在早期候选铅选择和优化过程中,对抗体开发能力的计算机评估至关重要。提供快速和无材料的筛选方法。然而,这些方法的预测能力和可重复性在很大程度上取决于分子描述符的选择,模型参数,预测结构模型的准确性,和构象采样技术。这里,我们提出了一组专门设计用于预测抗体发展的分子表面描述符。我们通过将它们的相关性与大量实验确定的生物物理特性进行基准测试来评估这些描述符的性能,包括粘度,聚合,疏水相互作用色谱,人体药代动力学清除率,肝素保留时间,和多特异性。Further,我们研究了这些表面描述符对方法学细微差别的敏感性,例如内部介电常数的选择,疏水性尺度,结构预测方法,以及构象抽样的影响。值得注意的是,根据所使用的结构预测方法,我们观察到表面描述符分布的系统变化,驱动表面描述符在结构模型中的弱相关性。在来自分子动力学的构象分布上对描述符值进行平均可以减轻系统偏移,并提高不同结构预测方法之间的一致性。尽管与生物物理数据的相关性改善不一致。根据我们的基准分析,我们提出了六个计算机可开发性风险标志,并评估了它们在预测一组案例研究分子的潜在可开发性问题方面的有效性.
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.