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@article{Mersmann:2021:nar/gkab350,
author = {Mersmann, S and Stromich, L and Song, F and Wu, N and Vianello, F and Barahona, M and Yaliraki, S},
doi = {nar/gkab350},
journal = {Nucleic Acids Research},
pages = {W551--W558},
title = {ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules},
url = {http://dx.doi.org/10.1093/nar/gkab350},
volume = {49},
year = {2021}
}
TY - JOUR
AB - 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.
AU - Mersmann,S
AU - Stromich,L
AU - Song,F
AU - Wu,N
AU - Vianello,F
AU - Barahona,M
AU - Yaliraki,S
DO - nar/gkab350
EP - 558
PY - 2021///
SN - 0305-1048
SP - 551
TI - ProteinLens: a web-based application for the analysis of allosteric signalling on atomistic graphs of biomolecules
T2 - Nucleic Acids Research
UR - http://dx.doi.org/10.1093/nar/gkab350
UR - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab350/6274523
UR - http://hdl.handle.net/10044/1/89402
VL - 49
ER -