SASSIE: Modelling AUC & SAS Data Using Atomistic Simulations

25th July 2017

David Wright & Emre Brookes

Molecular modelling and simulation: What is it?

  • Theoretical and computational methods used to model or mimic the behaviour or properties of molecules
  • Atomistic representation of molecules
  • Encode chemistry
  • Use classical or quantum mechanics to describe interactions

Molecular modelling and simulation: What is it good for?

  • Understanding experimental results
  • Combining information from multiple experiments
  • Providing atomistic explanations for higher level observations

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What do we need to model AUC & SAS experiments?

  • Initial models capturing known chemistry
  • Variety of physically plausible conformations
    • Global structure
    • Domain level rearrangements
  • Atomistic models we can analyse

Molecular Dynamics

Classical (Newtonian) dynamics

F = m a

Forcefield description of interactions

F=∇ U

Molecular Dynamics

MD

Experiment

Model system with N particles, solve F = ma until properties do not change (equilibrate) then you “measure” (i.e. average a property) until data converge

Sample in instrument, measure over time ... measure longer until data converge.

Molecular Dynamics

MD and experiment can both suffer from the similar issues

Experiment MD
Sample not prepared correctly Incorrect starting model structure
Measurement too short Simulation too short
System undergoes irreversible change (aggregation etc.) Structure trapped in local minima
Didn’t quite measure what we thought Bug in your analysis code

Molecular Dynamics

Requirements

  • Initial structure
  • Forcefield

Output

  • Trajectory of coordinates

Molecular Dynamics

  • Time step determined by fastest motion
  • Usually X-H bond
  • Even fastest supercomputers allow only microsecond simulations in general

Monte Carlo Simulation

  • Vary system
  • Evaluate energy
  • Keep new structure if
    1. ​More energetically favourable or
    2. With probability related to energy change
  • Repeat first step

Dihedral Angle Monte Carlo Simulation

Frenkel and Smit, Understanding Molecular Simulation

Dihedral Angle Monte Carlo Simulation

  • MC with all degrees of freedom varied at least as slow as MD
  • "Freezing" some degrees of freedom potentially gives a huge speedup
  •  Sample only the dihedral potential using the Metropolis criterion
    • Dihedral Angle Monte Carlo

Dihedral Angle Monte Carlo

8 cores

13 days

1 core

15 mins

  • Rapid generation of ensemble
  • Sampling Ramachandran obviously limiting
  • Use as hypothesis to test against data

SASSIE

Simple unified interface to tools facilitating:

  • Initial model building/preparation
  • Simulation
    • ​Dihedral angle Monte Carlo
    • Molecular dynamics
    • Torsion angle MD
  • Calculation
    • ​SAS curves
    • AUC profiles
  • ​Comparison to experimental data

Further Reading

Full three day training course (today is similar to day 2):

https://sassie-web.chem.utk.edu/training/uk_2017/main.html

MD

A. Leach, Molecular Modelling: Principles and Applications

J. D. Durrant & J. A. McCammon, Molecular dynamics simulations and drug discovery, BMC Biology, 2011, 9:71, DOI: 10.1186/1741-7007-9-71

Dihedral Angle Monte Carlo

J. E. Curtis et al, SASSIE: A program to study intrinsically disordered biological molecules and macromolecular ensembles using experimental scattering restraints, Computer Physics Communications, 2012, 183:2, DOI: 10.1016/j.cpc.2011.09.010

Torsion Angle MD

W. Zhang et al, Combined Monte Carlo/torsion-angle molecular dynamics for ensemble modeling of proteins, nucleic acids and carbohydrates, J Mol Graph Model, 2017, 73, DOE: 10.1016/j.jmgm.2017.02.010

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