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ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules

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posted on 2020-05-28, 18:52 authored by Sophia Mersmann, Léonie StrömichLéonie Strömich, Florian Song, Francesca Vianello, Mauricio Barahona, Sophia N. Yaliraki
Allostery describes the effect of a distant binding event towards the orthosteric site activity of the same protein. Drug discovery efforts targeting allosteric modes of action have gained traction over the past decades, as allosteric binding sites provide selectivity and sensitivity advantages, especially in large protein families. However, allosteric site discovery has traditionally been serendipitous or connected to time- and effort-intensive high-throughput screenings. Computational methodologies that can provide a more targeted approach to investigate allosteric effects in protein structures are of great interest.

We present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric sites and pathways based on atomistic graph-theoretical methods. ProteinLens first obtains an atomistic, energy-weighted graph description of biomolecular structures and subsequently provides access to two computationally efficient methods for the analysis of allosteric signalling and communication, Markov transients and Bond-to-bond propensities. We obtain an atomistic graph from the 3D coordinates of proteins or protein/DNA complexes and capture the physicochemical detail by first detecting and then weighting the graph edges according to covalent and weak interactions (hydrogen bonds, electrostatic and hydrophobic interactions) between atoms. ProteinLens then further implements and scores bonds and residues through Markov transient and Bond-to-bond propensitiy analysis. The results of both methodologies are presented to the user with interactive plots and adjustable 3D structure viewers, which can also be downloaded. A putative allosteric site can be further scored in comparison to a random site through bootstrapping. Thus, ProteinLens allows the investigation of communication in structures of interest and the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at


We acknowledge the Engineering and Physical Sciences Research Council (EPSRC) award EP/N014529/1 supporting the EPSRC Centre for Mathematics of Precision Healthcare, award EP/L015498/1 and the Wellcome Trust grant award 215360/Z/19/Z.