Department of Computer Science, University of Missouri-St. Louis, 312 Express Scripts Hall, St. Louis, MO, USA.
Department of Computer Science, Saint Louis University, 217 Ritter Hall, St. Louis, MO, USA.
BMC Bioinformatics. 2021 Jan 6;22(1):8. doi: 10.1186/s12859-020-03938-z.
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.
蛋白质残基间的接触和距离预测是准确蛋白质结构预测的两个关键中间步骤。距离预测有两种形式:实值距离和“分箱”距离分布图,这是二进制接触预测问题的更细粒度变体。后者已作为 2020 年第 14 届蛋白质结构预测技术评估(CASP14)实验的新挑战引入。尽管最近出现了许多预测距离的方法,但用于评估这些预测的方法却很少。目前仅使用评估整个预测的数值指标,这些指标无法深入了解预测的结构细节。因此,需要新的方法和工具。
我们开发了一个用于评估预测的残基间距离的网络服务器。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。所有这些输出都便于进行视觉和定性评估。该服务器还使用其他指标(如平均绝对误差、均方根误差和接触精度)评估预测。
DISTEVAL 生成的可视化效果相互补充,共同构成了预测接触和距离的定量和定性评估的强大工具,即使没有真实的 3D 结构也是如此。