Literature DB >> 33876197

IsAb: a computational protocol for antibody design.

Tianjian Liang1, Hui Chen1, Jiayi Yuan1, Chen Jiang1, Yixuan Hao1, Yuanqiang Wang2, Zhiwei Feng1, Xiang-Qun Xie3.   

Abstract

The design of therapeutic antibodies has attracted a large amount of attention over the years. Antibodies are widely used to treat many diseases due to their high efficiency and low risk of adverse events. However, the experimental methods of antibody design are time-consuming and expensive. Although computational antibody design techniques have had significant advances in the past years, there are still some challenges that need to be solved, such as the flexibility of antigen structure, the lack of antibody structural data and the absence of standard antibody design protocol. In the present work, we elaborated on an in silico antibody design protocol for users to easily perform computer-aided antibody design. First, the Rosetta web server will be applied to generate the 3D structure of query antibodies if there is no structural information available. Then, two-step docking will be used to identify the binding pose of an antibody-antigen complex when the binding information is unknown. ClusPro is the first method to be used to conduct the global docking, and SnugDock is applied for the local docking. Sequentially, based on the predicted binding poses, in silico alanine scanning will be used to predict the potential hotspots (or key residues). Finally, computational affinity maturation protocol will be used to modify the structure of antibodies to theoretically increase their affinity and stability, which will be further validated by the bioassays in the future. As a proof of concept, we redesigned antibody D44.1 and compared it with previously reported data in order to validate IsAb protocol. To further illustrate our proposed protocol, we used cemiplimab antibody, a PD-1 checkpoint inhibitor, as an example to showcase a step-by-step tutorial.
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  antibody design; computer-aided antibody protocol; protein engineering; protein–protein docking

Mesh:

Substances:

Year:  2021        PMID: 33876197      PMCID: PMC8579189          DOI: 10.1093/bib/bbab143

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  74 in total

Review 1.  Somatic immunoglobulin hypermutation.

Authors:  Marilyn Diaz; Paolo Casali
Journal:  Curr Opin Immunol       Date:  2002-04       Impact factor: 7.486

2.  Cyclic coordinate descent: A robotics algorithm for protein loop closure.

Authors:  Adrian A Canutescu; Roland L Dunbrack
Journal:  Protein Sci       Date:  2003-05       Impact factor: 6.725

3.  Development and testing of a general amber force field.

Authors:  Junmei Wang; Romain M Wolf; James W Caldwell; Peter A Kollman; David A Case
Journal:  J Comput Chem       Date:  2004-07-15       Impact factor: 3.376

4.  Protein-protein docking benchmark version 4.0.

Authors:  Howook Hwang; Thom Vreven; Joël Janin; Zhiping Weng
Journal:  Proteins       Date:  2010-11-15

Review 5.  Theoretical and computational protein design.

Authors:  Ilan Samish; Christopher M MacDermaid; Jose Manuel Perez-Aguilar; Jeffery G Saven
Journal:  Annu Rev Phys Chem       Date:  2011       Impact factor: 12.703

6.  Assessment of flexible backbone protein design methods for sequence library prediction in the therapeutic antibody Herceptin-HER2 interface.

Authors:  Mariana Babor; Daniel J Mandell; Tanja Kortemme
Journal:  Protein Sci       Date:  2011-05-03       Impact factor: 6.725

7.  Computational Systems Pharmacology-Target Mapping for Fentanyl-Laced Cocaine Overdose.

Authors:  Jin Cheng; Siyi Wang; Weiwei Lin; Nan Wu; Yuanqiang Wang; Maozi Chen; Xiang-Qun Xie; Zhiwei Feng
Journal:  ACS Chem Neurosci       Date:  2019-07-15       Impact factor: 4.418

8.  AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences.

Authors:  Gideon D Lapidoth; Dror Baran; Gabriele M Pszolla; Christoffer Norn; Assaf Alon; Michael D Tyka; Sarel J Fleishman
Journal:  Proteins       Date:  2015-06-06

9.  OptMAVEn--a new framework for the de novo design of antibody variable region models targeting specific antigen epitopes.

Authors:  Tong Li; Robert J Pantazes; Costas D Maranas
Journal:  PLoS One       Date:  2014-08-25       Impact factor: 3.240

10.  An unexpected N-terminal loop in PD-1 dominates binding by nivolumab.

Authors:  Shuguang Tan; Hao Zhang; Yan Chai; Hao Song; Zhou Tong; Qihui Wang; Jianxun Qi; Gary Wong; Xiaodong Zhu; William J Liu; Shan Gao; Zhongfu Wang; Yi Shi; Fuquan Yang; George F Gao; Jinghua Yan
Journal:  Nat Commun       Date:  2017-02-06       Impact factor: 14.919

View more
  2 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.  SYNBIP: synthetic binding proteins for research, diagnosis and therapy.

Authors:  Xiaona Wang; Fengcheng Li; Wenqi Qiu; Binbin Xu; Yanlin Li; Xichen Lian; Hongyan Yu; Zhao Zhang; Jianxin Wang; Zhaorong Li; Weiwei Xue; Feng Zhu
Journal:  Nucleic Acids Res       Date:  2022-01-07       Impact factor: 16.971

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.