This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was employed based on the ensemble of two complementary coevolution features coupling with deep residual networks. We also improved our multiple sequence alignment (MSA) generation protocol with wholesale meta-genome sequence databases. On 22 CASP14 free modeling (FM) targets, the proposed model achieved a top-L/5 long-range precision of 63.8% and a mean distance bin error of 1.494. Based on the predicted distance potentials, 11 out of 22 FM targets and all of the 14 FM/template-based modeling (TBM) targets have correctly predicted folds (TM-score >0.5), suggesting that our approach can provide reliable distance potentials for ab initio protein folding.

中文翻译：

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通过耦合互补协同进化特征与 CASP14 中的深度残差网络预测蛋白质残基间接触和距离

本文在第 14 届蛋白质结构预测技术关键评估 (CASP14) 中报告并分析了我们的方法对蛋白质接触和距离预测的结果。基于与深度残差网络耦合的两个互补协同进化特征的集合，采用了一种新的基于深度学习的接触/距离预测器。我们还使用批发元基因组序列数据库改进了我们的多序列比对 (MSA) 生成协议。在 22 个 CASP14 自由建模 (FM) 目标上，所提出的模型取得了 top -L/5 远程精度为 63.8%，平均距离 bin 误差为 1.494。基于预测的距离潜力，22 个 FM 目标中的 11 个和所有 14 个 FM/基于模板的建模 (TBM) 目标都正确预测了褶皱（TM-score >0.5），表明我们的方法可以为以下目标提供可靠的距离潜力从头算蛋白质折叠。