Teplyakov Alexey, Luo Jinquan, Obmolova Galina, Malia Thomas J, Sweet Raymond, Stanfield Robyn L, Kodangattil Sreekumar, Almagro Juan Carlos, Gilliland Gary L
Janssen Research & Development, LLC, 1400 McKean Road, Spring House, Pennsylvania, 19477.
Proteins. 2014 Aug;82(8):1563-82. doi: 10.1002/prot.24554. Epub 2014 Mar 31.
To assess the state-of-the-art in antibody structure modeling, a blinded study was conducted. Eleven unpublished Fab crystal structures were used as a benchmark to compare Fv models generated by seven structure prediction methodologies. In the first round, each participant submitted three non-ranked complete Fv models for each target. In the second round, CDR-H3 modeling was performed in the context of the correct environment provided by the crystal structures with CDR-H3 removed. In this report we describe the reference structures and present our assessment of the models. Some of the essential sources of errors in the predictions were traced to the selection of the structure template, both in terms of the CDR canonical structures and VL/VH packing. On top of this, the errors present in the Protein Data Bank structures were sometimes propagated in the current models, which emphasized the need for the curated structural database devoid of errors. Modeling non-canonical structures, including CDR-H3, remains the biggest challenge for antibody structure prediction.
为了评估抗体结构建模的最新技术水平,我们进行了一项盲法研究。使用11个未发表的Fab晶体结构作为基准,以比较由七种结构预测方法生成的Fv模型。在第一轮中,每位参与者为每个靶标提交三个未排序的完整Fv模型。在第二轮中,在去除CDR-H3的晶体结构提供的正确环境背景下进行CDR-H3建模。在本报告中,我们描述了参考结构并展示了对模型的评估。预测中一些基本的误差来源可追溯到结构模板的选择,这在CDR规范结构和VL/VH堆积方面均是如此。除此之外,蛋白质数据库结构中存在的误差有时会在当前模型中传播,这凸显了对无误差的精选结构数据库的需求。对包括CDR-H3在内的非规范结构进行建模仍然是抗体结构预测面临的最大挑战。