DESTINI: A deep-learning approach to contact-driven protein structure prediction
DESTINI: A deep-learning approach to contact-driven protein structure prediction
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DOI：
10.1038/s41598-019-40314-1
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发表时间：
2019-03-05
影响因子：
4.6
通讯作者：
Skolnick, Jeffrey
中科院分区：
文献类型：
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作者：
Gao, Mu;Zhou, Hongyi;Skolnick, Jeffrey
蛋白质的氨基酸序列编码了其天然结构的蓝图。从蛋白质序列中预测相应的结构折叠是计算生物学中最具挑战性的问题之一。在这项工作中，我们介绍了Destini(蛋白质的深度结构推理)，这是一种新的计算方法，它结合了蛋白质残基/残基接触预测的深度学习算法和基于模板的结构建模。首次在超过1,200个单域蛋白质的大规模三级结构预测中证明了显著提高的预测能力。Destini成功地预测了“硬”目标(模板质量较差的目标)数量的四倍的三级结构，即以前基于模板的方法的“玻璃天花板”，也就是“容易”目标(模板质量较好的目标)的模型质量。Destini的显著更好的性能在很大程度上是由于在模板建模中纳入了更好的接触预测。为了理解为什么深度学习可以实现更准确的接触预测，系统的聚类表明，与共同进化分析相比，深度学习预测连贯的、原生的接触模式。综上所述，这项工作为解决蛋白质结构预测问题提供了一种很有前途的策略。
The amino acid sequence of a protein encodes the blueprint of its native structure. To predict the corresponding structural fold from the protein's sequence is one of most challenging problems in computational biology. In this work, we introduce DESTINI (deep structural inference for proteins), a novel computational approach that combines a deep-learning algorithm for protein residue/residue contact prediction with template-based structural modelling. For the first time, the significantly improved predictive ability is demonstrated in the large-scale tertiary structure prediction of over 1,200 single-domain proteins. DESTINI successfully predicts the tertiary structure of four times the number of "hard" targets (those with poor quality templates) that were previously intractable, viz, a "glass-ceiling" for previous template-based approaches, and also improves model quality for "easy" targets (those with good quality templates). The significantly better performance by DESTINI is largely due to the incorporation of better contact prediction into template modelling. To understand why deep-learning accomplishes more accurate contact prediction, systematic clustering reveals that deep-learning predicts coherent, native-like contact patterns compared to co-evolutionary analysis. Taken together, this work presents a promising strategy towards solving the protein structure prediction problem.