Topic 2 Flashcards
-Describe the similarities and differences between all-atom and coarse-grained force fields. -Explain how coarse-graining attempts to address the length and timescale problems in molecular dynamics -Compare three different methods for developing coarse-grained interaction potentials/force fields -Evaluate a coarse-grained simulation study of a biological system
1
Q
Briefly discuss and compare three different force-fields, using the alcohol octanol as an example.
A
- All-atom: An all atom/atomistic force-field treats each atom explicitly. At every step, the simulation must input all bonding/non-bonding interactions in a system. In long molecules like octanol, this can lead to many dihedral angles needing to be specified for all differing ~-OH chains. Very precise however, computationally costly.
- United-atom: cheaper way to describe the system that saves computational time, but still expensive. It does this by grouping less chemically involved hydrogen atoms into CH2 groups. Good for lipid descriptions
- Coarse graining: 4-5 heavy atoms with their H’s are grouped into a single interaction site, saving large amount of computational effort. Octanol is split into a polar (hydrophilic) and non-polar (hydrophobic) half. Lose a degree of chemical detail as cannot capture hydrogen bonding explicitly. However partial charges assigned to these blocks to describe electrostatic and non-bonding interactions.
2
Q
Using a course-grained forcefield allows simulation of larger … for … timescales than with all-atom forcefields, giving a higher chance of overcoming … …
A
Using a course-grained forcefield allows simulation of larger systems for longer timescales than with all-atom forcefields, giving a higher chance of overcoming kinetic barriers.
3
Q
What are three ways in which coarse graining allow simualtion on larger systems for longer timescales?
A
- Decrease in total number of particles N
- Larger time step
- Smoother energy landscape
4
Q
How does a decrease in total number of particles N ‘speed up’ a coarse grain simulation?
A
- A decrease in total number of particles reduces the number of force calculations required at each step.
- Resulting in less computational time needed to move the system forward one-time step, as less forces and energies must be calculated each step.
5
Q
How does larger time-step ‘speed up’ a coarse grain simulation?
A
- A time step for integration needs to be smaller than the highest frequency motion in a system.
- The verlet algorithm can be used to move an atom through calculation of the force, with respect to all other atoms, to apply in one timestep, which should be constant.
- A time step may be suitable for bond stretch of a low frequency (left), resulting in it being closer to its equilibrium value. However, the same force acting on a higher frequency motion (right) results in a high energy final position, leading to assumption of force being constant over our chosen timestep to be invalid.
- Timesteps should be 10x smaller than highest frequency motion, which are generally bond stretches of light hydrogen atoms.
- By removing bonds involving them, a larger time step can be used, as particles of higher mass oscillate slower. Each time step now propagates us further forward in time.
6
Q
How does a smoother energy landscape ‘speed up’ coarse graining?
A
- Rugged energy landscape of an atomistic model has many local minima.
- Averaging that occurs through coarse-graining results in a smoother energy landscape with less local trapping/friction and faster dynamics.
- Beads can slide over each other much easier
7
Q
Why must we develop new interaction potentials for coarse grained systems?
A
- Coarse-grained particles do not interact in the same way that atoms do.
- New coarse-grained potentials must be developed that are unique to each system.
- Parameterising is a large challenge as takes a lot of time to predict the correct output.
8
Q
What are two methods of generating course-grained interaction potentials?
A
- Use an analytical functional form with associated parameters; like atomistic interaction potentials but with CG beads
- Numerically fitting data from atomistic simulations using tabulated potentials (list of r’s between atoms with corresponding F’s/E’s – useful for complex systems where don’t have analytical function)
9
Q
What is mapping, and why is it essential for developing a CGP? (PPQ)
A
- The first stage in developing a CG interaction potential (CGP) is to define mapping of the atomistic system in to a CG one.
- Essential for new molecules and there are many ways to assign atomistic structure depending on what interactions are expected to be the focus.
10
Q
What are 2 general rules of mapping?
A
- Each bead will inherit the mass/charge of its constituents
- Must maintain the overall shape of the molecule (otherwise may not replicate the correct self-assembled structure)
11
Q
Outline how you would produce a CG trajectory from an atomistic simulation using a bottom up approach
A
- First, run a reference atomistic simulation to use as our benchmark (however does not necessarily equal experimental/ab initio PES)
- Extract effective CG potentials using a trajectory of a system (a series of snapshots over time).
- Each snapshot in the atomistic simulation will have positions for all atoms at each timestep which will be used to find their corresponding Force/Energy.
- A CG pseudo trajectory can be attained by grouping atoms in to beads using the beads centre of mass based on an atomistic average.
12
Q
What is an RDF?
A
- A radial distribution function g(r) shows the probability of finding a particle at distance r from a reference particle.
- At small distances there is a low probability of finding another atom due to coulombic repulsion (LJP?)
- Shells form around the reference particle as other bands of particles appear, with depletion zones between them.
13
Q
How does an RDF used as a target reference functions help form non-bonded potentials from the trajectory of a system?
A
- Goal is to form a CGP that reproduces the RDF obtained from atomistic simulations.
- The CG RDF, g(i) is linked to the trajectory of our system CG trajectory,
- Which in turn is linked to an atomistic RDF, g(ref), derived from an atomistic trajectory.
14
Q
What is the potential mean force and how is it related to g(ref)?
A
- Links the potential of mean force (average force that an atom experiences due to its neighbours) to the radial distribution function at that point.
15
Q
Why can we not use VPMF as a pair potential in our CG model? (not imp)
A
- Contains manybody contributions from all particles in the system.
- Want a pairwise interaction potential showing how energy varies with r, the sum of which equals our RDF.
16
Q
How is VPMF used in Iterative Boltzmann Inversion iteratively?
A
- VPMF =kBTln[gref(r)] instead is used as a starting guess for VCG (intermolecular CG potential)
- Vi+1CG(r) = ViCG(r) + kBTln[gi(r) / gref(r)] is our improved guess after each iteration
- gref(r) is our target probability distribution from atomistic simulation
- gi(r) is the probability distribution from a CG simulation using the ith potential
- Difference between the two moves us closer of further to result
17
Q
- How does our improved guess for the intermolecular CG potential [VCGi+1(r)] change when gi(r) = gref(r)? – 1 on graph
A
kBTln…= 0, no change to interaction potential needed as CG simulation produces same pairwise structure as atomistic
18
Q
- How does our improved guess for the intermolecular CG potential [VCGi+1(r)] change when gi(r) < gref(r)? – 2 on graph
A
- kBT… is -ve
- probability of finding particle (correlation between CG beads) at that r underestimated.
- Equation will lower pairwise CG energy at that r, making interaction more favourable
19
Q
- How does our improved guess for the intermolecular CG potential [VCGi+1(r)] change when gi(r) < gref(r)? – 3 on graph
A
- kBTln… is +ve
- probability of finding particle (correlation between CG beads) at that r overestimated.
- Equation raises pairwise CG energy at that r, making interaction more appropriately favourable (less than before).
20
Q
- Why does it take so long to produce a CG potential through IBI that is like an atomistic trajectory?
A
- Must iteratively calculate each pairwise interaction in your system using the IBI method until a full CG tabulated potential that is like the original atomistic trajectory is gained.
- However once this is done, CG allows us to reach much higher simulation times.
21
Q
- What is a problem with the IBI discussed in lectures as well as an example of this being solved?
A
- It takes only a singular atomistic simulation into account as a reference that will be under certain set conditions. This is a problem if one wanted to study phase transitions, which are dependent temperature changes.
- Moore et al (2016) developed a CGP constructed using many different atomistic simulations at varying temperatures to account for this, known as multi-state IBI.
22
Q
- What is force matching and how does it differ to Iterative Boltzmann inversion?
A
- IBI tried to match the structure of an atomistic simulation where the RDF is used to reach our target data.
- Force matching aims to match forces on the CG interaction sites with the forces at the atomistic level.
23
Q
- What is an example of a property one may want to look for using force matching?
A
- Force matching is ideal for looking at thermodynamic properties, for example a partition coefficient.
24
Q
- Explain the process of force matching. (don’t need to know in detail)
A
- A set of reference forces is collected through running an atomistic simulation
- A reference force Firef on a CG bead = sum of associated atomistic forces on beads constituents fγ , generating a pseudo CG trajectory acting as our target.
- Match the set of CG forces to the reference forces to minimise (image)
- Filp is the Net force on site i in atomistic config l
- Filref is the force determined by the trail force field (similar to VPMF). Often an existing CGP that is then adapted to fit closer to chosen reference.
25
Q
- What kind of approaches are IBI and force matching and what is the alternative?
A
- They are both bottom-up approaches, where a reference atomistic simulation is used to construct an effective CGP for our system which takes a long time to do. Inverse Monte Carlo (IMC) is another.
- Alternatively, one can use a top-down approach, which is generally used for investigating thermodynamic properties.
26
Q
- What are some advantages of both bottom-up and top-down approaches?
A
- Bottom-up: capable of capturing fine detail of interaction as CG interactions are extracted from reference atomistic simulations
- Top-down: provide potentials that are more easily transferable and applicable to experimental data.
27
Q
- What is an example of a top-down approach?
A
- The MARTINI force field, a CG potential fitted to reproduce experimental target data, using few parameters and standard interaction potentials to maximise transferability.
- A choice of building blocks calibrated against experimental oil/water partition coefficients are available to build a system
28
Q
- What is a disadvantage of the MARTINI force-field?
A
- No systematic way for developing potentials and introducing new molecules unavailable in building blocks.
29
Q
- Give examples of two systems that require a CG potential
A
- van Eerden et al – simulation of a large photosynthetic protein complex with membrane bound and water bound parts. Would be very difficult to simulate this complex system for long atomistically.
- Fowler et al – using the MARTINI FF to study the effects of embedded proteins on membrane stiffness using a course grained model.
30
Q
- What are some disadvantages of CG models?
A
- A model can be too biased, and very untransferable to another system
- A model may only be parameterised for a specific class of molecules, showing a lack of compatibility
- Model may be too coarse to capture desired behaviour
31
Q
- Water must be described accurately in CG models, why is this?
A
- Present in abundance in many biological systems, showing complex behaviours within those systems. Models generally are split in to three categorise to parameterise water (implicit, explicit, and polarizable)
