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@INPROCEEDINGS{deBruyn:903372,
author = {de Bruyn, Emile and Zimmermann, Olav and Grohe, Martin and
Rossetti, Giulia},
title = {{M}odelling {I}on-{R}esidue {I}nteraction in {I}mplicit
{S}olvation for {I}ntrinsically {D}isordered {P}roteins},
school = {RWTH Aachen},
reportid = {FZJ-2021-05061},
year = {2021},
abstract = {This project aims to develop a new implicit solvent model
that enables rapid and accurate exploration of the
conformational space of Intrinsically Disordered Proteins
(IDPs).IDPs are characterised by their structural
heterogeneity and visit diverse and transient conformational
ensembles. These proteins have attracted broad interest due
to their ubiquitous involvement in biological function and
dysfunction, including promising therapeutic targets. This
makes characterisation of IDPs' structure-function
relationships both essential and challenging.Molecular
Dynamics (MD) simulations are commonly used to explore the
conformational landscape of IDPs. These explorations mostly
overlook interactions between ions and solvent with
individual amino acid residues that reshape the energy
landscape and drive ensemble switching in IDPs. MD
simulations are also unfortunately slow to stabilise complex
aggregates of solvent and solutes, even though these
simulations are drastically reduced in complexity compared
to biological conditions. Ion motions have been identified
as a rate-determining step in the already large free energy
surface sample required, and as such impede the exploration
and study of IDP dynamics. Approaches to avoid this rate
determining step exist: coarse-grained models combined with
Monte Carlo (MC) simulation approaches have already proven
valuable in characterising several IDPs. Achieving finer
accuracy through atomistic MC simulations require implicit
or continuum solvent, which at present insufficiently model
the interactions between ions and residues, and therefore
cannot accurately explore IDPs’ conformational
landscape.To accurately model the effect of ions on
individual amino acid residues, this project uses existing
trajectory data and data from highly time-resolved
state-of-the-art MD simulations. The positional data within
MD trajectories are used as input for Machine Learning to
obtain data-driven parameters for a new implicit solvent
model.},
month = {Oct},
date = {2021-10-05},
organization = {INM $\&$ IBI Retreat 2021, Jülich
(Germany), 5 Oct 2021 - 6 Oct 2021},
subtyp = {After Call},
cin = {JSC / IAS-5 / INM-9},
cid = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IAS-5-20120330 /
I:(DE-Juel1)INM-9-20140121},
pnm = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
and Research Groups (POF4-511) / HDS LEE - Helmholtz School
for Data Science in Life, Earth and Energy (HDS LEE)
(HDS-LEE-20190612)},
pid = {G:(DE-HGF)POF4-5112 / G:(DE-Juel1)HDS-LEE-20190612},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/903372},
}