Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen
Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year....
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| Format: | Article |
| Language: | English |
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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/10.1080/19420862.2024.2362775 |
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| author | Mark Hutchinson Jeffrey A. Ruffolo Nantaporn Haskins Michael Iannotti Giuliana Vozza Tony Pham Nurjahan Mehzabeen Harini Shandilya Keith Rickert Rebecca Croasdale-Wood Melissa Damschroder Ying Fu Andrew Dippel Jeffrey J. Gray Gilad Kaplan |
| author_facet | Mark Hutchinson Jeffrey A. Ruffolo Nantaporn Haskins Michael Iannotti Giuliana Vozza Tony Pham Nurjahan Mehzabeen Harini Shandilya Keith Rickert Rebecca Croasdale-Wood Melissa Damschroder Ying Fu Andrew Dippel Jeffrey J. Gray Gilad Kaplan |
| author_sort | Mark Hutchinson |
| collection | DOAJ |
| description | Over the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody–antigen interface, which is notoriously difficult in the absence of crystal structures. |
| format | Article |
| id | doaj-art-fd2f8ba9525448dbbf68ca2c03eef55e |
| institution | Kabale University |
| issn | 1942-0862 1942-0870 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | mAbs |
| spelling | doaj-art-fd2f8ba9525448dbbf68ca2c03eef55e2025-01-31T04:19:37ZengTaylor & Francis GroupmAbs1942-08621942-08702024-12-0116110.1080/19420862.2024.2362775Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigenMark Hutchinson0Jeffrey A. Ruffolo1Nantaporn Haskins2Michael Iannotti3Giuliana Vozza4Tony Pham5Nurjahan Mehzabeen6Harini Shandilya7Keith Rickert8Rebecca Croasdale-Wood9Melissa Damschroder10Ying Fu11Andrew Dippel12Jeffrey J. Gray13Gilad Kaplan14Biologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USAProgram in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiopharmaceuticals Development, R&D, AstraZeneca, Cambridge, UKBiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Cambridge, UKBiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USAProgram in Molecular Biophysics, The Johns Hopkins University, Baltimore, MD, USABiologics Engineering, R&D, AstraZeneca, Gaithersburg, MD, USAOver the past two decades, therapeutic antibodies have emerged as a rapidly expanding domain within the field of biologics. In silico tools that can streamline the process of antibody discovery and optimization are critical to support a pipeline that is growing more numerous and complex every year. High-quality structural information remains critical for the antibody optimization process, but antibody-antigen complex structures are often unavailable and in silico antibody docking methods are still unreliable. In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. We sought to determine whether DeepAb-designed variants containing combinations of beneficial mutations from the DMS exhibit enhanced thermostability and whether this optimization affected their developability profile. The 200 variants were produced through a robust high-throughput method and tested for thermal and colloidal stability (Tonset, Tm, Tagg), affinity (KD) relative to the parental antibody, and for developability parameters (nonspecific binding, aggregation propensity, self-association). Of the designed clones, 91% and 94% exhibited increased thermal and colloidal stability and affinity, respectively. Of these, 10% showed a significantly increased affinity for HEL (5- to 21-fold increase) and thermostability (>2.5C increase in Tm1), with most clones retaining the favorable developability profile of the parental antibody. Additional in silico tests suggest that these methods would enrich for binding affinity even without first collecting experimental DMS measurements. These data open the possibility of in silico antibody optimization without the need to predict the antibody–antigen interface, which is notoriously difficult in the absence of crystal structures.https://www.tandfonline.com/doi/10.1080/19420862.2024.2362775Antibodiescomputational designdevelopabilitymachine learningthermostability |
| spellingShingle | Mark Hutchinson Jeffrey A. Ruffolo Nantaporn Haskins Michael Iannotti Giuliana Vozza Tony Pham Nurjahan Mehzabeen Harini Shandilya Keith Rickert Rebecca Croasdale-Wood Melissa Damschroder Ying Fu Andrew Dippel Jeffrey J. Gray Gilad Kaplan Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen mAbs Antibodies computational design developability machine learning thermostability |
| title | Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen |
| title_full | Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen |
| title_fullStr | Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen |
| title_full_unstemmed | Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen |
| title_short | Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen |
| title_sort | toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen |
| topic | Antibodies computational design developability machine learning thermostability |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2024.2362775 |
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