HeMoQuest: a webserver for qualitative prediction of transient heme binding to protein motifs

Abstract

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.

Linking COVID-19 and heme-driven pathophysiologies: A combined computational-experimental approach

“…The concordance of the expression patterns in these datasets with each relation is shown in Additional le 5. Screening For Potential HBMS In Covid-19-related Proteins HeMoQuest (http://131.220.139.55/SeqDHBM/) [55] was used to identify potential HBMs in proteins of SARS-Cov-2 (i.e., S protein, M protein, E protein, protein 7a) and human host cells (i.e., ACE2, TMPRSS2). HBM prediction was re ned considering surface accessibility, glycosylation sites and the involvement in disul de bonds.…”

“…We identi ed potential HBMs in all target proteins using the recently published machine-learning web application HeMoQuest [55], which predicts HBMs from primary structure and was trained on a large array of heme-binding peptides. Screening of the amino acid sequences of S protein, protein 7a, ACE2 and TMPRSS2 resulted in 50, 6, 21, and 32 potential HBMs, respectively.…”

“…We provide insights into pathways that might play a role when considering heme in the context of COVID-19 infections by superimposing the heme KG [42] and the COVID-19 KG [54]. Furthermore, we investigated the interaction of heme with select SARS-CoV-2 proteins and speci c host cell proteins by applying our established strategy employing the hemebinding motif (HBM) prediction system HeMoQuest [55] and experimental studies to characterize the computationally predicted heme-binding sites in the viral and host proteins.…”

Insights into the Roles of the Sideroflexins/SLC56 Family in Iron Homeostasis and Iron-Sulfur Biogenesis

“…Whether SFXN1 could bind to heme and help in its trafficking is another hypothesis that merits our attention. We thus seek heme binding motifs (HBMs) in SFXN1 with the HeMoQuest tool dedicated to the prediction of heme-coordination sites in protein sequences [ 77 , 78 ] and we found four HBMs that are solvent-accessible ( Figure A1 , Appendix A ). These predicted HBM may permit transient interactions between heme and SFXN1.…”

Model-driven design of Mycoplasma as a vaccine chassis

“…The investigation of this process requires computational protein domain analyses, complemented with mathematical models that allow the identification of the protein motif responsible for the synthesis of the compound. Several methods can be used to build protein motifs relative to a specific function: recently, many machine learning and deep learning methods have been developed for this purpose, mostly with the aim of identifying binding sites [264][265][266][267] . For our purpose, i.e.…”