_PROBLEM CoEPrA-2006_Classification_002

_GROUP_NAME Artem Cherkasov

_GROUP_MEMBERS
Emre Karakoc
Cenk Sahinalp
Artem Cherkasov

_ADDRESS
University of British Columbia, Medicine
Simon Fraser University, Computer Science
Vancouver, BC, Canada

_MODELING_PROCEDURE
Descriptors:
Based on our previous experience with QSAR modeling of peptides and 
general
QSAR clustering and classification of bioactivity properties, we 
decided to
use the following strategy. 
First, we have optimized the geometry of the studied peptides using 
MMFF94
force field; carboxylic groups have been deprotonated, amino groups -
protonated, partial charges computed according to [1].
Then we have computed QSAR descriptors that describe an entire peptide
molecule (global parameters) as well as descriptors corresponding to
constituent aminoacids considered in the context of their peptide
environment (we did not use 'isolated aminoacids' approximation).
Thus, for all peptides in the testing and training sets of
Classification_002 problem, we initially calculated > 400 various 3D 
and 2D
QSAR parameters that included:

- 50 global 'inductive' QSAR descriptors as described in [2]. 
- 10 local 'inductive' QSAR descriptors (computed toward CA atom) have
been calculated for each aminoacid of a given 8-mer; therefore, 80
additional 'inductive' QSAR descriptors have been produced. 
- 260 global atomic type-specific 'inductive' QSAR descriptors -
(previously unpublished parameters) that have been computed additively 
for
specific atomic types presented in the studied peptides;
- We have also computed ~90 conventional 3D and 2D global QSAR
parameters which are implemented within the MOE modeling package [3].

All 'inductive' QSAR descriptors that are described above, have been
calculated by our own SVL scripts for the MOE; most of them can be 
freely
downloaded through the SVL exchange. 

Descriptors Selection:
As a first step, the kNN-based linear optimization method based on a
distance measure [4] has been used for the initial selection of most
relevant QSAR parameters. As the result, 31 global, local 'inductive' 
and
conventional 2D descriptors have been selected for QSAR modeling of the
Clafficifcation_002 problem (the corresponding values can be found in 
the
attached excel spreadsheet). 

Modeling Procedure:
In order to build a predictive QSAR model on the set of the training
peptides we the PLS approach as it is implemented within the MOE [3]. 
The
quality of the PLS-based model has been ensured with Leave-One-Out 
(LOO)
validation. 

The resulting PLS solution has been applied to the external peptides of 
the
Clafficifcation_002 problem and the outputs from the models have been
interpreted with the Boolean terms using 7.77 threshold value.


[1] Cherkasov, A. Inductive Electronegativity Scale. Iterative 
Calculation
of Inductive Partial Charges. Journal of Chemical Information and 
Computer
Sciences, 2003, 43, 2039-2047. Cherkasov, A., Z. Shi, Y. Li, S.M. 
Jones, M.
Fallahi, G.L. Hammond. 'Inductive' Charges on Atoms in Proteins: 
Comparative
Docking with the Extended Steroid Benchmark Set and Discovery of a 
Novel
SHBG Ligand. Journal of Chemical Information and Modelling, 2005, 45,
1842-1853.
[2] Cherkasov, A. 'Inductive' Descriptors. 10 Successful Years in QSAR.
Current Computer-Aided Drug Design, 2005, 1, 21-42.
[3] Molecular Operational Environment, 2005, by Chemical Computing 
Group
Inc., Montreal, Canada.
[4] Karakoc E., Cherkasov A., Sahinalp S. C.  Distance Based Algorithms 
for
Small Biomolecule Classification and Structural Similarity Search. 
ISMB'06,
14th Annual International conference on Intelligent Systems for 
Molecular
Biology, Fortaleza, Brazil 2006.
[5] SNNS: Stuttgart Neural Network Simulator; Version 4.0, University 
of
Stuttgart, 1995.


_PREDICTION
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