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| Home > Publications database > Modelling Ion-Residue Interaction in Implicit Solvation for Intrinsically Disordered Proteins |
| Home > Publications database > Modelling Ion-Residue Interaction in Implicit Solvation for Intrinsically Disordered Proteins |
| Poster (After Call) | FZJ-2021-05061 |
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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.
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