Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: https://twitter.com/mzs0149.
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States. Electronic address: https://twitter.com/CaptainRafi97.
J Mol Biol. 2023 Jul 15;435(14):168057. doi: 10.1016/j.jmb.2023.168057. Epub 2023 Mar 23.
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.
近年来，蛋白质结构预测取得了显著进展，使得能够以前所未有的更高精度对蛋白质结构进行计算建模。虽然最先进的结构预测方法为其自身预测提供了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，可以应用于广泛的预测建模场景。在这里，我们展示了 iQDeep，这是一个用于蛋白质评分的集成和高度可定制的网络服务器，可在 http://fusion.cs.vt.edu/iQDeep 上免费获得。iQDeep 的基础方法采用多尺度深度残差神经网络（ResNets）来执行残基级错误分类，然后概率地组合用于蛋白质评分的错误分类。通过调整错误分辨率，我们的方法可以可靠地估计 Global Distance Test 度量标准的标准或高精度变体，以实现多功能蛋白质评分。该方法的性能已经在多轮蛋白质结构预测技术评估（Critical Assessment of Techniques for Protein Structure Prediction，CASP）实验中进行了广泛测试和比较，包括在 CASP12 和 CASP13 中的基准评估以及在 CASP14 中的盲评估。iQDeep 网络服务器提供了许多方便的功能，包括：（i）选择单个和批处理模式；（ii）用于自动作业提交、跟踪和结果检索的交互式和隐私保护的网络界面；（iii）基于网络的结果的定量和可视化分析，包括总体估计分数及其残基分解，以及各种序列和结构水平特征之间的一致性；（iv）通过基于网络的教程和帮助工具提示，提供有关作业提交和结果解释的大量帮助信息。
