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Manuscript # 2004-07-21763A
Supplementary Methods
Additional details on the computations
To determine the contribution to the total discrimination factor of 6.8 kcal/mol between
oxoG and G in the active site and the alternative site (see text), additional free energy
difference simulations were performed. In each case, a thermodynamic cycle was used
to obtain meaningful chemical results from alchemical simulations. The first of these
yielded the difference between oxoG and G in solution and in the active site; the
calculated value for this difference (A1) is 4.5 kcal/mol favoring oxoG. This value
was corrected for the difference in solvation free energy between oxoG and G, which
was obtained by doing alchemical free energy simulations in the gas phase and in
solution; the simulations yielded A2 = 4.1 kcal/mol, again favoring oxoG.
Combining these two values yields 8.6 kcal/mol for the discrimination free energy in the
active site and, by difference, 1.8 kcal/mol in the alternative site, both favoring oxoG, as
indicated in the text.
Additional details on the QM/MM free energy simulations
The mixing parameter used in the alchemical free energy simulation was set equal to
0.02, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95 and 0.98. The end point
singularity problem was treated by the extrapolation method1. Molecular dynamic
simulations were run with a stochastic boundary potential2 for a 25 Å-radius sphere,
taking N9 of the base as the center; a 1fs timestep was used. Long-range electrostatics
was treated using charge-scaling procedure3. RESP charges4 for oxoG and G analogues
were utilized for the Poisson-Boltzmann calculations. To simulate the free energy
difference of single bases in aqueous solution, the methyl group carbon attached to N9,
2
which is shared by two alchemical species, was restrained to the center of 25 Å-radius
sphere with a force constant 3 kcal/mol·Å2. Free energy derivatives were corrected
using the relation of Valleau & Torrie5; the contribution is small. Convergence of the
free energy simulation was controlled with the reverse cumulative averaging method5;
simulations between 0.65 ns and 1.7 ns in length were required.
In the reverse
cumulative averaging method6, the standard errors of the energy derivatives for all the 
runs are calculated in the equilibrated region. The latter is determined by the normality
test, with the assumption that the energy derivative data are normally distributed once
the system is equilibrated. In the present study, 0.4 kcal/mol is pre-set as the upper limit
of the allowed standard error of the free energy derivative, (see Eq. 6 of ref 45). Based
on this criterion, simulations between 0.65 ns and 1.7 ns in length were required. The
upper bound of the standard error for the overall free energy change, obtained by the
integration of  from 0 to 1 is on the order 0.4 kcal/mol.
Data Collection and Structure Solution
Data on the oxoG-complex was collected on a Rigaku R-Axis IV++ detector at Enanta
Pharmaceuticals. Data on the complexes containing 7-deazaG and 7-deaza-8-azaG
were collected at NSLS X4A, and processed using HKL20007. Data on the G-complex
were collected at the CHESS A1 beamline and processed with DENZO and
SCALEPACK7. Data collection statistics are summarized in Supplementary Table.
The coordinates of the protein from the isomorphous structure of K249Q hOGG18
bound to oxoG-containing DNA was used as the initial model in refinement using
CNS9. The catalytic residues and residues involved in protein-DNA interaction were
omitted from the initial search model.
A rigid body fit followed by energy
minimization and simulated annealing performed in CNS resulted in a partial model.
Electron density for the DNA and the omitted residues became clearly visible in a A-
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weighted10 Fo-Fc map at this stage. The model was subsequently improved by iterative
rounds of energy minimization, simulated annealing and grouped B factor refinement in
CNS and model building in Quanta (Acelrys) while monitoring Rfree11. Simulatedannealing omit maps were frequently utilized to reduce model bias. Once the model
was nearly complete, individual B-factor refinement was included. Water molecules
were added to the model using both automated methods (in CNS) and manual
inspection of difference maps. Amino acid side-chains of some residues were truncated
at the -, -, -, or -carbon positions or the residues modelled as alanine residues if
electron density was not visible for the full side-chain. Details of data collection and
refinement statistics appear in Supplementary Table.
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