Docking

Docking techniques are another application of MM methodologies. They

allow in silico prediction of whether a substrate will bind a certain site in a

protein in a productive configuration, for this reason these methods are

very popular in drug design. Multiple conformations of the ligand (and, if

required, of the protein too) are tested and a scoring function determines

which are acceptable bindings and which are not. More detail of the

aspects mentioned below can be found in books

77,78

_{. Good reviews of the}

techniques in their current state and are given also available

79-84

.

There are different methods that can be used to perform a conformational

search of the substrate. Systematic methods, as their name implies, work

by testing all possible conformations, which results in a problem known as

combinatorial explosion (in which the calculation becomes unaffordable

because of the large number of possible conformations available).

Stochastic methods use as a starting point a random configuration of the

ligand, which is accepted or rejected with a certain probability (popular

examples of these are Monte Carlo and genetic algorithms). Molecular

Dynamics in which different parts of the system are simulated at different

temperatures are also used for conformational searches in docking

79

_.

The scoring function ranks the docked structures, and it is a key aspect of

the method as the accuracy of the results depends on it. It can be based on

Molecular Mechanics force fields (described in the previous section), or it

can be fitted to reproduce experimental binding energies or geometries.

The first approach will reproduce the limitations of the force field chosen,

while the other two will depend on the data set used. For these reasons,

the scoring functions are the main weakness of docking techniques and

where improvement efforts are directed

80

.

2.6 Thermal factors

Β-factors (or thermal factors) quantify the thermal motion of atoms based

on the attenuation of x-ray scattering. They have been used to study

protein flexibility

85

_{and thermostability}

86,87

_{, among other properties. B-}

factors can also be extracted from MD simulations making use of the

relationship between these, RMSD (Root Mean Square Deviation) from the

initial coordinates and RMSF (Root Mean Square Fluctuations)

88

. The

following equations summarize the basis of this relationship.

RMSF

2

₌

!! !!!

(2.7)

!"#$

! !_!

₌

!!! !!!!

!"#$

! !_!

(2.8)

(where Β is the Β-factor and N

s

is the number of structures in the

ensemble).

For !

!

≫1,

!"#$

! !_!

_≈

! !! !!! !!! !! !!!

(2.9)

(where N

a

is the number of atoms in the structure).

2.7 Computational resources

2.7.1 Hardware

This research was undertaken with the assistance of resources provided at

the National Computing Infrastructure (NCI) National Facility (NF)

systems at the Australian National University through the National

Computational Merit Allocation Scheme supported by the Australian

Government. Part of the Molecular Dynamics simulations were done on a

Quad-Core AMD Opteron(TM) Processor 2356 with 8GB of RAM with

time generously provided by Professor Thomas Huber.

2.7.2 Software

QM geometry optimisations and MP2 single point energy calculations

were carried out with Gaussian09

89

_{. Scaling factors for wB97XD and}

M062X were taken from Alecu et al.

90

_{. RI-MP2 single point energies were}

calculated with QChem4.1

91,92

. CCSD(T) energy calculations were done

with Molpro2012.1

93

_{. Preparation of input geometries was done using}

GaussView5

94

. Visualisation of results was done with GaussView or

Molden5.0

95

_{. Docking was done using AutoDock Vina}

96

_{. Visualization of}

results was done with Pymol

97

. Molecular dynamics simulations were

performed using AMBER12

98

_{; generation of parameters and trajectory}

analysis were carried out using the AmberTools included in the package.

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In document Computational studies of the E3 carboxylesterase from Lucilia cuprina (Page 52-61)