Sydow Jasmin F, Lipsmeier Florian, Larraillet Vincent, Hilger Maximiliane, Mautz Bjoern, Mølhøj Michael, Kuentzer Jan, Klostermann Stefan, Schoch Juergen, Voelger Hans R, Regula Joerg T, Cramer Patrick, Papadimitriou Apollon, Kettenberger Hubert
Large Molecule Research, Roche Pharmaceutical Research and Early Development, Penzberg, Germany.
Therapeutics Modalities Informatics, Roche Pharmaceutical Research and Early Development, Penzberg, Germany.
PLoS One. 2014 Jun 24;9(6):e100736. doi: 10.1371/journal.pone.0100736. eCollection 2014.
Monoclonal antibodies (mAbs) and proteins containing antibody domains are the most prevalent class of biotherapeutics in diverse indication areas. Today, established techniques such as immunization or phage display allow for an efficient generation of new mAbs. Besides functional properties, the stability of future therapeutic mAbs is a key selection criterion which is essential for the development of a drug candidate into a marketed product. Therapeutic proteins may degrade via asparagine (Asn) deamidation and aspartate (Asp) isomerization, but the factors responsible for such degradation remain poorly understood. We studied the structural properties of a large, uniform dataset of Asn and Asp residues in the variable domains of antibodies. Their structural parameters were correlated with the degradation propensities measured by mass spectrometry. We show that degradation hotspots can be characterized by their conformational flexibility, the size of the C-terminally flanking amino acid residue, and secondary structural parameters. From these results we derive an accurate in silico prediction method for the degradation propensity of both Asn and Asp residues in the complementarity-determining regions (CDRs) of mAbs.
单克隆抗体(mAb)和含有抗体结构域的蛋白质是不同适应症领域中最普遍的一类生物治疗药物。如今,诸如免疫接种或噬菌体展示等成熟技术能够高效地产生新的单克隆抗体。除了功能特性外,未来治疗性单克隆抗体的稳定性是关键的筛选标准,这对于将候选药物开发成上市产品至关重要。治疗性蛋白质可能会通过天冬酰胺(Asn)脱酰胺和天冬氨酸(Asp)异构化而降解,但导致这种降解的因素仍知之甚少。我们研究了抗体可变区中大量、统一的天冬酰胺和天冬氨酸残基数据集的结构特性。它们的结构参数与通过质谱测量的降解倾向相关。我们表明,降解热点可以通过其构象灵活性、C末端侧翼氨基酸残基的大小和二级结构参数来表征。从这些结果中,我们推导出一种准确的计算机预测方法,用于预测单克隆抗体互补决定区(CDR)中天冬酰胺和天冬氨酸残基的降解倾向。