Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches
Asparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemi...
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Taylor & Francis Group
2024-12-01
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| Series: | mAbs |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19420862.2024.2333436 |
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| author | David Hoffmann Joschka Bauer Markus Kossner Andrew Henry Anne R. Karow-Zwick Giuseppe Licari |
| author_facet | David Hoffmann Joschka Bauer Markus Kossner Andrew Henry Anne R. Karow-Zwick Giuseppe Licari |
| author_sort | David Hoffmann |
| collection | DOAJ |
| description | Asparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemically stable monoclonal antibodies (mAbs) can substantially mitigate the risk of subsequent failure. In this study, we introduce a novel in silico approach for predicting deamidation and isomerization sites in therapeutic antibodies by analyzing the structural environment surrounding asparagine and aspartate residues. The resulting quantitative structure-activity relationship (QSAR) model was trained using previously published forced degradation data from 57 clinical-stage mAbs. The predictive accuracy of the model was evaluated for four different states of the protein structure: (1) static homology models, (2) enhancing low-frequency vibrational modes during short molecular dynamics (MD) runs, (3) a combination of (2) with a protonation state reassignment, and (4) conventional full-atomistic MD simulations. The most effective QSAR model considered the accessible surface area (ASA) of the residue, the pKa value of the backbone amide, and the root mean square deviations of both the alpha carbon and the side chain. The accuracy was further enhanced by incorporating the QSAR model into a decision tree, which also includes empirical information about the sequential successor and the position in the protein. The resulting model has been implemented as a plugin named “Forecasting Reactivity of Isomerization and Deamidation in Antibodies” in MOE software, completed with a user-friendly graphical interface to facilitate its use. |
| format | Article |
| id | doaj-art-3c09ebb50b6e4afeb85adac939a9278e |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
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| series | mAbs |
| spelling | doaj-art-3c09ebb50b6e4afeb85adac939a9278e2025-01-31T04:19:38ZengTaylor & Francis GroupmAbs1942-08621942-08702024-12-0116110.1080/19420862.2024.2333436Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approachesDavid Hoffmann0Joschka Bauer1Markus Kossner2Andrew Henry3Anne R. Karow-Zwick4Giuseppe Licari5Early Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, GermanyEarly Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, GermanyScientific Services, Chemical Computing Group, Cologne, GermanyScientific Support, Chemical Computing Group, Cambridge, UKEarly Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, GermanyEarly Stage Pharmaceutical Development Biologicals, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach/Riss, GermanyAsparagine (Asn) deamidation and aspartic acid (Asp) isomerization are common degradation pathways that affect the stability of therapeutic antibodies. These modifications can pose a significant challenge in the development of biopharmaceuticals. As such, the early engineering and selection of chemically stable monoclonal antibodies (mAbs) can substantially mitigate the risk of subsequent failure. In this study, we introduce a novel in silico approach for predicting deamidation and isomerization sites in therapeutic antibodies by analyzing the structural environment surrounding asparagine and aspartate residues. The resulting quantitative structure-activity relationship (QSAR) model was trained using previously published forced degradation data from 57 clinical-stage mAbs. The predictive accuracy of the model was evaluated for four different states of the protein structure: (1) static homology models, (2) enhancing low-frequency vibrational modes during short molecular dynamics (MD) runs, (3) a combination of (2) with a protonation state reassignment, and (4) conventional full-atomistic MD simulations. The most effective QSAR model considered the accessible surface area (ASA) of the residue, the pKa value of the backbone amide, and the root mean square deviations of both the alpha carbon and the side chain. The accuracy was further enhanced by incorporating the QSAR model into a decision tree, which also includes empirical information about the sequential successor and the position in the protein. The resulting model has been implemented as a plugin named “Forecasting Reactivity of Isomerization and Deamidation in Antibodies” in MOE software, completed with a user-friendly graphical interface to facilitate its use.https://www.tandfonline.com/doi/10.1080/19420862.2024.2333436Deamidationdevelopabilityin silico methodsisomerizationquantitative structure-activity relationshiptherapeutic antibody |
| spellingShingle | David Hoffmann Joschka Bauer Markus Kossner Andrew Henry Anne R. Karow-Zwick Giuseppe Licari Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches mAbs Deamidation developability in silico methods isomerization quantitative structure-activity relationship therapeutic antibody |
| title | Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches |
| title_full | Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches |
| title_fullStr | Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches |
| title_full_unstemmed | Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches |
| title_short | Predicting deamidation and isomerization sites in therapeutic antibodies using structure-based in silico approaches |
| title_sort | predicting deamidation and isomerization sites in therapeutic antibodies using structure based in silico approaches |
| topic | Deamidation developability in silico methods isomerization quantitative structure-activity relationship therapeutic antibody |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2024.2333436 |
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