Literature DB >> 31958430

Engineering Stability, Viscosity, and Immunogenicity of Antibodies by Computational Design.

Daisuke Kuroda1, Kouhei Tsumoto2.   

Abstract

In recent years, computational methods have garnered much attention in protein engineering. A large number of computational methods have been developed to analyze the sequences and structures of proteins and have been used to predict the various properties. Antibodies are one of the emergent protein therapeutics, and thus, methods to control their physicochemical properties are highly desirable. However, despite the tremendous efforts of past decades, computational methods to predict the physicochemical properties of antibodies are still in their infancy. Experimental validations are certainly required for real-world applications, and the results should be interpreted with caution. Among the various properties of antibodies, we focus in this review on stability, viscosity, and immunogenicity, and we present the current status of computational methods to engineer such properties.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

Keywords:  antibody engineering; colloidal stability; computer-aided design; conformational stability; immunogenicity; machine learning; molecular simulations; viscosity

Mesh:

Substances:

Year:  2020        PMID: 31958430     DOI: 10.1016/j.xphs.2020.01.011

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


  7 in total

Review 1.  Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

Authors:  Rahmad Akbar; Habib Bashour; Puneet Rawat; Philippe A Robert; Eva Smorodina; Tudor-Stefan Cotet; Karine Flem-Karlsen; Robert Frank; Brij Bhushan Mehta; Mai Ha Vu; Talip Zengin; Jose Gutierrez-Marcos; Fridtjof Lund-Johansen; Jan Terje Andersen; Victor Greiff
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

2.  Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics.

Authors:  Pin-Kuang Lai; Austin Gallegos; Neil Mody; Hasige A Sathish; Bernhardt L Trout
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

3.  Delicate balance among thermal stability, binding affinity, and conformational space explored by single-domain VHH antibodies.

Authors:  Emina Ikeuchi; Daisuke Kuroda; Makoto Nakakido; Akikazu Murakami; Kouhei Tsumoto
Journal:  Sci Rep       Date:  2021-10-18       Impact factor: 4.379

Review 4.  Computational models for studying physical instabilities in high concentration biotherapeutic formulations.

Authors:  Marco A Blanco
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

5.  Large-scale application of free energy perturbation calculations for antibody design.

Authors:  Fangqiang Zhu; Feliza A Bourguet; William F D Bennett; Edmond Y Lau; Kathryn T Arrildt; Brent W Segelke; Adam T Zemla; Thomas A Desautels; Daniel M Faissol
Journal:  Sci Rep       Date:  2022-07-21       Impact factor: 4.996

Review 6.  Toward Drug-Like Multispecific Antibodies by Design.

Authors:  Manali S Sawant; Craig N Streu; Lina Wu; Peter M Tessier
Journal:  Int J Mol Sci       Date:  2020-10-12       Impact factor: 5.923

Review 7.  In silico prediction of post-translational modifications in therapeutic antibodies.

Authors:  Shabdita Vatsa
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

  7 in total

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