School of Mathematical Sciences, Nankai University, Tianjin, 300071, China.
Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
Adv Sci (Weinh). 2021 Dec;8(24):e2102592. doi: 10.1002/advs.202102592. Epub 2021 Oct 31.
The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure prediction is presented. The major improvement over trRosetta consists of two folds. The first is the application of a new multi-scale network, i.e., Res2Net, for improved prediction of inter-residue geometries, including distance and orientations. The second is an attention-based module to exploit multiple homologous templates to increase the accuracy further. Compared with trRosetta, trRosettaX improves the contact precision by 6% and 8% on the free modeling targets of CASP13 and CASP14, respectively. A preliminary version of trRosettaX is ranked as one of the top server groups in CASP14's blind test. Additional benchmark test on 161 targets from CAMEO (between Jun and Sep 2020) shows that trRosettaX achieves an average TM-score ≈0.8, outperforming the top groups in CAMEO. These data suggest the effectiveness of using the multi-scale network and the benefit of incorporating homologous templates into the network. The trRosettaX algorithm is incorporated into the trRosetta server since Nov 2020. The web server, the training and inference codes are available at: https://yanglab.nankai.edu.cn/trRosetta/.
近年来，由于深度学习技术的引入，从头蛋白质结构预测的准确性得到了极大的提高。在这项工作中，提出了 trRosettaX，这是一种用于蛋白质结构预测的 trRosetta 的改进版本。与 trRosetta 相比，trRosettaX 的主要改进有两个方面。第一个是应用新的多尺度网络，即 Res2Net，用于改进残基间几何形状的预测，包括距离和方向。第二个是基于注意力的模块，用于利用多个同源模板进一步提高准确性。与 trRosetta 相比，trRosettaX 分别将 CASP13 和 CASP14 的自由建模目标的接触精度提高了 6%和 8%。trRosettaX 的初步版本在 CASP14 的盲测中被评为顶级服务器组之一。在 2020 年 6 月至 9 月的 CAMEO 上对 161 个目标进行的额外基准测试表明，trRosettaX 的平均 TM 分数≈0.8，优于 CAMEO 中的顶级组。这些数据表明了使用多尺度网络的有效性以及将同源模板纳入网络的益处。自 2020 年 11 月以来，trRosettaX 算法已被纳入 trRosetta 服务器中。该网络服务器、训练和推理代码可在以下网址获得：https://yanglab.nankai.edu.cn/trRosetta/。
