The folding of a protein structure is a process governed by both local and nonlocal interactions. While incorporating local dependencies into a machine learning algorithm for protein structure prediction is simple and has been exploited for some time, the modeling of long-range dependences which result from structurally-neighboring residues has only recently begun to be addressed. Structural properties designed to localize the prediction space from direct tertiary structure prediction, such as secondary structure, contact maps, and intrinsic disorder, among others, have begun to greatly benefit from machine learning models capable of modeling a widened, potentially global protein context. This has led to a direct enhancement of the quality of predicted tertiary structures through both the optimization of structural constraints and improved reliability of alignments to structural templates. These improvements have stemmed from the application of recurrent and convolutional neural network architectures effective not only at innate sequential context propagation but also deep feature extraction due to novel skip connections and normalization techniques allowing for greatly enhanced error back-propagation. The recent results from independent blind testing in Critical Assessment of protein Structure Prediction 13 have signaled the beginning of a new generation of protein structure prediction through the utilization of these contextual techniques. The ripples from advancements in the determination of one-dimensional and two-dimensional structural properties have us moving ever closer to the solution of the protein structure prediction problem.

中文翻译：

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了解邻居：上下文机器学习已成为蛋白质结构预测的时代。

蛋白质结构的折叠是受局部和非局部相互作用共同控制的过程。虽然将局部依赖关系合并到用于蛋白质结构预测的机器学习算法中很简单，并且已经被利用了一段时间，但是由结构上相邻的残基导致的远程依赖关系的建模才刚刚开始得到解决。旨在从直接的三级结构预测中定位预测空间的结构特性（例如二级结构，接触图和内在无序等）已开始从能够对扩展的潜在全球蛋白质环境建模的机器学习模型中受益匪浅。通过结构约束的优化和结构模板对齐方式的可靠性的提高，这直接提高了预测的三级结构的质量。这些改进源于递归和卷积神经网络体系结构的应用，该体系结构不仅在先天顺序上下文传播中有效，而且由于新颖的跳过连接和归一化技术（可大大增强错误反向传播）而对深度特征提取有效。蛋白质结构预测的关键评估13中独立盲测的最新结果表明，通过利用这些上下文技术，新一代蛋白质结构预测开始了。