Literature DB >> 30298157

Third generation antibody discovery methods: in silico rational design.

Pietro Sormanni1, Francesco A Aprile, Michele Vendruscolo.   

Abstract

Owing to their outstanding performances in molecular recognition, antibodies are extensively used in research and applications in molecular biology, biotechnology and medicine. Recent advances in experimental and computational methods are making it possible to complement well-established in vivo (first generation) and in vitro (second generation) methods of antibody discovery with novel in silico (third generation) approaches. Here we describe the principles of computational antibody design and review the state of the art in this field. We then present Modular, a method that implements the rational design of antibodies in a modular manner, and describe the opportunities offered by this approach.

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Year:  2018        PMID: 30298157     DOI: 10.1039/c8cs00523k

Source DB:  PubMed          Journal:  Chem Soc Rev        ISSN: 0306-0012            Impact factor:   54.564


  30 in total

1.  Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Authors:  Derek M Mason; Simon Friedensohn; Cédric R Weber; Christian Jordi; Bastian Wagner; Simon M Meng; Roy A Ehling; Lucia Bonati; Jan Dahinden; Pablo Gainza; Bruno E Correia; Sai T Reddy
Journal:  Nat Biomed Eng       Date:  2021-04-15       Impact factor: 25.671

2.  AbRSA: A robust tool for antibody numbering.

Authors:  Lei Li; Shuang Chen; Zhichao Miao; Yang Liu; Xu Liu; Zhi-Xiong Xiao; Yang Cao
Journal:  Protein Sci       Date:  2019-05-11       Impact factor: 6.725

3.  De novo discovery of antibody drugs - great promise demands scrutiny.

Authors:  William J J Finlay; Alexey A Lugovskoy
Journal:  MAbs       Date:  2019-06-06       Impact factor: 5.857

4.  Computational maturation of a single-domain antibody against Aβ42 aggregation.

Authors:  Jiacheng Lin; Chiara Figazzolo; Michael A Metrick; Pietro Sormanni; Michele Vendruscolo
Journal:  Chem Sci       Date:  2021-10-07       Impact factor: 9.825

Review 5.  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

6.  Systematic Activity Maturation of a Single-Domain Antibody with Non-canonical Amino Acids through Chemical Mutagenesis.

Authors:  Philip R Lindstedt; Francesco A Aprile; Pietro Sormanni; Robertinah Rakoto; Christopher M Dobson; Gonçalo J L Bernardes; Michele Vendruscolo
Journal:  Cell Chem Biol       Date:  2020-11-19       Impact factor: 8.116

Review 7.  Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics.

Authors:  Rahul Khetan; Robin Curtis; Charlotte M Deane; Johannes Thorling Hadsund; Uddipan Kar; Konrad Krawczyk; Daisuke Kuroda; Sarah A Robinson; Pietro Sormanni; Kouhei Tsumoto; Jim Warwicker; Andrew C R Martin
Journal:  MAbs       Date:  2022 Jan-Dec       Impact factor: 5.857

8.  Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods.

Authors:  Adriana-Michelle Wolf Pérez; Nikolai Lorenzen; Michele Vendruscolo; Pietro Sormanni
Journal:  Methods Mol Biol       Date:  2022

9.  Rationally Designed Bicyclic Peptides Prevent the Conversion of Aβ42 Assemblies Into Fibrillar Structures.

Authors:  Tatsuya Ikenoue; Francesco A Aprile; Pietro Sormanni; Michele Vendruscolo
Journal:  Front Neurosci       Date:  2021-02-25       Impact factor: 4.677

Review 10.  Prospects of Neutralizing Nanobodies Against SARS-CoV-2.

Authors:  Fangfang Chen; Zhihong Liu; Fan Jiang
Journal:  Front Immunol       Date:  2021-05-28       Impact factor: 7.561

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