulting simulations of these coarse-grained antibodies that interact through screened electrostatics are done at six different concentrations. It is observed that a particular monoclonal antibody (hereafter referred to as MAb1) forms three-dimensional heterogeneous structures with dense regions or clusters compared to a different monoclonal antibody (hereafter referred to as MAb2) that forms more homogeneous structures (no clusters). These structures, together with the potential mean force (PMF) and radial distribution functions (RDF) between pairs of coarse-grained regions on the MAbs, are qualitatively consistent with the experimental observation that MAb1 has a significantly higher viscosity compared to MAb2, especially at concentrations >50 mg/mL, even though the only difference between the MAbs lies with a few amino acids at the antigen-binding loops (CDRs). It is also observed that the structures in MAb1 are formed due to stronger Fab-Fab interactions in corroboration with experimental observations. Evidence is also shown that Fab-Fc interactions can be equally important in addition to Fab-Fab interactions. The coarse-grained representations are effective in picking up differences based on local charge distributions of domains and make predictions on the self-association characteristics of these protein solutions. This is the first computational study of its kind to show that there are differences in structures formed by two different monoclonal antibodies at high concentrations.

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Year:  2012        PMID: 22694284     DOI: 10.1021/jp301140u

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  23 in total

Review 1.  Molecular basis of high viscosity in concentrated antibody solutions: Strategies for high concentration drug product development.

Authors:  Dheeraj S Tomar; Sandeep Kumar; Satish K Singh; Sumit Goswami; Li Li
Journal:  MAbs       Date:  2016-01-06       Impact factor: 5.857

2.  Small-angle neutron scattering characterization of monoclonal antibody conformations and interactions at high concentrations.

Authors:  Eric J Yearley; Isidro E Zarraga; Steven J Shire; Thomas M Scherer; Yatin Gokarn; Norman J Wagner; Yun Liu
Journal:  Biophys J       Date:  2013-08-06       Impact factor: 4.033

3.  Boosting antibody developability through rational sequence optimization.

Authors:  Daniel Seeliger; Patrick Schulz; Tobias Litzenburger; Julia Spitz; Stefan Hoerer; Michaela Blech; Barbara Enenkel; Joey M Studts; Patrick Garidel; Anne R Karow
Journal:  MAbs       Date:  2015       Impact factor: 5.857

4.  Process optimization and protein engineering mitigated manufacturing challenges of a monoclonal antibody with liquid-liquid phase separation issue by disrupting inter-molecule electrostatic interactions.

Authors:  Qun Du; Melissa Damschroder; Timothy M Pabst; Alan K Hunter; William K Wang; Haibin Luo
Journal:  MAbs       Date:  2019-04-14       Impact factor: 5.857

Review 5.  Structure, heterogeneity and developability assessment of therapeutic antibodies.

Authors:  Yingda Xu; Dongdong Wang; Bruce Mason; Tony Rossomando; Ning Li; Dingjiang Liu; Jason K Cheung; Wei Xu; Smita Raghava; Amit Katiyar; Christine Nowak; Tao Xiang; Diane D Dong; Joanne Sun; Alain Beck; Hongcheng Liu
Journal:  MAbs       Date:  2018-12-17       Impact factor: 5.857

6.  Contrasting the Influence of Cationic Amino Acids on the Viscosity and Stability of a Highly Concentrated Monoclonal Antibody.

Authors:  Barton J Dear; Jessica J Hung; Thomas M Truskett; Keith P Johnston
Journal:  Pharm Res       Date:  2016-11-11       Impact factor: 4.200

7.  In Silico Prediction of Diffusion Interaction Parameter (kD), a Key Indicator of Antibody Solution Behaviors.

Authors:  Dheeraj S Tomar; Satish K Singh; Li Li; Matthew P Broulidakis; Sandeep Kumar
Journal:  Pharm Res       Date:  2018-08-20       Impact factor: 4.200

8.  Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior in terms of molecular properties.

Authors:  Li Li; Sandeep Kumar; Patrick M Buck; Christopher Burns; Janelle Lavoie; Satish K Singh; Nicholas W Warne; Pilarin Nichols; Nicholas Luksha; Davin Boardman
Journal:  Pharm Res       Date:  2014-06-07       Impact factor: 4.200

Review 9.  Assessment and significance of protein-protein interactions during development of protein biopharmaceuticals.

Authors:  Sandeep Yadav; Jun Liu; Thomas M Scherer; Yatin Gokarn; Barthélemy Demeule; Sonoko Kanai; James D Andya; Steven J Shire
Journal:  Biophys Rev       Date:  2013-03-14

10.  Charge-mediated Fab-Fc interactions in an IgG1 antibody induce reversible self-association, cluster formation, and elevated viscosity.

Authors:  Jayant Arora; Yue Hu; Reza Esfandiary; Hasige A Sathish; Steven M Bishop; Sangeeta B Joshi; C Russell Middaugh; David B Volkin; David D Weis
Journal:  MAbs       Date:  2016-08-25       Impact factor: 5.857

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