Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling
In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular des...
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| 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/10.1080/19420862.2024.2362788 |
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| author | Eliott Park Saeed Izadi |
| author_facet | Eliott Park Saeed Izadi |
| author_sort | Eliott Park |
| collection | DOAJ |
| description | In silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules. |
| format | Article |
| id | doaj-art-eb65760186ba4c83a17db95cba9a86f0 |
| 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-eb65760186ba4c83a17db95cba9a86f02025-01-31T04:19:37ZengTaylor & Francis GroupmAbs1942-08621942-08702024-12-0116110.1080/19420862.2024.2362788Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational samplingEliott Park0Saeed Izadi1Pharmaceutical Development, Genentech Inc, South San Francisco, CA, USAPharmaceutical Development, Genentech Inc, South San Francisco, CA, USAIn silico assessment of antibody developability during early lead candidate selection and optimization is of paramount importance, offering a rapid and material-free screening approach. However, the predictive power and reproducibility of such methods depend heavily on the selection of molecular descriptors, model parameters, accuracy of predicted structure models, and conformational sampling techniques. Here, we present a set of molecular surface descriptors specifically designed for predicting antibody developability. We assess the performance of these descriptors by benchmarking their correlations with an extensive array of experimentally determined biophysical properties, including viscosity, aggregation, hydrophobic interaction chromatography, human pharmacokinetic clearance, heparin retention time, and polyspecificity. Further, we investigate the sensitivity of these surface descriptors to methodological nuances, such as the choice of interior dielectric constant, hydrophobicity scales, structure prediction methods, and the impact of conformational sampling. Notably, we observe systematic shifts in the distribution of surface descriptors depending on the structure prediction method used, driving weak correlations of surface descriptors across structure models. Averaging the descriptor values over conformational distributions from molecular dynamics mitigates the systematic shifts and improves the consistency across different structure prediction methods, albeit with inconsistent improvements in correlations with biophysical data. Based on our benchmarking analysis, we propose six in silico developability risk flags and assess their effectiveness in predicting potential developability issues for a set of case study molecules.https://www.tandfonline.com/doi/10.1080/19420862.2024.2362788Aggregationantibodiesdevelopabilityelectrostaticshydrophobicityin silico prediction |
| spellingShingle | Eliott Park Saeed Izadi Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling mAbs Aggregation antibodies developability electrostatics hydrophobicity in silico prediction |
| title | Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling |
| title_full | Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling |
| title_fullStr | Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling |
| title_full_unstemmed | Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling |
| title_short | Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling |
| title_sort | molecular surface descriptors to predict antibody developability sensitivity to parameters structure models and conformational sampling |
| topic | Aggregation antibodies developability electrostatics hydrophobicity in silico prediction |
| url | https://www.tandfonline.com/doi/10.1080/19420862.2024.2362788 |
| work_keys_str_mv | AT eliottpark molecularsurfacedescriptorstopredictantibodydevelopabilitysensitivitytoparametersstructuremodelsandconformationalsampling AT saeedizadi molecularsurfacedescriptorstopredictantibodydevelopabilitysensitivitytoparametersstructuremodelsandconformationalsampling |