PreprintDatasetVersion 1Preserved in Portico This version is not peer-reviewed
Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design
Version 1
: Received: 29 February 2020 / Approved: 1 March 2020 / Online: 1 March 2020 (12:39:55 CET)
How to cite:
Li, W. Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design. Preprints2020, 2020030011. https://doi.org/10.20944/preprints202003.0011.v1
Li, W. Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design. Preprints 2020, 2020030011. https://doi.org/10.20944/preprints202003.0011.v1
Li, W. Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design. Preprints2020, 2020030011. https://doi.org/10.20944/preprints202003.0011.v1
APA Style
Li, W. (2020). Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design. Preprints. https://doi.org/10.20944/preprints202003.0011.v1
Chicago/Turabian Style
Li, W. 2020 "Extracting the Interfacial Electrostatic Features from Experimentally Determined Antigen and/or Antibody-Related Structures inside Protein Data Bank for Machine Learning-Based Antibody Design" Preprints. https://doi.org/10.20944/preprints202003.0011.v1
Abstract
The importance of antibodies in health care and the biotechnology research and development demands not only knowledge of their experimental structures at high resolution, but also practical implementation of this knowledge for both effective and efficient design and production of antibody for its use in both medical and research applications. While the experimental wet-lab approach is usually costly, laborious and time-consuming, computational (dry-lab) approaches, in spite of their intrinsic limitations in comparison with its experimental (wet-lab) counterpart, provide a cheaper and faster alternative option. For the first time, this article reports a comprehensive set of structural electrostatic features extracted from experimentally determined antigen-antibody-related structures, including especially those structural electrostatic features at the interfaces of all experimentally determined antigen-antibody complex structures as of February 29, 2020, to facilitate effective and efficient machine learning-based computational antibody design using currently available experimental structures inside Protein Data Bank.
Keywords
antigen-antibody complex structure; interfacial electrostatic feature; Machine Learning-Based Antibody Design; Protein Data Bank
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
