Literature DB >> 21789769

Aggregation in protein-based biotherapeutics: computational studies and tools to identify aggregation-prone regions.

Neeraj J Agrawal1, Sandeep Kumar, Xiaoling Wang, Bernhard Helk, Satish K Singh, Bernhardt L Trout.   

Abstract

Because of their large, complex, and conformationally heterogeneous structures, biotherapeutics are vulnerable to several physicochemical stresses faced during the various processing steps from production to administration. In particular, formation of protein aggregates is a major concern. The greatest risk with aggregates arises from their potential to give rise to immunogenic reactions. Hence, it is desirable to bring forward biotherapeutic drug candidates that show low propensity for aggregation and, thus, improved developability. Here, we present a comprehensive review of computational studies into the sequence and structural factors that underpin protein and peptide aggregation. A number of computational approaches have been applied including coarse grain models, atomistic molecular simulations, and bioinformatic approaches. These studies have focused on both the mechanism of aggregation and the identification of potential aggregation-prone sequence and structural motifs. We also survey the computational tools available to predict aggregation in therapeutic proteins. The findings communicated here provide insights that could be potentially useful in the rational design of therapeutic candidates with not only high potency and specificity but also improved stability and solubility. These sequence-structure-based approaches can be applied to both novel as well as follow-on biotherapeutics.
Copyright © 2011 Wiley-Liss, Inc.

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Year:  2011        PMID: 21789769     DOI: 10.1002/jps.22705

Source DB:  PubMed          Journal:  J Pharm Sci        ISSN: 0022-3549            Impact factor:   3.534


  43 in total

1.  Rational design of therapeutic mAbs against aggregation through protein engineering and incorporation of glycosylation motifs applied to bevacizumab.

Authors:  Fabienne Courtois; Neeraj J Agrawal; Timothy M Lauer; Bernhardt L Trout
Journal:  MAbs       Date:  2016       Impact factor: 5.857

2.  Reduction of Nonspecificity Motifs in Synthetic Antibody Libraries.

Authors:  Ryan L Kelly; Doris Le; Jessie Zhao; K Dane Wittrup
Journal:  J Mol Biol       Date:  2017-11-26       Impact factor: 5.469

Review 3.  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

4.  In vitro and in silico assessment of the developability of a designed monoclonal antibody library.

Authors:  Adriana-Michelle Wolf Pérez; Pietro Sormanni; Jonathan Sonne Andersen; Laila Ismail Sakhnini; Ileana Rodriguez-Leon; Jais Rose Bjelke; Annette Juhl Gajhede; Leonardo De Maria; Daniel E Otzen; Michele Vendruscolo; Nikolai Lorenzen
Journal:  MAbs       Date:  2019-01-18       Impact factor: 5.857

5.  Usage of a dataset of NMR resolved protein structures to test aggregation versus solubility prediction algorithms.

Authors:  Daniel B Roche; Etienne Villain; Andrey V Kajava
Journal:  Protein Sci       Date:  2017-07-15       Impact factor: 6.725

6.  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

7.  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

8.  Fibpredictor: a computational method for rapid prediction of amyloid fibril structures.

Authors:  Hamed Tabatabaei Ghomi; Elizabeth M Topp; Markus A Lill
Journal:  J Mol Model       Date:  2016-08-08       Impact factor: 1.810

9.  Coarse-grained model for colloidal protein interactions, B(22), and protein cluster formation.

Authors:  Marco A Blanco; Erinc Sahin; Anne S Robinson; Christopher J Roberts
Journal:  J Phys Chem B       Date:  2013-12-10       Impact factor: 2.991

10.  Assessment of physical stability of an antibody drug conjugate by higher order structure analysis: impact of thiol- maleimide chemistry.

Authors:  Jianxin Guo; Sandeep Kumar; Amarnauth Prashad; Jason Starkey; Satish K Singh
Journal:  Pharm Res       Date:  2014-01-24       Impact factor: 4.200

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