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In Silico Methods in Antibody Design

  1. Author:
    Zhao, Jun
    Nussinov, Ruth
    Wu, Wen-Jin
    Ma, Buyong

  2. Author Address

    US FDA, Div Biotechnol Review & Res 1, Off Biotechnol Prod, Off Pharmaceut Qual,Ctr Drug Evaluat & Res, 10903 New Hampshire Ave, Silver Spring, MD 20993 USA.NCI, Bethesda, MD 20892 USA.NCI, Canc & Inflammat Program, Ft Detrick, MD 21702 USA.NCI, Basic Sci Program, Leidos Biomed Res Inc, Canc & Inflammat Program, Ft Detrick, MD 21702 USA.Tel Aviv Univ, Sackler Sch Med, Dept Human Genet & Mol Med, Sackler Inst Mol Med, IL-69978 Tel Aviv, Israel.
    1. Year: 2018
    2. Date: Sep
    3. Epub Date: 2018 06 29
  2. Journal: ANTIBODIES
  3. MDPI,
    1. 7
    2. 3
  5. Type of Article: Review
  6. Article Number: 22
  7. ISSN: 2073-4468
  1. Abstract:

    Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.

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External Sources

  1. View Full Text
  2. DOI: 10.3390/antib7030022
  3. PMID: 31544874
  4. PMCID: PMC6640671
  5. View record in Web of Science®
  6. WOS: 000448330700002

Library Notes

  1. Fiscal Year: FY2017-2018
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