The performance of automated model building in crystal structure determination usually decreases with the resolution of the experimental data, and may result in fragmented models and incorrect side-chain assignment. Presented here are new methods for machine-learning-based docking of main-chain fragments to the sequence and for their sequence-independent connection using a dedicated library of protein fragments. The combined use of these new methods noticeably increases sequence coverage and reduces fragmentation of the protein models automatically built with ARP/wARP.

中文翻译：

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蛋白质晶体结构低分辨率建模的序列分配。


晶体结构测定中自动模型构建的性能通常会随着实验数据的分辨率而降低，并可能导致模型碎片化和侧链分配不正确。这里介绍的是基于机器学习的主链片段与序列对接的新方法，以及使用专用蛋白质片段库进行与序列无关的连接的新方法。这些新方法的结合使用显着增加了序列覆盖率并减少了使用 ARP/wARP 自动构建的蛋白质模型的碎片。