Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities
Autor: | Ing-Chien Chen, Tung Chao-Ping, Hung-Pin Peng, Kuo Wei-Ying, Jhih-Wei Jian, Yu Chung-Ming, Chiu Yi-Kai, An-Suei Yang, Yueh-Liang Tsou, Hong-Sen Chen, Hung-Ju Hsu |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
hot spot residues for antibody-protein interactions
Immunology Antibody Affinity Protein Engineering Affinity maturation Machine Learning 03 medical and health sciences Structure-Activity Relationship 0302 clinical medicine synthetic antibody library Antigen Antibody Specificity Peptide Library Report Protein recognition Immunology and Allergy Humans 030304 developmental biology chemistry.chemical_classification affinity maturation 0303 health sciences biology antibody engineering Chemistry Immune protection anti-HER2 antibodies antibody-antigen affinity prediction Amino acid Biochemistry 030220 oncology & carcinogenesis biology.protein Antibody human activities Single-Chain Antibodies |
Zdroj: | mAbs |
ISSN: | 1942-0870 1942-0862 |
Popis: | Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. We designed and constructed phage-displayed synthetic antibody libraries with enriched protein antigen-recognition propensities calculated with machine learning predictors, which indicated that the designed single-chain variable fragment variants were encoded with enhanced distributions of complementarity-determining region (CDR) hot spot residues with high protein antigen recognition propensities in comparison with those in the human antibody germline sequences. Antibodies derived directly from the synthetic antibody libraries, without affinity maturation cycles comparable to those in in vivo immune systems, bound to the corresponding protein antigen through diverse conformational or linear epitopes with specificity and affinity comparable to those of the affinity-matured antibodies from in vivo immune systems. The results indicated that more densely populated CDR hot spot residues were sustainable by the antibody structural frameworks and could be accompanied by enhanced functionalities in recognizing protein antigens. Our study results suggest that synthetic antibody libraries, which are not limited by the sequences found in antibodies in nature, could be designed with the guidance of the computational machine learning algorithms that are programmed to predict interaction propensities to molecules of diverse chemical properties, leading to antibodies with optimal characteristics pertinent to their medical applications. |
Databáze: | OpenAIRE |
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