Title: A Genetic Algorithm for Flexible Molecular Overlay and Pharmacophore Elucidation
1A Genetic Algorithm for Flexible Molecular
Overlay and Pharmacophore Elucidation
- Gareth Jones
- OpenEye Cup Feb 22, 2005, Santa Fe NM
2What is a GA?
- A Genetic Algorithm is based on the process of
Darwinian Evolution and is good for complex
search problems. - A Chromosome is an encoded representation of a
problem, typically an integer or binary string. - A chromosome encodes the conformation of each
active and the superimposition of the set. - Each chromosome can be assigned a fitness value,
which is a measure of how good a solution the
chromosome is. - A GA starts with a random population of
chromosomes. - Genetic operators such as crossover or mutation
are repeatedly applied to the population. - Over time the average fitness of the population
increases.
3Overview of the GA
4Roulette Wheel Parent Selection.
- Each member of the population gets a slice of the
roulette wheel that is proportional to it's
(ranked) fitness i.e. selection is biased
towards fitter individuals
5Genetic Operators
- Mutation. Randomly choose some portion of the
chromosome string and randomly alter it. - Crossover. Choose a random cross point after
which the string is copied from the other parent.
- Mixing. Crossover for sparse strings
6Genetic Algorithm for Pharmacophore Elucidation
- GAPE
- Based on GASP
- Distributed by Tripos Associates
- G. Jones, P. Willett and R. C. Glen, Journal of
Computer-Aided Molecular Design 9 (1995) p532 - Incorporates ideas from GOLD
- Distributed by CCDC
- G. Jones, P. Willett, R. C. Glen, A. R. Leach and
R. Taylor, Journal of Molecular Biology 267
(1997) p727 - And new stuff
- Java code base (some JNI)
7Molecular Overlay and Pharmacophore elucidation
- The problem is to overlay a series of actives
(without any prior knowledge of the
pharmacophore) such that common molecule receptor
interactions can be identified. - We use a GA to search simultaneously the
conformational space of a series of actives as
well as the possible superimpositions of each
conformation.
8Design of the Algorithm
- Identify important features in each molecule and
label them. - GA chromosome contains conformational information
in binary bitstrings and encodes mappings as
integer strings. - When decoding select one molecule as the base
molecule (the one with the most features) and use
GA mappings to fit other molecules on top of the
base molecule. - Fitness function comprises a score for the
overlay of similar features, internal steric
energies and common molecular volume. - Similarity scores based on hydrogen bond
distributions (Mills and Dean).
9Chromosome Encoding
- An overlay of N molecules requires a chromosome
of 2N-1 strings. - N binary strings encode conformational
information one string for each molecule. Each
byte encodes an angle of rotation about one
rotatable bond and bits are used to flip free
corners - Integer strings map each molecule onto the
fitting molecule.
10Chromosome Decoding
- The N binary strings are decoded to produce
molecular conformations for each molecule. - Each of the N-1 integer strings is decoded
- Two passes of least-squares fitting is used to
overlay molecules on top of the base molecule.
Fitting is applied so that functional groups are
positioned to interact with the same point in the
receptor. The chromosome is then rebuilt.
11GA Mapping
12Scoring Function
13Geometric Weight
- R (fitting radius) is annealed from 3.5 to 1.5
- Additional geometric constraints (linear drop-off
between maximum and minimum angles) - Acceptor geometries LP, Plane, Cone, None
- Donor, fitting point, donor angle.
- Desolvation correction
H1
r12
R
D2
H2
D1
14Donor/Acceptor Weights
- J.E.J Mills and P.M. Dean, Three-Dimensional
Hydrogen-bond Geometry and Probability
Information from a Crystal Survey, Journal of
Computer-Aided Molecular Design 10 (1996)
pp.607-622. - Ionization term
15Other Terms
- Volume Integral
- Uses atomic Gaussians
- Grant Pickup J. Phys. Chem. 1995, v99, p3503.
- VDW
- Parameters from TAFF
- Clark et al. J. Comp. Chem. 1989 v10 p892.
- User defined Feature Sets
16Torsional Distributions
- Restrict conformational search to observed
torsions - Examples from GOLD
- Can also use NUMIMBA Klebe and Meitzer, J.
CAMD, 1994, V8, p583
17Incorporation of Activities
- Acitivities can guide creation of the overlay
- Use pKi to specify activity.
- Weight pair-wise terms in volume integral and
pharmacophore feature score. - pKi values are used as initial weights then the
weights are normalized so that the average weight
is 1.
18Examples
- Examples use standard protocol
- Steady state operator-based GA
- Selection pressure of 1.001
- Score Weights
- Donor Hydrogen Weight 1500
- Acceptor Atom Weight 1750
- Aromatic Ring Weight 2500
- Volume Weight 10
- Energy Weight 10
- 60000 operations (crossovers 47.5, mutations
47.5 and migrations 5) - Island model- 5 populations of 100 chromosomes
- Input structures minimized and randomized
- Best guess at atom protonation
- 10 runs per test system (avg run time 256s)
- Display best scoring system.
195-HT3 Antagonists
20Angiotensin II Antagonists
215-HT2A Antagonists
22Incorporation of Activities
8.76 8-OH_DPAT7.94 Buspirone7.56
Spiperone7.16 MCPP5.75 Quipazine5.38
Remoxipride
http//www.gpcr.org 5HT1A pKi human activity
23Overlay with Activities
Overlay without Activities
24Examples from PDB
- A comparison of the pharmacophore identification
programs Catalyst, DISCO and GASP Patel,
Gillet, Bravi Leach JCAMD V16 (2002) 653-681. - DHFR 1boz, 1drf, 1ohk, 1dlr, 1hfp, 2dhf
- CDK2 1aq1, 1di8, 1e1v, 1e1x, 1fin, fvv
- Thrombin 1c4v, 1d4p, 1d6w, 1dwd, 1fpc
- HIV RT 1bqm, 1tvr, 1ddt, 1fk9, 1rt5, 1rt1, 1ep4,
1klm, 1rt3, 1vru - Thermolysin 1hyt, 4tmn, 5tmn, 1qf1, 5tln, 7tln
25DHFR
26DHFR
27Thrombin
28Thrombin
29CDK 2
30CDK 2
31HIV RT
- Shared pharmacophore 1bqm, 1tvr, 1ddt, 1fk9,
1rt5, 1rt1 - Others 1ep4, 1klm, 1rt3, 1vru.
32HIV RT
33HIV RT
34Thermolysin
35Thermolysin
36Pharmacophore Search
- Query molecules are held rigid.
- Query can be the output of a GAPE overlay.
- Database of target compounds sequentially matched
using GAPE against queries. - Sort alignments using GAPE score
- Test on 20 5HT2A ligands, overlaid on a
conformation of Sarpogrelate. - Used in-house on datasets of 800 compounds.
375HT2A Example
38Rigid Pharmacophore Search
- Query pharmacophore and structure(s) from GAPE.
- Use clique detection to screen conformations.
- Rank structures that pass using GAPE feature and
volume score. - Example 5HT3 query. Database of 18M conformers
(ChemNavigator collection). - 650 conformers/second.
- Proximity filter.
- Work in progress.
395-HT3 Example
40Conclusions
- The GA is a good method for molecular overlay and
pharmacophore generation. - Development of chromosome representation and
scoring function required considerable effort. - Believe that GAPE is a significant improvement
over GASP. - Pharmacophore features do not have to be present
in all molecules. - Incorporation of activities.
- More improvements.
41Acknowledgements
- Paul Watson, Carleton Sage, Runtong Wang Arena
Pharmaceuticals. - Val Gillet, Simon Cottrell, University of
Sheffield, UK PDB test systems. - Robin Taylor, CCDC GOLD torsional library.
- PyMol
- AstexViewer