Pharmaceutical Biochemistry and Bioanalytics, Pharmaceutical Institute, An der Immenburg 4, University of Bonn, 53121, Bonn, Germany.
BMC Bioinformatics. 2020 Mar 27;21(1):124. doi: 10.1186/s12859-020-3420-2.
The notion of heme as a regulator of many physiological processes via transient binding to proteins is one that is recently being acknowledged. The broad spectrum of the effects of heme makes it important to identify further heme-regulated proteins to understand physiological and pathological processes. Moreover, several proteins were shown to be functionally regulated by interaction with heme, yet, for some of them the heme-binding site(s) remain unknown. The presented application HeMoQuest enables identification and qualitative evaluation of such heme-binding motifs from protein sequences.
We present HeMoQuest, an online interface (http://bit.ly/hemoquest) to algorithms that provide the user with two distinct qualitative benefits. First, our implementation rapidly detects transient heme binding to nonapeptide motifs from protein sequences provided as input. Additionally, the potential of each predicted motif to bind heme is qualitatively gauged by assigning binding affinities predicted by an ensemble learning implementation, trained on experimentally determined binding affinity data. Extensive testing of our implementation on both existing and new manually curated datasets reveal that our method produces an unprecedented level of accuracy (92%) in identifying those residues assigned "heme binding" in all of the datasets used. Next, the machine learning implementation for the prediction and qualitative assignment of binding affinities to the predicted motifs achieved 71% accuracy on our data.
Heme plays a crucial role as a regulatory molecule exerting functional consequences via transient binding to surfaces of target proteins. HeMoQuest is designed to address this imperative need for a computational approach that enables rapid detection of heme-binding motifs from protein datasets. While most existing implementations attempt to predict sites of permanent heme binding, this application is to the best of our knowledge, the first of its kind to address the significance of predicting transient heme binding to proteins.
通过与蛋白质的瞬时结合来调节许多生理过程的血红素的概念最近才被人们所认识。血红素的广泛作用使其有必要进一步确定血红素调节蛋白，以了解生理和病理过程。此外，一些蛋白质被证明可以通过与血红素的相互作用而在功能上受到调节，但对于其中一些蛋白质，血红素结合位点仍未知。本研究提出的 HeMoQuest 应用程序可从蛋白质序列中鉴定和定性评估这种血红素结合基序。
我们提出了 HeMoQuest，这是一个在线界面（http://bit.ly/hemoquest），提供了两种不同的定性算法。首先，我们的实现可以快速检测到从输入的蛋白质序列中瞬时结合非肽基序的血红素。此外，通过分配基于实验确定的结合亲和力数据训练的集成学习实现预测的结合亲和力，可以定性地评估每个预测基序结合血红素的潜力。在现有的和新的人工 curated 数据集上对我们的实现进行广泛测试，结果表明我们的方法在识别所有使用的数据集“血红素结合”的残基时，达到了前所未有的精度（92%）。接下来，对预测和定性分配预测基序的结合亲和力的机器学习实现，在我们的数据上达到了 71%的准确性。
血红素作为一种调节分子，通过与靶蛋白表面的瞬时结合来发挥功能作用。HeMoQuest 的设计目的是满足对一种计算方法的迫切需求，这种方法可以从蛋白质数据集中快速检测血红素结合基序。虽然大多数现有的实现方法都试图预测永久血红素结合位点，但就我们所知，该应用程序是第一个解决预测蛋白质中瞬时血红素结合意义的方法。
