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Letchworth, 16th March 2004ParaSurf Progress Report
Consensus Set of 74 Drug Molecules (diverse)
QSAR set (31 CoMFA steroids)
WDI subset (2,400 comps)
Harvard Chembank dataset (2,000 comps)
3D co-ordinates from CORINA
QM calculations with VAMP
Local Properties and surfaces from ParaSurf
Surfaces fit to Spherical Harmonics
MEP, LIE, LEA and LP
Encoded at points on the surface
Encoded as Spherical Harmonic Expansions
Each Local Property encoded as a colour
LIE encoded on Red channel
LEA encoded on Green Channel
LP encoded on Blue Channel
Absolute value of MEP
Efficient All Atom MD analysis (DASH)
Treated as time series (not Cluster Analysis)
Scales linearly with simulation length
No need for arbitrary choice of number of clusters
Can be analysed using Markov Chain methodology
Well founded methodology e.g. CNS / XPLOR (Axel T. Brunger, Stanford University)
Idea is to use rigid groups to model flexibility:
In the ligand
and the protein binding site.
Allows time-steps of 10fs to 20fs.
34 descriptors based on Normal Distribution
Spherical Harmonic Co-efficients
Minimum value of the local ionization energy
Mean value of the local ionization energy
Range of the local ionization energy
Variance in the local ionization energyDescriptors for LIE
Order 1 – Mean
Order 2 – Variance
Order 3 – Skewness
Order 4 – Kurtosis
Derived from previous work on MD analysis
Models derived from Local Properties
Surface Integral Model for Solvation Energy
RMS Error ~ 0.75 Kcal
SOMs trained on WDI (drugs) & Maybridge (general)
Parameters from PC of Local Property Descriptors
Medium sized datasets superimposed on SOMs
ParaSurf compiled on
GRID enabling at Portsmouth (Mark Baker), Southampton and Oxford.
SGI R10k, 256MB
VAMP ~ 30s/compound
ParaSurf ~ 10s/compound
Intel 1.8 Xeon/ AMD Athlon XP-2000+
ParaSurf ~ 2s/compound
SGI FUEL Workstation R14K
ParaSurf ~ 2s/compound
Two different approaches:
A grid is placed on a ParaSurf surface in order to reduce
the number of surface points from 4038 to 55.
all surface points on one surface are compared with all points on the other.
The voting pairs can have a critical effect
on the quality of the surface alignment.
The internal distance matrix
can be used to distinguish
between surface points.
By comparing rows and
columns from distance matrices
of different surfaces we can
detect similar surface features.
Similar local features, or interest points, on the molecular surface can be identified using a distance matrix.
For a point on each surface:
The optimum alignment is composed of a rotation R and a translation T.
(x,y coordinates plotted)
Some voting pairs for example rotations
Characterize the behaviour of a property
f : S
on amolecular surface S, in terms of a directed graph G on S derived from the gradient vector field
x =grad f(x)
Vertices (G) =fixed points of grad f (= critical points of f ).
Edges (G) = stable and unstable manifolds of the saddle points.
#maxima – #saddles + #minima = (S) = 2
S = spherical harmonic surface
f = MEP, LIE, LEA and LP
Critical features features