The Basic Technology Research Programme

Proof of Concept Studies & Consortia Building Networks

Background

• Cross research council endeavour– administered by EPSRC

• Funding for research to create a new technology

• Change the way we do science

• Underpin the future industrial base

Background

• 15 research projects funded up to April 2003

• Total funding for this period - £41M

• To support large, long term, high risk, high impact research consortia

• Encourage investigation of speculative ideas

Background

• Two levels of funding– One year start up– Full grant up to five years

• Two types of start up funding– Proof of concept– Consortia building networking

Proof of Concept Studies

• One year funding up to £100K• Research to investigate feasibility of

developing the new technology• Output – a business case for the next step of

investigation to be submitted in May 2004– Basic Technology Programme– Existing Research Council initiatives– DTI programmes

Consortia Building Networks

• Involvement of the users of the new technology at a very early stage

• Funding to form networks & hold workshops

ParaSurf – in silico Screening Technology

• Basic Technology Funding for October 2003 to September 2004– Proof of concept– Consortia building networking

• Academic partners– University of Portsmouth– University of Erlangen– University of Southampton– University of Oxford– University of Aberdeen

ParaSurf – Proof of Concept Research Programme

• Development of techniques to describe irregular solids & surfaces

• Development of projection & pattern recognition techniques for non-planar colour-coded surfaces

– spherical harmonics, molecular topology

• Conformational analysis• Rigid body dynamics incorporating surface features

– rigid parts of molecule treated as anisotropic solids linked by rotatable bonds

• Investigate how best to generate prediction models using surface properties that define a low dimensional chemical space

– QSAR, pattern recognition, artificial intelligence, analysis of surfaces

• Bench marking using Grid computing

ParaSurf – Proof of Concept Research Programme

Potential applications of the in silico screening technology

• High throughput virtual docking

• Physical property mapping

• ADMET prediction

• Long time-period simulation techniques

• Crystallisation and solubility

• Prediction of tautomers

• Chemical reactivity and metabolism

ParaSurf Progress Report

Letchworth, 16th March 2004

Main Areas

1. Molecular Surfaces and Property Calculation

2. RGB Encoding & Pattern Recognition3. Conformational Analysis4. Rigid Body Molecular Dynamics5. Analysis of Variables & QSAR models6. Grid Computing7. Consortium Building

Datasets

Small

Consensus Set of 74 Drug Molecules (diverse)

QSAR set (31 CoMFA steroids)

Medium

WDI subset (2,400 comps)

Harvard Chembank dataset (2,000 comps)

Large

WDI (50,000)

Maybridge (50,000)

Example Molecule

Allopurinol

Surface Definition & Local Property

Calculation

Calculations

3D co-ordinates from CORINA

QM calculations with VAMP

Local Properties and surfaces from ParaSurf

ParaSurf v1.0

SurfacesIsodensity Surfaces

Shrink WrapMarching Cube

Surfaces fit to Spherical Harmonics

PropertiesMEP, LIE, LEA and LPEncoded at points on the surfaceEncoded as Spherical Harmonic Expansions

Small molecule

RGB Encoding & Pattern Recognition

RGB Encoding

Each Local Property encoded as a colourLIE encoded on Red channel

LEA encoded on Green Channel

LP encoded on Blue Channel

Allopurinol RGB Surface

RGB Encoding

Alternative EncodingLIE

LEA

Absolute value of MEP

Allopurinol RGB Surface

Conformational Analysis

Conformational Analysis

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

MD studies of Rosiglitazone

Rigid Body Molecular Dynamics

Rigid body molecular dynamics

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.

QSAR models

Distribution of Properties

Correlation Matrix

1-0.10.470.39MEP

-0.110.580.26LP

0.470.5810.44LEA

0.390.260.441LIE

MEPLPLEALIE

Descriptors

34 descriptors based on Normal Distribution

Principal Components

Spherical Harmonic Co-efficients

Descriptors for LIEmaxLIE

minLIE

LIE

LIE

2IE

Maximum value of the local ionization energy

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 energy

Other Descriptors

MomentsOrder 1 – Mean

Order 2 – Variance

Order 3 – Skewness

Order 4 – Kurtosis

Overlapping GaussiansDerived from previous work on MD analysis

QSAR models

Models derived from Local PropertiesSurface Integral Model for Solvation Energy

RMS Error ~ 0.75 Kcal

Drug LikenessSOMs trained on WDI (drugs) & Maybridge (general)

Parameters from PC of Local Property Descriptors

Medium sized datasets superimposed on SOMs

GRID Computing

GRID Computing

ParaSurf compiled onSGI IRIXWindowsLinux (SUSE)IBM AIX

Future PlatformsSUN Solaris

GRID enabling at Portsmouth (Mark Baker), Southampton and Oxford.

Provisional Timings

SGI R10k, 256MBVAMP ~ 30s/compound

ParaSurf ~ 10s/compound

Intel 1.8 Xeon/ AMD Athlon XP-2000+ParaSurf ~ 2s/compound

SGI FUEL Workstation R14KParaSurf ~ 2s/compound

Conclusions

Conclusions

• Properties can be calculated

• Properties can be RGB encoded

• Properties are local

• Properties can be used for QSAR models

Computer vision methods for comparing molecular surfaces

• Comparing and recognising 3D objects is an active research area in robotics and AI.

• Fast methods have been developed for database indexing.

• Rotationally invariant descriptors of 3D objects are possible.

Pattern matching on molecular surfaces

• Can we recognise similar surfaces?

• Can we recognise similar surface fragments?

• Can we identify the most similar surface to our target?

• How do we compare field descriptors on the molecular surface?

Rotationally invariant 3D object descriptors

• Internal coordinates e.g. a distance matrix.

• Energy distributions based on the spherical harmonics.

• The spherical harmonic coefficients.

• Radial integration, radial scanning, and invariant moments.

Surface comparison

Two different approaches:1. Using spherical harmonic molecular

surfaces [J. Comp. Chem. 20(4) 383-395; Ritchie and

Kemp 2000; University of Aberdeen].2. Partial molecular alignment via local

structure analysis [J. Chem. Inf. Comput. Sci.

40(2) 503-512 ; Robinson, Lyne and Richards 1999;

University of Oxford].

An example grid of surface points

A grid is placed on a ParaSurf surface in order to reduce the number of surface points from 4038 to 55.

Partial molecular alignment

• We do not know which points on the two surfaces need to be aligned with each other.

• The essential approach is: all surface points on one surface are compared

with all points on the other.• For two surfaces, with M and N points, MN

possible alignments are possible: – we want to reduce this large search space!

Voting pairs are possible alignments

The voting pairs can have a critical effect on the quality of the surface alignment.

The voting table

• A voting table may list all matching pairs of surface points (i.e. all possible alignments).

• A smart editing of votes within the voting table can enable speed and accuracy. – We want to only consider alignments between

similar local features on the surfaces.– The more false votes we have in the voting

table the harder it is to find the optimum alignment.

A distance matrix can be used to describe local surface features

P1

P2

The internal distance matrix can be used to distinguish between surface points.

By comparing rows and columns from distance matricesof different surfaces we candetect similar surface features.

P3

Selecting the voting pairs

Similar local features, or interest points, on the molecular surface can be identified using a distance matrix.

For a point on each surface:1. Arrays of internal surface point distances are

calculated for both points i.e. dist1[], dist2[].2. After a crude alignment, the absolute difference

of dist1[] and dist2[] indicates the similarity of this pair of points.

Scoring the possible alignmentsThe optimum alignment is composed of a rotation R

and a translation T.• Apply the current rotation r:

1. Score the translation vectors t = p – q of all voting pairs (p,q) using a gravitational potential:

2. High potentials identify clusters of similar translation vectors.

3. The vector with the highest potential is the optimum translation T.

• Scoring all r gives R and T.

||

1

ji

ji

ji tt

P

Scoring with a gravitational potential

Translation vectors (x,y coordinates plotted)Some voting pairs for example rotations

Can we use the potential to compare aligned structures?

Can we get better alignments with more voting pairs?

Example alignments

1

3

4

2

Example 1: RMSD = 0.75

A

B

Example 2: RMSD = 1.05

A

B

Example 3: RMSD = 1.20

A

B

Example 4: RMSD = 1.89

A

B

Matching with the surface field descriptors: example 1

• Surfaces are aligned (using a quick search method; e.g. 45º rotations).

• Best N alignments are selected.

• Each alignment is gently perturbed and optimised using the field descriptors.

Matching with the surface field descriptors: example 2

• Align using the field descriptors’ values to identify suitable voting pairs: – only match on similar field descriptors.

• Filtering can be achieved by aligning the fields separately.

• More accurate alignments can be generated by combining field values.

Parameterisation• Voting pairs:

– The distance between points in surface grid. – The number of voting pairs.– Identifying and selecting local features. – How to represent the fields at interest points.

• Scoring:– Scoring function to identify the correct rotation and

translation (e.g. gravitational potential).– Target function to compare different surface alignments

(e.g. RMSD).

• Optimising the alignments.

Molecular Surface Property Graphs

Characterize the behaviour of a property

f : S

on a molecular 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.

Gradient Flow

• minima• saddles• maxima

Molecular Surface Property Graph

Applications

• Similarity– Pattern recognition methods– Maximal common subgraphs

• Complementarity– Compare ligand graph with graph induced on ligand by receptor

• QSAR– Topological indices

ExampleS = Connolly Surface f(x) = Electrostatic Potential = ∑ q(i) / d(x,i)

Method

• Locate critical points of f (Newton-Raphson).

• Linearize at saddles, find eigenvectors of Hessian( f ).

• Integrate gradient vector field forward in time from 2 points on unstable eigenvector, backward in time from 2 points on stable eigenvector (Runge-Kutta).

• Integrate to boundary of Connolly surface patch, then continue on adjacent patch until reaching another critical point.

Allopurinol

8 maxima 7 minima13 saddles

#maxima – #saddles + #minima = (S) = 2

Work in Progress …

• Implementation for

S = spherical harmonic surface

f = MEP, LIE, LEA and LP

– Use images of triangulation points as starting points for Newton-Raphson search for critical points.

– Automatic differentiation.

Summary

Molecular

surfaces

QM properties

presented on

surface

Compound

screening

Pattern matching

on surfacesMartin Swain

Critical featuresDave Whitley

Data reduction

and QSARBrian Hudson

Spherical harmonic

representationDave Ritchie

Future directions

• High-throughput ligand docking– Superimposition of ligand and a “negative” of the

receptor

• Use of the fields to drive simulation– Use of the fields to derive intermolecular forces

– Rigid-body motions – long time-step MD

– Free energy calculations

A hierarchy of methods

• Rapid screening using computationally fast approaches– 3D fields – Andy Vinter

• On reduced set:– Semi-empirical property calculations and alignments

• On most interesting molecules:– Density-functional or ab-initio calculations and

alignment

• More accurate molecular representations are used as appropriate, as resources allow