Machine learning methods for predicting protein structure from single sequences.
Machine learning methods for predicting protein structure from single sequences.
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DOI：
10.1016/j.sbi.2023.102627
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发表时间：
2023-06
影响因子：
6.8
通讯作者：
S. M. Kandathil;Andy M. Lau;David T. Jones
中科院分区：
文献类型：
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作者：
S. M. Kandathil;Andy M. Lau;David T. Jones
最近在蛋白质结构预测方面的突破越来越依赖于深度神经网络的使用。这些最近的方法值得注意的是，它们产生三维原子坐标作为网络的直接输出，这一特征显示出许多优点。虽然这种类型的大多数技术使用多个序列比对作为其主要输入，但新一波的方法试图仅使用单个序列作为输入。我们讨论了这些模式的组成和运作原理，并强调了这些领域的新发展，以及未来的发展方向。
Recent breakthroughs in protein structure prediction have increasingly relied on the use of deep neural networks. These recent methods are notable in that they produce 3-D atomic coordinates as a direct output of the networks, a feature which presents many advantages. Although most techniques of this type make use of multiple sequence alignments as their primary input, a new wave of methods have attempted to use just single sequences as the input. We discuss the make-up and operating principles of these models, and highlight new developments in these areas, as well as areas for future development.