eMap 2.0: A web-based platform for identifying electron transfer pathways in proteins and protein families

In this review we present eMap 2.0, a web-based application for predicting electron/hole transfer pathways in proteins and protein families based on their structures. The underlying model can be viewed as a coarse-grained version of the Pathways approach by Beratan and Onuchic [Beratan et al. J. Chem. Phys. 1987, 86, 4488]. Similar to the original framework, eMap employs graph-theory algorithms to search for the most efficient electron transfer pathways as shortest paths on a graph representation of the protein. What distinguishes eMap from the original formulation is that the nodes represent electron transfer active sites instead of atoms and only through-space tunneling is considered for each individual electron/hole hop. eMap 2.0 takes one step further by aiming at identifying shared electron transfer pathways in protein sets. From a graph theory standpoint, this is achieved using frequent subgraph mining (FSM) algorithms. Lastly, eMap 2.0 utilizes sequence and structural similarity measures to analyze and cluster the results. Here, we show how this robust method can be utilized to rapidly provide insights regarding conserved electron transfer pathways within protein families and to identify outliers in protein families, in which the conserved electron transfer pathway is blocked either by a mutation or conformational changes.