Protein inter-residue contact and distance prediction are two key intermediate steps essential to accurate protein structure prediction. Distance prediction comes in two forms: real-valued distances and ‘binned’ distograms, which are a more finely grained variant of the binary contact prediction problem. The latter has been introduced as a new challenge in the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14) 2020 experiment. Despite the recent proliferation of methods for predicting distances, few methods exist for evaluating these predictions. Currently only numerical metrics, which evaluate the entire prediction at once, are used. These give no insight into the structural details of a prediction. For this reason, new methods and tools are needed. We have developed a web server for evaluating predicted inter-residue distances. Our server, DISTEVAL, accepts predicted contacts, distances, and a true structure as optional inputs to generate informative heatmaps, chord diagrams, and 3D models. All of these outputs facilitate visual and qualitative assessment. The server also evaluates predictions using other metrics such as mean absolute error, root mean squared error, and contact precision. The visualizations generated by DISTEVAL complement each other and collectively serve as a powerful tool for both quantitative and qualitative assessments of predicted contacts and distances, even in the absence of a true 3D structure.

中文翻译：

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DISTEVAL：用于评估预测蛋白质距离的Web服务器

蛋白质残基间的接触和距离预测是准确预测蛋白质结构必不可少的两个关键中间步骤。距离预测有两种形式：实值距离和“绑定”距离图，它们是二进制接触预测问题的更精细的变体。后者已在2020年第14届蛋白质结构预测技术关键评估（CASP14）实验中作为新挑战引入。尽管最近用于预测距离的方法激增，但是很少有用于评估这些预测的方法。当前，仅使用一次评估整个预测的数值度量。这些都无法洞悉预测的结构细节。因此，需要新的方法和工具。我们已经开发了一个Web服务器，用于评估预测的残基间距离。我们的服务器DISTEVAL接受预测的接触，距离和真实结构作为可选输入，以生成信息丰富的热图，弦图和3D模型。所有这些输出有助于视觉和定性评估。服务器还使用其他度量标准评估预测，例如平均绝对误差，均方根误差和接触精度。即使在没有真正的3D结构的情况下，由DISTEVAL生成的可视化视图也可以相互补充，并共同充当用于预测接触和距离的定量和定性评估的强大工具。和3D模型。所有这些输出有助于视觉和定性评估。服务器还使用其他度量标准评估预测，例如平均绝对误差，均方根误差和接触精度。即使在没有真正的3D结构的情况下，由DISTEVAL生成的可视化视图也可以相互补充，并共同充当用于预测接触和距离的定量和定性评估的强大工具。和3D模型。所有这些输出有助于视觉和定性评估。服务器还使用其他度量标准评估预测，例如平均绝对误差，均方根误差和接触精度。即使在没有真正的3D结构的情况下，由DISTEVAL生成的可视化视图也可以相互补充，并共同充当用于预测接触和距离的定量和定性评估的强大工具。