ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
The investigation of allosteric effects in biomolecular structures is of great current interest in diverse areas, from fundamental biological enquiry to drug discovery. Here we present ProteinLens, a user-friendly and interactive web application for the investigation of allosteric signalling based on atomistic graph-theoretical methods. Starting from the PDB file of a biomolecule (or a biomolecular complex) ProteinLens obtains an atomistic, energy-weighted graph description of the structure of the biomolecule, and subsequently provides a systematic analysis of allosteric signalling and communication across the structure using two computationally efficient methods: Markov Transients and bond-to-bond propensities. ProteinLens scores and ranks every bond and residue according to the speed and magnitude of the propagation of fluctuations emanating from any site of choice (e.g. the active site). The results are presented through statistical quantile scores visualised with interactive plots and adjustable 3D structure viewers, which can also be downloaded. ProteinLens thus allows the investigation of signalling in biomolecular structures of interest to aid the detection of allosteric sites and pathways. ProteinLens is implemented in Python/SQL and freely available to use at: www.proteinlens.io.
Date Issued
2021-07-02
Date Acceptance
2021-04-22
Citation
Nucleic Acids Research, 2021, 49 (W1), pp.W551-W558
ISSN
0305-1048
Publisher
Oxford University Press
Start Page
W551
End Page
W558
Journal / Book Title
Nucleic Acids Research
Volume
49
Issue
W1
Copyright Statement
© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Wellcome Trust
Identifier
https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab350/6274523
Grant Number
EP/N014529/1
108908/B/15/Z
Subjects
Developmental Biology
05 Environmental Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status
Published
Date Publish Online
2021-05-12