Motivation Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. Results Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single- and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive with other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. Availability and implementation http://yanglab.nankai.edu.cn/QDistance. Supplementary information Supplementary data are available at Bioinformatics online.

中文翻译：

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使用预测的残基间距离改进模型质量估计

动机蛋白质模型质量评估 (QA) 是蛋白质结构预测的重要组成部分，其目的是在不知道天然结构的情况下估计结构模型的质量和/或从结构模型池中选择最准确的模型。QA 仍然是蛋白质结构预测中的一项具有挑战性的任务。结果 基于最近基于深度学习的结构预测算法 trRosetta 预测的残基间距离，我们开发了 QDistance，这是一种估计全局和局部质量的新方法。QDistance 适用于单模型和多模型输入。我们设计了几个基于距离的特征来评估预测的和模型衍生的残基间距离之间的一致性。连同一些广泛使用的功能，它们被输入到一个简单而强大的线性回归模型中，以推断出全球 QA 分数。每个结构模型的本地 QA 分数是根据与一组选定参考模型的比较分析预测的。对于多模型输入，参考模型是根据预测的全局 QA 分数从输入中选择的。对于单模型输入，参考模型由 trRosetta 预测。凭借信息丰富的基于距离的特征，QDistance 可以以令人满意的准确度预测全局质量。CASP13 和 CAMEO 结构模型的基准测试表明 QDistance 与其他方法相比具有竞争力。CASP14 实验中的盲测表明 QDistance 是稳健的，并且在预测因子中名列前茅。尤其，QDistance 是排名前三的局部 QA 方法，对不可靠的局部区域做出了最准确的局部 QA 预测。分析表明，这种优异的性能可归因于包含预测的残基间距离。可用性和实施​​ http://yanglab.nankai.edu.cn/QDistance。补充信息 补充数据可在 Bioinformatics 在线获取。