Protein contact map refinement for improving structure prediction using generative adversarial networks
Protein contact map refinement for improving structure prediction using generative adversarial networks
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DOI：
10.1093/bioinformatics/btab220
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发表时间：
2021-03
期刊：
影响因子：
5.8
通讯作者：
Sai Raghavendra Maddhuri Venkata Subramaniya;Genki Terashi;Aashish Jain;Yuki Kagaya;D. Kihara
中科院分区：
文献类型：
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作者：
Sai Raghavendra Maddhuri Venkata Subramaniya;Genki Terashi;Aashish Jain;Yuki Kagaya;D. Kihara
动机蛋白质结构预测一直是计算生物学和生物物理学研究的重要课题之一。在过去的几年里，蛋白质残基-残基接触预测有了长足的进步，这使得它成为成功进行蛋白质结构预测的关键驱动力。因此，提高接触预测的准确性已成为蛋白质结构预测的前沿。结果提出了一种新的基于产生式对抗性网络(GAN)的联系人地图精化方法ContactGAN。当在包括CASP13和CASP14中的蛋白质结构建模目标在内的三个数据集上进行测试时，ContactGAN能够比最近的接触预测方法所做的预测有显着的改进。我们展示了接触预测精度的提高，这转化为蛋白质三级结构模型精度的提高。另一方面，与trRosetta相比，观察到的改善相对较小，讨论了原因。ContactGAN将是结构预测流水线中的一个有价值的补充，以实现接触预测精度的额外提高。可用性https://github.com/kiharalab/ContactGAN.补充信息补充数据可在生物信息学在线上获得。
MOTIVATION Protein structure prediction remains as one of the most important problems in computational biology and biophysics. In the past few years, protein residue-residue contact prediction has undergone substantial improvement, which has made it a critical driving force for successful protein structure prediction. Boosting the accuracy of contact predictions has, therefore, become the forefront of protein structure prediction. RESULTS We show a novel contact map refinement method, ContactGAN, which uses Generative Adversarial Networks (GAN). ContactGAN was able to make a significant improvement over predictions made by recent contact prediction methods when tested on three datasets including protein structure modeling targets in CASP13 and CASP14. We show improvement of precision in contact prediction, which translated into improvement in the accuracy of protein tertiary structure models. On the other hand, observed improvement over trRosetta was relatively small, reasons for which are discussed. ContactGAN will be a valuable addition in the structure prediction pipeline to achieve an extra gain in contact prediction accuracy. AVAILABILITY https://github.com/kiharalab/ContactGAN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.