iQDeep: an integrated web server for protein scoring using multiscale deep learning models
iQDeep: an integrated web server for protein scoring using multiscale deep learning models
复制标题
iQDeep：使用多尺度深度学习模型进行蛋白质评分的集成网络服务器
DOI：
10.1016/j.jmb.2023.168057
复制
发表时间：
2023
影响因子：
5.6
通讯作者：
Bhattacharya, Debswapna
中科院分区：
文献类型：
--
作者：
Shuvo, Md Hossain;Karim, Mohimenul;Bhattacharya, Debswapna
最近在蛋白质结构预测方面取得的显著进展使蛋白质结构的计算建模比以往任何时候都要精确得多。虽然最先进的结构预测方法提供了自己预测的自我评估置信度评分，但仍然需要一个独立的开放获取的蛋白质评分系统，该系统可以应用于广泛的预测建模场景。在这里，我们提出iQDeep，一个集成的和高度可定制的蛋白质评分web服务器，免费提供http://fusion.cs.vt.edu/iQDeep。iQDeep的底层方法采用多尺度深度残差神经网络（ResNets）进行残差级错误分类，然后概率结合这些错误分类进行蛋白质评分。通过调整误差分辨率，我们的方法可以可靠地估计通用蛋白质评分的全局距离测试度量的标准或高精度变体。该方法的性能已在多轮蛋白质结构预测技术关键评估（CASP）实验中进行了广泛的测试，并与最先进的方法进行了比较，包括CASP12和CASP13的基准评估以及CASP14的盲评估。iQDeep web服务器提供了许多方便的功能，包括(i)个人和批量处理模式的选择；（ii）用于自动作业提交、跟踪和结果检索的交互式且保护隐私的网络界面；（iii）对结果进行基于网络的定量和可视化分析，包括总体估计分数及其残差分解，以及各种序列和结构水平特征之间的一致性；（iv）通过网络教程和帮助工具提示提供有关作业提交和结果解释的广泛帮助信息。
The remarkable recent advances in protein structure prediction have enabled computational modeling of protein structures with considerably higher accuracy than ever before. While state-of-the-art structure prediction methods provide self-assessment confidence scores of their own predictions, an independent and open-access system for protein scoring is still needed that can be applied to a broad range of predictive modeling scenarios. Here, we present iQDeep, an integrated and highly customizable web server for protein scoring, freely available at http://fusion.cs.vt.edu/iQDeep. The underlying method of iQDeep employs multiscale deep residual neural networks (ResNets) to perform residue-level error classifications, and then probabilistically combines the error classifications for protein scoring. By adjusting the error resolutions, our method can reliably estimate the standard- or high-accuracy variants of the Global Distance Test metric for versatile protein scoring. The performance of the method has been extensively tested and compared against the state-of-the-art approaches in multiple rounds of Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments including benchmark assessment in CASP12 and CASP13 as well as blind evaluation in CASP14. The iQDeep web server offers a number of convenient features, including (i) the choice of individual and batch processing modes; (ii) an interactive and privacy-preserving web interface for automated job submission, tracking, and results retrieval; (iii) web-based quantitative and visual analyses of the results including overall estimated score and its residue-wise breakdown along with agreements between various sequence- and structural-level features; (iv) extensive help information on job submission and results interpretation via web-based tutorial and help tooltips.