School of Computer Science and Informatics and Complex and Adaptive Systems Laboratory, University College Dublin, Belfield, Dublin 4, Ireland.
Curr Protein Pept Sci. 2011 Sep;12(6):549-62. doi: 10.2174/138920311796957649.
In order to use a predicted protein structure one needs to know how good it is, as the utility of a model depends on its quality. To this aim, many Model Quality Assessment Programs (MQAP) have been developed over the last decade, with MQAP also being assessed at the CASP competition. We present a new knowledge-based MQAP which evaluates single protein structure models. We use a tree representation of the Cα trace to train a novel Neural Network Pairwise Interaction Field (NN-PIF) to predict the global quality of a model. NN-PIF allows fast evaluation of multiple structure models for a single sequence. In our tests on a large set of structures, our networks outperform most other methods based on different and more complex protein structure representations in global model quality prediction. Moreover, given NN-PIF can evaluate protein conformations very fast, we train a separate version of the model to gauge its ability to fold protein structures ab initio. We show that the resulting system, which relies only on basic information about the sequence and the Cα trace of a conformation, generally improves the quality of the structures it is presented with and may yield promising predictions in the absence of structural templates, although more research is required to harness the full potential of the model.
为了使用预测的蛋白质结构，需要知道它的质量如何，因为模型的效用取决于其质量。为此，在过去的十年中已经开发了许多模型质量评估程序（MQAP），并且在 CASP 竞赛中也对 MQAP 进行了评估。我们提出了一种新的基于知识的 MQAP，用于评估单个蛋白质结构模型。我们使用 Cα 轨迹的树表示形式来训练一种新的神经网络成对相互作用场（NN-PIF），以预测模型的整体质量。NN-PIF 允许快速评估单个序列的多个结构模型。在对一组大型结构的测试中，我们的网络在全局模型质量预测方面优于基于不同且更复杂的蛋白质结构表示的大多数其他方法。此外，由于 NN-PIF 可以非常快速地评估蛋白质构象，因此我们训练了模型的单独版本来评估其从头开始折叠蛋白质结构的能力。我们表明，该系统仅依赖于序列的基本信息和构象的 Cα 轨迹，通常可以提高所提供结构的质量，并且在没有结构模板的情况下可能会产生有希望的预测，尽管需要进一步研究才能充分利用模型的潜力。
