The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to sequence information) both language model embeddings and template information weighted by sequence similarity to the target. We also developed a refinement pipeline that recombines models generated by template-free and template utilizing versions of trRosetta guided by the DeepAccNet accuracy predictor. Both benchmark tests and CASP results show that the new pipeline is a considerable improvement over the original trRosetta, and it is faster and requires less computing resources, completing the entire modeling process in a median < 3 h in CASP14. Our human group improved results with this pipeline primarily by identifying additional homologous sequences for input into the network. We also used the DeepAccNet accuracy predictor to guide Rosetta high-resolution refinement for submissions in the regular and refinement categories; although performance was quite good on a CASP relative scale, the overall improvements were rather modest in part due to missing inter-domain or inter-chain contacts.

中文翻译：

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在 CASP14 中使用深度学习和 Rosetta 进行蛋白质三级结构预测和细化

trRosetta 结构预测方法采用深度学习来生成预测的残基-残基距离和方向分布，从中构建 3D 模型。我们试图通过将语言模型嵌入和由与目标的序列相似性加权的模板信息作为输入（除了序列信息）来改进该方法。我们还开发了一个细化管道，它重新组合由无模板和模板利用版本的 trRosetta 生成的模型，由 DeepAccNet 准确度预测器引导。基准测试和 CASP 结果都表明，新的管道比原来的 trRosetta 有了相当大的改进，而且速度更快，需要的计算资源更少，在 CASP14 中完成整个建模过程的中位时间 < 3 h。我们的人类小组主要通过识别额外的同源序列以输入到网络中来改进该管道的结果。我们还使用 DeepAccNet 准确率预测器来指导 Rosetta 对常规和细化类别中提交的高分辨率细化；尽管在 CASP 相对规模上性能相当不错，但整体改进相当有限，部分原因是缺少域间或链间联系。