BACKGROUND 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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HeMoQuest：用于定性预测瞬时血红素与蛋白质基序结合的网络服务器。

背景技术血红素作为通过与蛋白质的瞬时结合的许多生理过程的调节剂的概念是最近被公认的一种。血红素作用的广谱性使得重要的是鉴定进一步的血红素调节蛋白以了解生理和病理过程。而且，显示出几种蛋白质通过与血红素相互作用而在功能上受到调节，但是，对于其中一些，血红素结合位点仍然未知。提出的应用程序HeMoQuest可以从蛋白质序列中识别和定性评估此类血红素结合基序。结果我们介绍了HeMoQuest，它是一种在线算法（http://bit.ly/hemoquest），可为用户提供两种明显的质量优势。第一，我们的实现从输入的蛋白质序列中快速检测到瞬时的血红素与非肽基序的结合。另外，通过分配由整体学习实施方式预测的结合亲和力，在实验确定的结合亲和力数据上进行训练，定性地评估了每个预测的基序与血红素结合的可能性。在现有数据集和新的人工整理数据集上对我们的实现进行的广泛测试表明，我们的方法在识别所有使用的数据集中分配为“血红素结合”的残基时，产生了前所未有的准确性（92％）。接下来，用于将绑定亲和力与预测的图案进行预测和定性分配的机器学习实现，在我们的数据上达到71％的准确性。结论血红素起着调节分子的作用，它通过与靶蛋白表面的瞬时结合发挥功能性作用。HeMoQuest旨在满足这种对计算方法的迫切需求，该计算方法能够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都试图预测永久性血红素结合的位点，但据我们所知，这是第一个解决预测短暂的血红素与蛋白质结合的重要性的应用。