High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments
High-accuracy modeling of antibody structures by a search for minimum-energy recombination of backbone fragments
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DOI：
10.1002/prot.25185
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发表时间：
2017-01-01
影响因子：
2.9
通讯作者：
Fleishman, Sarel J.
中科院分区：
文献类型：
--
作者：
Norn, Christoffer H.;Lapidoth, Gideon;Fleishman, Sarel J.
当前的抗体结构预测方法依赖于与已知结构的序列同源性。尽管这种策略通常会产生准确的预测，但模型可能会受到立体化学的影响。在这里，我们提出了一种名为 AbPredict 的全自动算法，该算法忽略序列同源性，而是使用蒙特卡洛搜索来搜索由抗体分子结构中出现的主链片段和刚体方向构建的低能构象。我们发现 AbPredict 选择序列同一性低至 10% 的准确环模板，而序列同一性最高的模板与查询的构象有很大差异。因此，在最近的抗体建模评估基准报告中报告的几个案例中，AbPredict 模型比任何参与者的模型都更准确，并且模型的立体化学质量始终很高。此外，在晶体学家在结构测定之前向我们提供的两个盲例中，该方法实现了
Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo-chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low-energy conformations built from backbone segments and rigid-body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo-chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved