The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational approaches for determining functionally-relevant structures. The majority of structures generated in silico are not relevant. Discriminating relevant/native protein structures from non-native ones is an outstanding challenge in computational structural biology. Inherently, this is a recognition problem that can be addressed under the umbrella of machine learning. In this paper, based on the premise that near-native structures are effectively anomalies, we build on the concept of anomaly detection in machine learning. We propose methods that automatically select relevant subsets, as well as methods that select a single structure to offer as prediction. Evaluations are carried out on benchmark datasets and demonstrate that the proposed methods advance the state of the art. The presented results motivate further building on and adapting concepts and techniques from machine learning to improve recognition of near-native structures in protein structure prediction.

中文翻译：

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基于异常检测的近天然蛋白质结构识别


蛋白质分子的三维结构在很大程度上决定了其生物活性。蛋白质结构编码的关于蛋白质功能的丰富信息继续推动着确定功能相关结构的计算方法的发展。大多数计算机生成的结构都是不相关的。区分相关/天然蛋白质结构和非天然蛋白质结构是计算结构生物学中的一个突出挑战。从本质上讲，这是一个可以在机器学习的框架下解决的识别问题。在本文中，基于近原生结构实际上是异常的前提，我们建立在机器学习中异常检测的概念之上。我们提出了自动选择相关子集的方法，以及选择单个结构作为预测的方法。对基准数据集进行评估，并证明所提出的方法推进了现有技术的发展。所提出的结果激励进一步建立和适应机器学习的概念和技术，以提高蛋白质结构预测中近天然结构的识别。