Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemiareperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme's role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme's interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology.

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 hemebinding 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.

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In hemolytic disorders, erythrocyte lysis results in massive release of hemoglobin and, subsequently, toxic heme. Hemopexin is the major protective factor against heme toxicity in human blood and currently considered for therapeutic use. It has been widely accepted that hemopexin binds heme with extraordinarily high affinity of <1 pM in a 1:1 ratio. However, several lines of evidence point to a higher stoichiometry and lower affinity than determined 50 years ago. Here we re-analyzed this data. SPR and UV/Vis spectroscopy were used to monitor the interaction of heme with the human protein. The heme-binding sites of hemopexin were characterized using hemopexin-derived peptide models and competitive displacement assays. We obtained a KD value of 0.32 ± 0.04 nM and the ratio for the interaction was determined to be 1:1 at low heme concentrations and at least 2:1 (heme:hemopexin) at high concentrations. We were able to identify two yet unknown potential heme-binding sites on hemopexin. Furthermore, molecular modelling with a newly created homology model of human hemopexin suggested a possible recruiting mechanism by which heme could consecutively bind several histidine residues on its way into the binding pocket. Our findings have direct implications for the potential administration of hemopexin in hemolytic disorders.