arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1811.03481q-bio.BM
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深度学习支持的基于距离的蛋白质折叠

Distance-based Protein Folding Powered by Deep Learning

Jinbo Xu

摘要:

接触辅助蛋白质折叠已经取得了很好的进展,但仍然存在两个挑战。一个是精确的接触预测的蛋白质缺乏许多序列同源物,另一个是,耗时的折叠模拟往往需要预测良好的3D模型从预测的接触。我们表明,蛋白质距离矩阵可以通过深度学习很好地预测,然后直接用于构建3D模型,而无需折叠模拟。使用距离几何从我们预测的距离矩阵构建3D模型,我们在20个CPU的Linux计算机上在4小时内成功折叠了37个CASP12硬靶标中的21个,其中值家族大小为58个有效序列同源物。相比之下,直接耦合分析(DCA)预测的接触不能折叠任何折叠模拟的情况下,最好的CASP12组折叠其中11个通过整合预测的接触到复杂的,基于片段的折叠模拟。对15个CASP13靶标的严格实验验证表明,在新折叠的3个最难的靶标中,我们的基于距离的折叠服务器成功折叠了2个具有<150个序列同源物的大型靶标,而其他服务器在所有三个靶标上都失败了,并且我们的从头折叠服务器还预测了大型同源建模靶标的最佳,高质量的3D模型。在CAMEO中进一步的实验验证表明,我们的从头折叠服务器预测正确的折叠膜蛋白的新折叠与200个残基和229个序列同源物,而所有其他服务器失败。这些结果表明,深度学习为个人计算机上的从头折叠提供了一种有效而准确的解决方案。

英文摘要:

Contact-assisted protein folding has made very good progress, but two challenges remain. One is accurate contact prediction for proteins lack of many sequence homologs and the other is that time-consuming folding simulation is often needed to predict good 3D models from predicted contacts. We show that protein distance matrix can be predicted well by deep learning and then directly used to construct 3D models without folding simulation at all. Using distance geometry to construct 3D models from our predicted distance matrices, we successfully folded 21 of the 37 CASP12 hard targets with a median family size of 58 effective sequence homologs within 4 hours on a Linux computer of 20 CPUs. In contrast, contacts predicted by direct coupling analysis (DCA) cannot fold any of them in the absence of folding simulation and the best CASP12 group folded 11 of them by integrating predicted contacts into complex, fragment-based folding simulation. The rigorous experimental validation on 15 CASP13 targets show that among the 3 hardest targets of new fold our distance-based folding servers successfully folded 2 large ones with <150 sequence homologs while the other servers failed on all three, and that our ab initio folding server also predicted the best, high-quality 3D model for a large homology modeling target. Further experimental validation in CAMEO shows that our ab initio folding server predicted correct fold for a membrane protein of new fold with 200 residues and 229 sequence homologs while all the other servers failed. These results imply that deep learning offers an efficient and accurate solution for ab initio folding on a personal computer.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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