Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
Department of Chemistry, UCB Pharma, Slough SL1 3WE, UK.
Bioinformatics. 2018 Mar 15;34(6):949-956. doi: 10.1093/bioinformatics/btx718.
Protein function is often facilitated by the existence of multiple stable conformations. Structure prediction algorithms need to be able to model these different conformations accurately and produce an ensemble of structures that represent a target's conformational diversity rather than just a single state. Here, we investigate whether current loop prediction algorithms are capable of this. We use the algorithms to predict the structures of loops with multiple experimentally determined conformations, and the structures of loops with only one conformation, and assess their ability to generate and select decoys that are close to any, or all, of the observed structures.
We find that while loops with only one known conformation are predicted well, conformationally diverse loops are modelled poorly, and in most cases the predictions returned by the methods do not resemble any of the known conformers. Our results contradict the often-held assumption that multiple native conformations will be present in the decoy set, making the production of accurate conformational ensembles impossible, and hence indicating that current methodologies are not well suited to prediction of conformationally diverse, often functionally important protein regions.
Supplementary data are available at Bioinformatics online.
蛋白质的功能通常得益于多种稳定构象的存在。结构预测算法需要能够准确地模拟这些不同的构象，并生成一组代表目标构象多样性的结构，而不仅仅是单个状态。在这里，我们研究当前的环预测算法是否能够做到这一点。我们使用这些算法来预测具有多个实验确定构象的环的结构，以及只有一种构象的环的结构，并评估它们生成和选择接近任何或所有观察到的结构的诱饵的能力。
我们发现，虽然只有一种已知构象的环预测效果很好，但构象多样的环预测效果较差，在大多数情况下，这些方法返回的预测结果与任何已知的构象都不相似。我们的结果与一个普遍的假设相矛盾，即多个天然构象将存在于诱饵集中，这使得生成准确的构象组合变得不可能，因此表明当前的方法不太适合预测构象多样、通常具有重要功能的蛋白质区域。
补充数据可在Bioinformatics 在线获得。
