Transcript po-2_(CERMN)_Clustering studies on 5-HT4 ligands

Clustering studies on 5-HT4 ligands.
Thibault Varin, Nicolas Saettel, Jonathan Villain, Aurélien Lesnard,
François Dauphin, Ronan Bureau, Sylvain Rault.
CERMN, UPRES EA 3915, 5 rue Vaubénard, 14032 Caen Cedex
Introduction
5-HT4 receptors are implied in various peripheral and central functions,
such as gastro-intestinal tonicity and secretions, as well as the
modulation of emotional behaviour and cognitive mechanisms. Several
years ago, our research center (CERMN) has launched a program for
the discovery of new 5-HT ligands based on biological tests done in a
screening center. This program led to new families of 5-HT4 ligands in
the past years.1,2 The CERMN currently owns a chemolibrary of more
than 10000 compounds including one thousand 5-HT4 binding data. In
this study we analyse the performance of some fingerprint descriptors,
metrics and hierarchical methods to classify compounds according to
their activity.
Materials et methods
Clustering methods are based on the notion of distance (or similarity)
between individuals. Many molecular descriptors like physicochemical
properties or fingerprints can be used to perform theses distances. In this
study, we have tested two types of chemical fingerprints (binary
variables), a pharmacophore fingerprint and a fuzzy pharmacophore
fingerprint (continuous variables). One of the chemical fingerprint and
the two pharmacophore fingerprints were generated by JChem.3 The
second chemical fingerprint was generated by the Unity package of
Sybyl.4 For the binary variables, the distances were calculated by the
Euclidean, Tanimoto, Modified Tanimoto and Dice metrics. For the
continuous variables, the Euclidean, Canberra, Binary and Maximum
metrics were used. All distances were computed by R.5 For the binary
fingerprints, the « fp.sim.matrix » function of the fingerprint R package
and the « dist » function from the Stats R package for the continuous
fingerprints. For the canberra metric, if the denominator was equal to 0,
the variable was omitted.
Different hierarchical clustering methods : single, complete, average,
Ward and Energy. The functions « Agnes » and « Energy.hclust » from
the Cluster and Energy R packages was used for this step.
In function of the percentage of inhibition at 10-6M, the set was separated
into three groups corresponding to active, intermediate and inactive
derivatives. The inactive compounds have a percentage of inhibition
lower than 40%, the intermediate compounds have a percentage of
inhibition between 40% and 70% and the active compounds have a
percentage of inhibition greater than 70%. From this definition, 163
compounds were defined as actives.
Results
Metrics and methods comparisons :
Whatever the combination fingerprint/metric, we systematically obtain
the best results with the Energy method and by decreasing order with the
Ward, complete, average and simple linkage. For the metrics, no real
difference was observed for the chemical fingerprints whereas the best
results were obtained with the Canberra metric for the pharmacophore
fingerprints. None of the results obtained with the fuzzy pharmacophore
fingerprints were satisfactory.
Combinations comparison
At a first glance, the best combination was obtained with the Unity
fingerprints associated to the Tanimoto (Figure 1) or the Modified
Tanimoto coefficient and the Energy method. With this configuration,
only one active cluster was obtained, including 66% of active
compounds and 14% of intermediate compounds.
QCIw_max
Figure 1 : Dendrogram with an activity indication on tree lines
(in order : inactive, intermediate, active compounds).
With the pharmacophore fingerprints, the best results as a function of
QCId values were obtained with the Canberra and the Energy methods
(Figure 2). This combination led to 4 active clusters with one of the
highest QCI value recorded for all this study (0.65) and representing 88
% of the active compounds of the overall dataset. Moreover, these four
clusters include 72% of active compounds and only 13% of inactive
compounds (remaining set correspond to intermediate compounds).
We define as an active cluster each cluster for which the percentage of
active compounds is greater than the initial percentage of active
compounds in the dataset (16.3%).
With this definition, a new index was created by considering the
following variables : the maximum of active compounds in active
clusters (x), the minimum of inactive compounds in active clusters (y),
the minimum of active compounds in inactive clusters (z), the minimum
number of active singletons (w). An index, named QCI for Quality
Clustering Index was introduced with the following formula :
QCI = x/(x+y+z+w).
The curve corresponding to the maximal or optimal QCI values was
determined for each level of the clustering. This maximal curve was
used as reference to determine a Euclidean distance named QCId
between the calculated and this optimum curve. This value allows the
comparison of the different combinations (fingerprint / metric /
clustering) in terms of performance and stability during the clustering
process. The lowest value of QCId should correspond to the best
hierarchy.
A multiplicative factor was applied to QCI values according to the
clustering level (factor of 1 for the level 1 to 1/1000 for the level
1000). This operation has given a new index named QCIw (Figure 6).
For the hierarchy and to select the best level of clustering, the
maximum value of this parameter was considered (QCIw_max).
Considering theses indices, a comparison of the different combinations
(fingerprint, metric and clustering method) is presented herein.
QCIw_max
Figure 2 : Dendrogram with an activity indication on tree lines
(in order : inactive, intermediate, active compounds).
Conclusion
These comparative analyses of different combinations associated to
hierarchical clustering shows clearly the usefulness of 2D chemical and
pharmacophore fingerprints to efficiently classify chemical derivatives
in terms of molecular similarity and biological activity. We pointed out
the importance of the metrics associated to each descriptor and
particularly the Canberra approach for pharmacophore fingerprints. For
the clustering algorithms, our study demonstrates clearly, whatever the
fingerprint, the interest of a recent modification of the Ward method
named Energy. Our study illustrates the interest of clustering approach
to obtain an overview of the relationship between the structures and
biological data. However, for the quality of the hierarchy, it is necessary
to further investigate the best metric for each descriptor, amongst those
actually described. Global comparison of these metrics is currently
carried out.
Références
1. Bureau, R.; Daveu, C.; Lemaitre, S.; Dauphin, F.; Landelle, H.; Lancelot, J. C.; Rault, S., J Chem Inf Comput
Sci 2002, 42, (4), 962-7.
2. Hinsberger, A. Butt, S. Lelong, V. Boulouard, M. Dumuis, A. Dauphin, F. Bureau, R. Pfeiffer, B. Renard, P.
Rault, S., J. Med. Chem., 2003, 46, 138-147
3. JChem Screen 3.2, ChemAxon, Hungary, 2006, http://www.chemaxon.com.
4. Sybyl 7.3, Tripos Inc., 1699 South Hanley RD, St Louis, Missouri, 63144, USA, http://www.tripos.com
5. R 2.4.1, R Foundation for Statistical Computing, Austria, 2006,http://www.r-project.org.