_PROBLEM CoEPrA-2006_Regression_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 
Regression_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(previously unpublished parameters) that have been computed additively 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
Regression_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. 


[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
Obj_00001 7.978
Obj_00002 9.235
Obj_00003 6.222
Obj_00004 7.165
Obj_00005 7.523
Obj_00006 8.497
Obj_00007 7.104
Obj_00008 7.669
Obj_00009 8.983
Obj_00010 7.626
Obj_00011 6.495
Obj_00012 7.635
Obj_00013 8.130
Obj_00014 7.408
Obj_00015 14.155
Obj_00016 7.290
Obj_00017 7.530
Obj_00018 7.194
Obj_00019 7.714
Obj_00020 7.126
Obj_00021 8.056
Obj_00022 6.464
Obj_00023 7.511
Obj_00024 7.752
Obj_00025 6.817
Obj_00026 6.908
Obj_00027 7.399
Obj_00028 8.564
Obj_00029 7.506
Obj_00030 8.435
Obj_00031 10.145
Obj_00032 6.713
Obj_00033 7.373
Obj_00034 6.631
Obj_00035 6.556
Obj_00036 9.275
Obj_00037 6.398
Obj_00038 7.899
Obj_00039 7.733
Obj_00040 6.451
Obj_00041 6.803
Obj_00042 8.545
Obj_00043 7.623
Obj_00044 7.548
Obj_00045 7.104
Obj_00046 6.920
Obj_00047 8.273
Obj_00048 9.318
Obj_00049 7.823
Obj_00050 7.337
Obj_00051 7.868
Obj_00052 10.184
Obj_00053 7.661
Obj_00054 7.306
Obj_00055 7.196
Obj_00056 8.389
Obj_00057 7.394
Obj_00058 7.368
Obj_00059 10.002
Obj_00060 8.917
Obj_00061 7.585
Obj_00062 7.329
Obj_00063 8.584
Obj_00064 8.827
Obj_00065 7.819
Obj_00066 7.878
Obj_00067 9.079
Obj_00068 7.735
Obj_00069 7.413
Obj_00070 7.399
Obj_00071 7.628
Obj_00072 7.937
Obj_00073 7.902
Obj_00074 7.383
Obj_00075 7.634
Obj_00076 6.894