Molecular replacement (MR) is the predominant route to solution of the phase problem in macromolecular crystallography. Where the lack of a suitable homologue precludes conventional MR, one option is to predict the target structure using bioinformatics. Such modelling, in the absence of homologous templates, is called ab initio or de novo modelling. Recently, the accuracy of such models has improved significantly as a result of the availability, in many cases, of residue-contact predictions derived from evolutionary covariance analysis. Covariance-assisted ab initio models representing structurally uncharacterized Pfam families are now available on a large scale in databases, potentially representing a valuable and easily accessible supplement to the PDB as a source of search models. Here, the unconventional MR pipeline AMPLE is employed to explore the value of structure predictions in the GREMLIN and PconsFam databases. It was tested whether these deposited predictions, processed in various ways, could solve the structures of PDB entries that were subsequently deposited. The results were encouraging: nine of 27 GREMLIN cases were solved, covering target lengths of 109-355 residues and a resolution range of 1.4-2.9 Å, and with target-model shared sequence identity as low as 20%. The cluster-and-truncate approach in AMPLE proved to be essential for most successes. For the overall lower quality structure predictions in the PconsFam database, remodelling with Rosetta within the AMPLE pipeline proved to be the best approach, generating ensemble search models from single-structure deposits. Finally, it is shown that the AMPLE-obtained search models deriving from GREMLIN deposits are of sufficiently high quality to be selected by the sequence-independent MR pipeline SIMBAD. Overall, the results help to point the way towards the optimal use of the expanding databases of ab initio structure predictions.

中文翻译：

---

使用数据库中的结构预测进行分子置换。

分子置换（MR）是解决大分子晶体学中相问题的主要途径。在缺乏合适的同源物的情况下，常规MR除外，一种选择是使用生物信息学预测靶标结构。在没有同源模板的情况下，这种建模称为从头建模或从头建模。最近，由于在许多情况下可利用从进化协方差分析得出的残基接触预测，因此此类模型的准确性已显着提高。现在，可以在数据库中大规模使用代表结构未表征的Pfam家族的协方差辅助从头算模型，这可能表示对PDB的一种有价值且易于访问的补充，可以作为搜索模型的来源。这里，非常规MR管道AMPLE用于探索GREMLIN和PconsFam数据库中结构预测的价值。测试了以各种方式处理的这些沉积的预测是否可以解决随后沉积的PDB条目的结构。结果令人鼓舞：解决了27例GREMLIN病例中的9例，靶标长度为109-355个残基，分辨范围为1.4-2.9Å，靶标模型共享序列同一性低至20％。事实证明，AMPLE中的集群截断方法对于大多数成功至关重要。对于PconsFam数据库中总体质量较低的结构预测，在AMPLE管道中使用Rosetta进行重构被证明是最好的方法，可以从单一结构矿床生成整体搜索模型。最后，结果表明，从GREMLIN矿床获得的AMPLE搜索模型具有足够高的质量，可以由与序列无关的MR管道SIMBAD选择。总体而言，结果有助于指出从头开始结构预测的扩展数据库的最佳使用方式。