PDF(Pubmed)
Abstract：
UNASSIGNED: The use of Monoclonal Antibodies (MAbs) as therapeutics has been increasing over the past 30 years due to their high specificity and strong affinity toward the target. One of the major challenges toward their use as drugs is their low thermostability, which impacts both efficacy as well as manufacturing and delivery.
UNASSIGNED: To aid the design of thermally more stable mutants, consensus sequence-based method has been widely used. These methods typically have a success rate of about 50% with maximum melting temperature increment ranging from 10 to 32°C. To improve the prediction performance, we have developed a new and fast MAbs specific method by adding a 3D structural layer to the consensus sequence method. This is done by analyzing the close-by residue pairs which are conserved in >800 MAbs\' 3D structures.
UNASSIGNED: Combining consensus sequence and structural residue pair covariance methods, we developed an in-house application for predicting human MAb thermostability to guide protein engineers to design stable molecules. Major advantage of this structural level assessment is in significantly reducing the false positives by almost half from the consensus sequence method alone. This application has shown success in designing MAb engineering panels in multiple biologics programs.
UNASSIGNED: Our data science-based method shows impacts in Mab engineering.
UNASSIGNED: To aid the design of thermally more stable mutants, consensus sequence-based method has been widely used. These methods typically have a success rate of about 50% with maximum melting temperature increment ranging from 10 to 32°C. To improve the prediction performance, we have developed a new and fast MAbs specific method by adding a 3D structural layer to the consensus sequence method. This is done by analyzing the close-by residue pairs which are conserved in >800 MAbs\' 3D structures.
UNASSIGNED: Combining consensus sequence and structural residue pair covariance methods, we developed an in-house application for predicting human MAb thermostability to guide protein engineers to design stable molecules. Major advantage of this structural level assessment is in significantly reducing the false positives by almost half from the consensus sequence method alone. This application has shown success in designing MAb engineering panels in multiple biologics programs.
UNASSIGNED: Our data science-based method shows impacts in Mab engineering.
摘要：
单克隆抗体(MAb)作为治疗剂的使用在过去30年中由于它们对靶标的高特异性和强亲和力而不断增加。它们作为药物使用的主要挑战之一是它们的热稳定性低，这既影响功效，也影响制造和交付。
■为了帮助设计热更稳定的突变体，基于共有序列的方法得到了广泛的应用。这些方法通常具有约50%的成功率,最大熔化温度增量在10至32°C的范围内。为了提高预测性能，我们通过在共有序列方法中添加3D结构层，开发了一种新的快速MAb特异性方法。这是通过分析在>800MAb\'3D结构中保守的附近残基对来完成的。
■结合共有序列和结构残基对协方差方法，我们开发了一种预测人类MAb热稳定性的内部应用程序，以指导蛋白质工程师设计稳定的分子。这种结构水平评估的主要优点是仅通过共有序列方法将假阳性显着减少了近一半。此应用程序已在多个生物制剂计划中设计MAb工程面板方面取得了成功。
■我们基于数据科学的方法显示了Mab工程的影响。
■为了帮助设计热更稳定的突变体，基于共有序列的方法得到了广泛的应用。这些方法通常具有约50%的成功率,最大熔化温度增量在10至32°C的范围内。为了提高预测性能，我们通过在共有序列方法中添加3D结构层，开发了一种新的快速MAb特异性方法。这是通过分析在>800MAb\'3D结构中保守的附近残基对来完成的。
■结合共有序列和结构残基对协方差方法，我们开发了一种预测人类MAb热稳定性的内部应用程序，以指导蛋白质工程师设计稳定的分子。这种结构水平评估的主要优点是仅通过共有序列方法将假阳性显着减少了近一半。此应用程序已在多个生物制剂计划中设计MAb工程面板方面取得了成功。
■我们基于数据科学的方法显示了Mab工程的影响。
