SNIC SUPR
Modeling of proteins with low-resolution experimental data
Dnr:

SNIC 2020/3-37

Type:

SNIC Large Compute

Principal Investigator:

Erik Lindahl

Affiliation:

Stockholms universitet

Start Date:

2021-01-01

End Date:

2022-01-01

Primary Classification:

10603: Biophysics

Secondary Classification:

10602: Biochemistry and Molecular Biology

Tertiary Classification:

10407: Theoretical Chemistry

Allocation

Abstract

The purpose of our work is to understand how channels and other proteins move between conformations, how large motions and allosteric modulation are induced by small external factors (e.g. ligand binding, changes in membrane potential, or lipid composition), and to develop techniques to combine several sources of experimental data such as cryo-EM, electrophysiology and neutron scattering with bioinformatics and simulation to create models of the entire ensemble of structural and functional states for ion channels and transporters. Our computational work supported by SNIC enables us to build atomic-resolution models to study features not readily available in experiments, such as transitions between states and finding new ways to use low-resolution experimental data. Our primary model system is the pentameric ligand-gated ion channels responsible for mediating chemical singling across the synapse in the nervous system, as well as their bacterial homologs. While we increasingly have structural data for these channels, we still do not understand the molecular mechanism of gating, and the desensitised state all channels end up in shortly after activation is still almost entirely uncharacterised. Here, we propose to use molecular dynamics simulations in combination with Markov State Models to determine both the free energy landscape of channel activation, as well as the kinetics of transitions between states, and how this is changed by allosteric modulation. We will similarly use simulations to explain the differences in molecular interactions between positive (enhancing) and negative (dampening) allosteric modulators, to identify state-specific binding, and understand the interplay between lipid and small-molecule modulation of ion channels. As part of this work, we will also attempt to develop new techniques that enable us to use molecular simulations to screen weak lipid binding sites on membrane protein surfaces by using meta-MSMs (where each binding site is a state in the model for each lipid) and calculate approximate binding/interaction energies even for these large molecules. We will also combine cryo-EM data with simulations, and use new methods to apply Bayesian restraints to proteins based on intermediate-resolution electron density. Many of these structures are not well represented with a single all-atom model in blurred regions of large conformational flexibility or low resolution, but our approach provides an ensemble of conformations most likely to have generated the experimental data, instead of a single model. We will also use simulations to compute small-angle neutron scattering (SANS) spectra from open/closed-state channels and compare with experimental SANS data we are collecting in Grenoble. While SANS itself is extremely low resolution (essentially just describing shape), it provides the first accurate data about channel structure at room temperature, and comparing to simulations of existing structures could enable us to determine how well these correspond to functional channels, and in particular help us characterise the rapid desensitisation transitions occurring at room temperature in cells, but this far not explained with X-ray or cryo-EM structures.