10
IRUS Total
Downloads
  Altmetric

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

File Description SizeFormat 
gkab350.pdfPublished version2.28 MBAdobe PDFView/Open
Title: ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules
Authors: Mersmann, S
Stromich, L
Song, F
Wu, N
Vianello, F
Barahona, M
Yaliraki, S
Item 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.
Issue Date: 2-Jul-2021
Date of Acceptance: 22-Apr-2021
URI: http://hdl.handle.net/10044/1/89402
DOI: 10.1093/nar/gkab350
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.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Wellcome Trust
Funder's Grant Number: EP/N014529/1
108908/B/15/Z
Keywords: Developmental Biology
05 Environmental Sciences
06 Biological Sciences
08 Information and Computing Sciences
Publication Status: Published
Online Publication Date: 2021-05-12
Appears in Collections:Mathematics
Chemistry
Biological and Biophysical Chemistry
Applied Mathematics and Mathematical Physics
Faculty of Natural Sciences



This item is licensed under a Creative Commons License Creative Commons