Bujotzek Alexander, Fuchs Angelika, Qu Changtao, Benz Jörg, Klostermann Stefan, Antes Iris, Georges Guy
a Roche Pharmaceutical Research and Early Development; Large Molecule Research; Roche Innovation Center Penzberg ; Penzberg , Germany.
MAbs. 2015;7(5):838-52. doi: 10.1080/19420862.2015.1068492.
Knowledge of the 3-dimensional structure of the antigen-binding region of antibodies enables numerous useful applications regarding the design and development of antibody-based drugs. We present a knowledge-based antibody structure prediction methodology that incorporates concepts that have arisen from an applied antibody engineering environment. The protocol exploits the rich and continuously growing supply of experimentally derived antibody structures available to predict CDR loop conformations and the packing of heavy and light chain quickly and without user intervention. The homology models are refined by a novel antibody-specific approach to adapt and rearrange sidechains based on their chemical environment. The method achieves very competitive all-atom root mean square deviation values in the order of 1.5 Å on different evaluation datasets consisting of both known and previously unpublished antibody crystal structures.
了解抗体抗原结合区域的三维结构有助于基于抗体的药物设计与开发的众多实用应用。我们提出了一种基于知识的抗体结构预测方法,该方法融合了应用抗体工程环境中产生的概念。该方案利用丰富且不断增长的实验衍生抗体结构资源,能够快速且无需用户干预地预测互补决定区(CDR)环构象以及重链和轻链的堆积情况。同源模型通过一种新颖的抗体特异性方法进行优化,根据侧链的化学环境对其进行调整和重新排列。在由已知和先前未发表的抗体晶体结构组成的不同评估数据集上,该方法实现了极具竞争力的全原子均方根偏差值,约为1.5 Å。