 Research article
 Open Access
Identification of DNAbinding protein target sequences by physical effective energy functions: free energy analysis of lambda repressorDNA complexes.
 Elisabetta Moroni^{1, 2}Email author,
 Michele Caselle^{1} and
 Federico Fogolari^{3}
https://doi.org/10.1186/14726807761
© Moroni et al; licensee BioMed Central Ltd. 2007
 Received: 20 February 2007
 Accepted: 27 September 2007
 Published: 27 September 2007
Abstract
Background
Specific binding of proteins to DNA is one of the most common ways gene expression is controlled. Although general rules for the DNAprotein recognition can be derived, the ambiguous and complex nature of this mechanism precludes a simple recognition code, therefore the prediction of DNA target sequences is not straightforward. DNAprotein interactions can be studied using computational methods which can complement the current experimental methods and offer some advantages. In the present work we use physical effective potentials to evaluate the DNAprotein binding affinities for the λ repressorDNA complex for which structural and thermodynamic experimental data are available.
Results
The binding free energy of two molecules can be expressed as the sum of an intermolecular energy (evaluated using a molecular mechanics forcefield), a solvation free energy term and an entropic term. Different solvation models are used including distance dependent dielectric constants, solvent accessible surface tension models and the Generalized Born model. The effect of conformational sampling by Molecular Dynamics simulations on the computed binding energy is assessed; results show that this effect is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered. The free energy of binding for nonspecific complexes, estimated using the best energetic model, agrees with earlier theoretical suggestions. As a results of these analyses, we propose a protocol for the prediction of DNAbinding target sequences. The possibility of searching regulatory elements within the bacteriophage λ genome using this protocol is explored. Our analysis shows good prediction capabilities, even in absence of any thermodynamic data and information on the naturally recognized sequence.
Conclusion
This study supports the conclusion that physicsbased methods can offer a completely complementary methodology to sequencebased methods for the identification of DNAbinding protein target sequences.
Keywords
 Free Energy
 Root Mean Square Deviation
 Binding Free Energy
 Solvent Accessible Surface Area
 Surface Tension Coefficient
Background
ProteinDNA recognition plays an essential role in the regulation of gene expression. Although a significant number of structures of DNA binding proteins have been solved in complex with their DNA binding sites increasing our understanding of recognition principles, most of the questions remain unanswered. Several studies showed that proteinDNA recognition could not be explained by a simple onetoone correspondence between amino acids and bases [1–3], even if hypothesized hydrogen bonding patterns and definite preferences have been actually found in experimentally solved structures [1]. Moreover regulatory proteins are known to recognize specific DNA sequences directly through atomic contacts between protein and DNA and/or indirectly through watermediated contacts and conformational changes [1, 4, 5]. The degree of redundancy and flexibility seems to suggest that the recognition mechanism is ambiguous, therefore the prediction of DNA target sequences is not straightforward [6].
DNA protein interactions can be studied using several different computational methods, which could offer several advantages compared to the current experimental methods, more laborious and slow. In the following we will indicate, for simplicity, DNAbinding protein target sequences with the more specific term "transcription factor binding sequences", although the first term is more general.
Computational tools for the identification of Transcription Factors (TF) binding sequences can be organized in two main approaches:

"sequence based methods" in which a central role is played by the statistical properties of the base distribution in the DNA regions which are expected to be involved in transcriptional regulation (see [7, 8] for a general review on the subject).

"structure based tools" which use the structural information on proteinDNA complexes derived from Xray crystallography and Nuclear Magnetic Resonance.
The main focus of this paper is on the second approach, although the best results will likely be obtained by tools able to combine in a clever way these two approaches.
Sequence based methods
 i)
ii) local search algorithms, including expectation maximization and various flavours of Gibbs sampling (see e.g. [17–20]).
It is important to stress that this type of studies cannot be based exclusively on the statistical features of the DNA regions presumably involved in transcriptional regulation, but must be complemented with independent information about gene regulation. In this respect three important sources of information may be used: the functional annotations collected in public databases, gene expression data on a global scale, and the so called 'phylogenetic footprinting'. In particular this last approach, thanks to the increasing number of sequenced genomes, has proved to be very effective in these last few years (see e.g. [21–30]). The major problem of all these tools is the large number of false positives, above all in the case of higher eukaryotes (for a thorough analysis of this problem see the interesting assessment of TF binding sites discovery tools reported in [31]). It is exactly to cope with this type of problem that it could be important to resort to structure based approaches.
Structure based methods
 i)
those based on knowledge based potentials (mostly statistical effective energy functions, SEEFs);
ii) those based on physical potentials (or physical effective energy functions, PEEFs).
SEEFs are energy functions derived from a dataset of known proteinDNA structures. A set of features is selected (e.g. nucleotideamino acid contacts, roll angles for DNA bases, interatomic distances, etc.); the process often involves parameter choices, like threshold on distances or interval binning. The statistical properties of these features are compared with apriori expectations and logodd scores are derived [6, 33–38]. At the most basic level, structures may be used to define contacts among DNA bases and protein amino acids and, for each pair of positions, the occurrences of nucleotides and amino acids contacts are used to derive effective potentials [36]. Moreover a statistical potential, taking into account contact geometry and spatial arrangement of contacting residues can be derived [6]. Recently interesting developments of this approach have been proposed ([38, 35, 5, 39]). The approach suffers from theoretical and practical problems. From the theoretical point of view potentials of mean force are not in general additive and the exact modelization of apriori expectations (or socalled reference state) may be difficult for complex systems (see e.g. [40]). The main practical problem is the requirement of a large number of sequences or binding experimental data since the available data may be biased towards specific classes of proteinDNA complexes. Moreover datasets generally do not contain unfavourable interactions between amino acids and bases since they entail proteinDNA complexes that occur naturally. Thus the statistical potential may predict correctly the wild type targets as opposed to incorrect ones, but it may not be as good at distinguishing among mutants.
Notwithstanding all caveats usage of SEEFs are widespread in the field of structural predictions. Provided that sufficient data are available these methods are reasonably fast and accurate, as demonstrated for instance in the field of protein structure prediction (see e.g [41]).
 i)
difficulties in estimating entropic effects;
ii) difficulties in properly estimating solvation effects;
No consensus has emerged on the choice of parameters (e.g. inner dielectric constant, surface tension coefficient, forcefield parameters) and on the protocols that should be applied;
iii) difficulties in estimating gasphase energy with available forcefields which are derived from the analysis of small compounds at equilibrium and do not take into account electrostatic polarization.
In order to get rid as far as possible of all these problems, binding free energies are expressed relative to a reference system and in most computational studies optimal parameters have been chosen for matching experimental data.
As far as proteinDNA complexes are concerned attempts to compute binding free energies using physics based approaches have started in the 1990s. The electrostatic component of the binding free energy has been studied according to continuum methods and its dependence on temperature and salt concentration has been computed [44–46]. Integration of electrostatics with other components including DNA conformational free energy has been extended from DNAligand complexes [47] and proteinpeptide complexes [48] to proteinDNA complexes [49]. Recently Wojciechowski et al. [50] studied the complex of telomerase end binding protein with single stranded DNA optimizing the weights of different contributions in order to reproduce binding data. The availability of the successful analytical generalized Born model treatment of electrostatics solvation effects enabled computation of binding energies with hybrid molecular mechanics/Generalized Born surface accessibility methods by Jayaram et al. [51]. The group of Kollman developed the molecular mechanics/Poisson Boltzmann surface accessibility (MM/PBSA) methodology and applied it extensively to biomolecular systems (see for a review of these applications [42, 43] and [42, 43, 43, 52] for important extensions of these ideas).
However, when MM/GBSA or MM/PBSA energy versus time plots are presented for explicit solvent molecular dynamics simulation snapshots, fluctuations in the range of tens to hundreds of kcal/mol are found, thus posing an issue on the reliability of averages. In this respect SEEFs appear much more robust energy estimation methods.
In a few very recent reports interesting results have been reported concerning the capability of hybrid methods to predict proteinDNA binding sites [53–57]. In this paper we focus on the application of PEEFs to a single DNA binding protein in complex with many different DNA sequences.
The availability of high resolution Xray crystal structure [58] and suitable experimental data makes the λ repressoroperator complex an interesting system for computational analysis of proteinDNA interaction. The bacteriophage λ repressor protein is a small, 92 amino acid, protein that binds the DNA as a dimer. Each monomer binds to an operator half site. The aminoterminal domain of λ repressor is responsible for DNA binding and the carboxyterminal domain is primarily responsible for dimerization [59]. Each monomer contains a typical helixturnhelix motif found in a variety of DNA binding proteins [4, 60]. The free energy of binding of λ repressor for wildtype O_{ R }1 operator DNA and of all possible single basepair substitutions within the operator have been experimentally measured using the filter binding assay technique and changes in the free energy of binding caused by the mutations have been determined [61]. Besides being a perfect playground to test our methods, the so called "λswitch" in which the λ repressor is involved is very interesting in itself (for a review see [62]). This "genetic" switch is tightly regulated by the λ repressor and the Cro proteins. In these last years this system, due to its relative simplicity and to the availability of rather precise experimental data attracted a lot of interest and various models (see for instance [63–65] and references therein) have been proposed to describe its behaviour. Despite these efforts in all these models there are still a few open problems which need to be understood. In particular it has been recently realized that in order to ensure the remarkable stability of the λ switch one should require a very high nonspecific affinity both for the λ repressor and for Cro [65, 66]. Such a prediction is very difficult to test experimentally but could rather directly be evaluated with the tools which we shall discuss in this paper. In fact one of the main goal of the test which we shall perform on the λ repressor will be the evaluation of its nonspecific binding energy and the comparison with the prediction of the model discussed in [66].
In the present work we apply different techniques to evaluate the binding affinities by means of computational methods. It is assumed that the relative free energy of binding of a protein to different DNA sequences may be expressed as the sum of a molecular mechanical term, that includes the nonbonded electrostatic and Van der Waals contributions, and a hydration term that can be further split in a polar and a hydrophobic contribution. Due to the peculiar nature of hydrogen bonds similar alternative models are tested where an energy term proportional to the number of hydrogen bonds is added.
 1)
to provide an assessment of the accuracy of different methods and protocols by comparison with experimental data;
 2)
to provide a reliable estimate of nonspecific binding energies;
 3)
to propose a protocol for the prediction of DNAbinding target sequences which makes no use of sequence information.
 i)
we estimate optimal weights for different contributions to DNAprotein binding free energies using different solvation models;
ii) for 52 single basepair mutants we perform 1 ns molecular dynamics (MD) runs and we assess the effect of MD on the computed binding energies;
iii) we compute MM/GBSA binding energies for one thousand complexes where the bases of the double stranded DNA are substituted according to randomly generated DNA sequences in order to estimate nonspecific binding free energy;
iv) we scan the entire bacteriophage λ genome with the scoring profiles obtained from free energy computations. One of the profiles is obtained making use only of the structural data available for a single molecular complex, with no sequence information.
The statistical analysis of the results show that computational methods may offer a predictive tool truly complementary to sequencebased identification of DNAbinding protein target sequences. This is particularly important in view of the emergence of consensus protocols where the independence of the different methods is a prerequisite.
Results and discussion
Binding free energy changes between the λ repressor dimer and the DNA operator mutants have been calculated using different methodologies, as described in the Methods section.
MM/DDDCOONS
Optimal scaling factors for the MM/DDDCOONS model and the MM/DDDCHP model. Standard deviations (see Methods section) are given in parentheses.
MM/DDDCOONS  1r  2r  4r  8r 

x _{ vdW }  0.075 (0.071)  0.041 (0.100)  0.043 (0.111)  0.025 (0.116) 
x _{ DDDC }  0.083 (0.019)  0.184 (0.037)  0.359 (0.076)  0.802 (0.159) 
x _{ OONS }  0.072 (0.070)  0.020 (0.075)  0.084 (0.061)  0.043 (0.076) 
x _{ const }  65.254 (20.806)  73.523 (21.615)  75.782 (23.697)  64.715 (26.116) 
MM/DDDCOONS (+HB)  1r  2r  4r  8r 
x _{ vdW }  0.075 (0.072)  0.040 (0.100)  0.042 (0.109)  0.034 (0.113) 
x _{ DDDC }  0.068 (0.030)  0.154 (0.046)  0.286 (0.089)  0.637 (0.179) 
x _{ OONS }  0.075 (0.071)  0.019 (0.075)  0.066 (0.061)  0.010 (0.076) 
x _{ HB }  0.151 (0.249)  0.166 (0.152)  0.226 (0.145)  0.269 (0.144) 
x _{ const }  57.867 (24.223)  68.819 (21.992)  69.397 (23.718)  58.463 (25.680) 
MM/DDDCHP  1r  2r  4r  8r 
x _{ vdW }  0.144 (0.065)  0.215 (0.106)  0.085 (0.115)  0.133 (0.128) 
x _{ DDDC }  0.076 (0.017)  0.221 (0.033)  0.402 (0.075)  0.844 (0.153) 
x _{ P }  0.018 (0.008)  0.027 (0.011)  0.007 (0.011)  0.012 (0.012) 
x _{ H }  0.028 (0.007)  0.021 (0.007)  0.013 (0.008)  0.015 (0.008) 
x _{ const }  4.023 (26.223)  21.075 (24.822)  38.294 (32.091)  31.927 (29.069) 
MM/DDDCHP + (HB)  1r  2r  4r  8r 
x _{ vdW }  0.144 (0.065)  0.221 (0.104)  0.105 (0.111)  0.175 (0.123) 
x _{ DDDC }  0.059 (0.027)  0.185 (0.040)  0.281 (0.089)  0.623 (0.172) 
x _{ P }  0.018 (0.008)  0.029 (0.011)  0.011 (0.011)  0.018 (0.011) 
x _{ H }  0.029 (0.007)  0.022 (0.007)  0.017 (0.008)  0.017 (0.007) 
x _{ HB }  0.181 (0.218)  0.210 (0.138)  0.327 (0.143)  0.332 (0.137) 
x _{ const }  13.355 (28.624)  12.784 (25.076)  21.219 (31.635)  20.937 (28.041) 
RMSD and correlation coefficients (r) between calculated and experimental values using all available data (all) and the leaveoneout cross validation technique (loo)
all  loo  

rmsd  r  rmsd  r  
MM/DDDCOONS  F(3,48)  P  
1r  0.990  0.538  6.527  < 0.001  1.086  0.406 
2r  0.886  0.656  12.108  < 0.001  0.967  0.575 
4r  0.857  0.684  14.081  < 0.001  0.926  0.619 
8r  0.969  0.673  13.253  < 0.001  0.943  0.600 
MM/DDDCOONS (+HB)  F(4,47)  P  
1r  0.986  0.543  4.923  0.002  1.109  0.375 
2r  0.875  0.667  9.420  < 0.001  0.967  0.576 
4r  0.836  0.703  11.471  < 0.001  0.918  0.629 
8r  0.838  0.701  11.332  < 0.001  0.922  0.624 
MM/DDDCHP  F(4,47)  P  
1r  0.863  0.678  10.007  < 0.001  0.954  0.591 
2r  0.803  0.730  13.416  < 0.001  0.890  0.658 
4r  0.850  0.690  10.695  < 0.001  0.944  0.601 
8r  0.840  0.699  11.243  < 0.001  0.933  0.614 
MM/DDDCHP (+HB)  F(5,46)  P  
1r  0.857  0.684  8.089  < 0.001  0.967  0.578 
2r  0.783  0.745  11.500  < 0.001  0.888  0.662 
4r  0.805  0.728  10.369  < 0.001  0.903  0.645 
8r  0.791  0.739  11.099  < 0.001  0.889  0.659 
MM/GBSA  F(5,46)  P  
0.992  0.664  7.258  < 0.001  1.109  0.551  
MM/GBSA (+HB)  F(6,45)  P  
0.782  0.746  9.413  < 0.001  0.928  0.630 
Moreover the estimated variance of coefficients is often very large. Notwithstanding these difficulties it is worth noting that some terms appear to be particularly important. For instance each proteinDNA hydrogen bond (when explicitly included in the model) appears to contribute 0.15 to 0.27 kcal/mol, depending on the electrostatic model assumed.
As expected the electrostatic term is reduced when hydrogen bonds are taken into account separately. For ε = 2r the best scaling coefficient x_{ DDDC }changes from 0.154 to 0.182 upon removal of the term proportional to the number of hydrogen bonds.
The correlation between the different contributions is reflected in the changes, with changing dielectric model, of the OONS term scaling factor, which is always strongly reduced by the scaling factor ranging from 0.075 to 0.066. Finally the constant term which takes into account common entropic terms (which can be estimated to be in the range 20 to 40 kcal/mol) and the free energy of binding of the reference complex (which implies the addition of 11.3 kcal/mol), expected to be in the range of 30 to 50 kcal/mol, is slightly larger than expected.
MM/DDDCHP
The OONS solvation term is accounting for both apolar and electrostatic solvation terms which should be already taken into account, at least partly, in the distance dependent dielectric constant. The same calculations described above have been performed using a similar approach in which the solvation term of the binding free energy is taken to be proportional to the polar/apolar accessible surface area of the molecule (see Eq. 6). The best scaling factors have been determined fitting the set of experimentally measured proteinDNA binding affinities (Table 1). The quality of the computed binding free energies ΔG_{ calc }has been assessed evaluating the linear correlation coefficient r and the root mean square deviation (RMSD) between calculated and experimental values. In order to verify the performance of the model, a leaveoneout scheme has been adopted (Table 2). The Fstatistic shows that the model is significant (p < 0.001).
All the values of the distance dependent dielectric constant which have been tested gave a quite high and similar linear correlation coefficient. The highest correlation value (r = 0.745) was obtained for ε = 2r for the MM/GBSA(+HB) model and the lowest ones for ε = 1r similar to the MM/DDDCOONS model. Generally, binding free energy changes lower than 1.0 kcal/mol are overestimated whereas binding free energy changes greater than 2 kcal/mol are underestimated in all the cases. More accurate predictions have been obtained for ε = 2r, in particular for values lower than 1.0 kcal/mol (Figure 2).
Also for the present model the addition of an explicit hydrogen bond term reduces the coefficient of the electrostatic term as could be expected.
Components of the free energies (in kcal/mol) calculated according to the MM/DDDCHP(+HB) model. The distance dependent dielectric constant is 1r. The constant term x_{ const }is 13.355 kcal/mol
Original base  Mutated  ΔG_{exp}  ΔG_{calc}  vdW  Coul  H  P  HB 

T3A39  CG  0.400  0.311  50.460  12.925  36.028  47.532  6.508 
GC  0.600  0.204  50.326  12.684  35.875  47.203  6.508  
AT  0.400  0.091  50.384  12.669  35.965  46.861  6.508  
A4T38  CG  1.000  1.245  49.982  13.112  35.965  48.057  6.327 
TA  1.400  2.666  49.455  13.227  35.992  48.858  6.147  
GC  1.100  1.456  49.704  13.341  36.114  48.070  6.327  
C5G37  TA  0.800  1.681  50.791  13.042  36.270  49.108  6.508 
AT  0.600  0.404  50.786  12.985  36.360  47.860  6.689  
GC  0.700  0.659  50.921  12.952  36.417  47.978  6.508  
C6G36  TA  0.900  1.176  50.637  12.833  36.068  48.622  6.689 
AT  1.000  1.918  50.517  12.560  35.698  49.161  6.508  
GC  0.600  1.016  50.216  12.680  35.837  47.939  6.508  
T7A35  GC  0.400  0.018  50.525  13.005  35.845  47.387  6.327 
AT  0.200  0.301  50.387  13.076  35.731  47.715  6.327  
CG  0.900  0.725  50.472  12.987  36.267  47.781  6.508  
C8G34  TA  0.500  1.878  50.138  13.246  36.020  48.924  6.327 
AT  3.600  2.417  49.584  12.945  35.415  49.213  6.327  
GC  3.400  1.965  49.762  13.259  36.086  48.583  6.327  
T9A33  AT  2.800  2.387  49.570  12.933  36.187  48.386  6.327 
GC  1.000  1.111  49.390  13.552  36.308  47.427  6.327  
CG  0.300  0.489  50.729  12.988  36.235  47.834  6.508  
G10C32  AT  0.400  0.974  50.711  12.531  36.073  48.187  6.689 
TA  0.500  1.377  50.195  13.338  36.243  48.530  6.508  
CG  0.200  0.654  51.175  12.775  36.602  48.227  6.870  
G11C31  CG  1.400  0.061  50.889  13.098  36.203  47.768  6.689 
AT  1.100  0.760  50.512  13.375  35.925  48.766  6.689  
TA  2.300  0.451  51.217  12.921  36.481  48.332  6.870  
C12G30  GC  2.500  2.764  48.826  13.604  36.091  48.425  5.966 
AT  2.500  1.043  50.496  12.687  36.059  48.030  6.508  
TA  2.700  2.161  49.468  13.250  35.898  48.845  6.508  
G13C29  AT  2.900  2.337  50.000  12.972  36.226  48.766  6.327 
CG  3.400  2.413  49.823  12.729  36.460  48.188  6.327  
TA  3.200  2.546  49.982  12.803  36.574  48.621  6.508  
G14G28  CG  3.700  2.728  49.462  12.929  36.226  48.215  5.966 
TA  3.700  2.834  49.692  12.379  35.851  48.556  6.147  
AT  3.100  2.202  50.081  13.153  36.145  48.793  6.147  
T15A27  GC  2.900  1.728  50.522  12.607  36.887  47.834  6.508 
AT  2.000  1.089  50.515  12.812  36.617  47.663  6.508  
CG  2.500  0.966  50.540  12.811  36.544  47.637  6.508  
G16C26  CG  0.900  1.793  49.675  12.665  36.140  47.676  6.327 
TA  1.000  1.767  50.112  12.163  35.993  47.912  6.508  
AT  0.600  1.276  50.287  13.133  36.279  48.280  6.508  
A17T25  CG  0.200  1.168  50.790  12.598  36.796  47.624  6.508 
TA  0.100  1.134  50.710  12.660  36.638  47.729  6.508  
GC  0.200  1.256  50.358  12.788  36.760  47.506  6.508  
T18A24  CG  2.000  1.796  49.628  13.518  36.648  47.978  6.327 
AT  2.800  2.799  49.290  13.722  36.428  48.885  6.147  
GC  1.100  1.749  49.774  13.455  36.261  48.399  6.327  
A19T23  TA  0.400  1.239  50.111  12.649  36.212  47.650  6.508 
CG  0.500  1.378  50.196  12.587  36.216  47.808  6.508  
GC  0.400  0.780  50.410  12.892  36.243  47.703  6.508  
wild type    0.000  1.158  50.404  13.063  36.234  48.254  6.508 
MM/GBSA
Optimal scaling factors for the MM/GBSA model and the MM/GBSA(+HB) model: analysis of the λ repressoroperator system (λ), of the Crooperator system (Cro) and the joint analysis (λ + Cro). Standard deviations (see Methods section) are given in parentheses.
MM/GBSA  λ  Cro  λ + Cro 

x _{ Coul }  0.157 (0.045)  0.132 (0.067)  0.153 (0.037) 
x _{ vdW }  0.252 (0.124)  0.332 (0.132)  0.313 (0.086) 
x _{ GB }  0.142 (0.046)  0.121 (0.069)  0.145 (0.038) 
x _{ P }  0.010 (0.012)  0.019 (0.012)  0.009 (0.006) 
x _{ H }  0.029 (0.008)  0.009 (0.011)  0.031 (0.005) 
x _{ const }  11.744 (28.025)  70.791 (37.615)  19.519(11.903) 
MM/GBSA(+HB)  λ  Cro  λ + Cro 
x _{ Coul }  0.042 (0.053)  0.016 (0.066)  0.051 (0.041) 
x _{ vdW }  0.206 (0.113)  0.219 (0.118)  0.262 (0.079) 
x _{ GB }  0.025 (0.054)  0.002 (0.068)  0.043 (0.041) 
x _{ P }  0.016 (0.011)  0.001 (0.012)  0.024 (0.006) 
x _{ H }  0.023 (0.008)  0.005 (0.010)  0.029 (0.005) 
x _{ HB }  0.533 (0.156)  0.879 (0.224)  0.561 (0.125) 
x _{ const }  13.699 (26.308)  65.011 (32.867)  21.595 (10.908) 
The constant term is 11.7 kcal/mol, lower than what expected, probably as a consequence of the correlation of this term with the polar and hydrophobic surface area terms (the linear correlations of the coefficients are 0.51 and 0.75, respectively). The standard deviation of this term is however very large (28.0 kcal/mol).
Scaled free energy components for the MM/GBSA(+HB) model are reported in Table 5.
Components of the free energies (in kcal/rnol) calculated according to the MM/GBSA model. The constant term x_{ const }is 13.699 kcal/mol
Original base  Mutated  ΔG_{exp}  ΔG_{calc}  Coul  vdW  GB  P  H  HB 

T3A39  CG  0.400  0.115  79.691  33.547  50.408  30.607  37.296  18.658 
GC  0.600  0.525  79.292  33.467  50.226  30.690  37.327  18.658  
AT  0.400  1.165  78.394  33.432  49.648  30.588  37.181  18.125  
A4T38  CG  1.000  0.777  81.129  33.803  51.302  30.367  37.934  17.592 
TA  1.400  1.036  81.246  33.723  51.482  30.201  38.215  17.592  
GC  1.100  1.613  79.949  33.852  50.681  30.609  38.017  17.592  
C5G37  TA  0.800  2.136  80.893  33.864  51.109  30.552  39.125  17.592 
AT  0.600  0.996  79.955  33.842  50.645  30.484  38.090  18.125  
GC  0.700  0.377  81.302  33.810  51.382  30.589  37.944  18.125  
C6G36  TA  0.900.  1.362  80.476  33.730  50.877  30.661  38.456  18.125 
AT  1.000  0.622  79.641  33.667  50.411  30.314  38.163  18.658  
GC  0.600  1.110  80.186  33.579  50.678  30.491  38.132  18.125  
T7A35  GC  0.400  0.082  80.781  33.792  51.084  30.579  37.254  18.125 
AT  0.200  0.318  80.382  33.960  50.847  30.200  38.038  18.125  
CG  0.900  1.146  80.387  33.817  50.873  30.729  37.641  17.592  
C8G34  TA  0.500  1.350  80.849  33.862  51.113  30.593  38.247  17.592 
AT  3.600  3.360  77.402  33.305  49.165  30.110  38.152  17.059  
GC  3.400  2.435  78.703  33.834  49.920  30.582  37.829  17.059  
T9A33  AT  2.800  2.732  78.808  33.818  50.131  30.297  38.289  17.059 
GC  1.000  1.489  79.210  34.213  50.394  30.812  37.599  17.592  
CG  0.300  0.714  80.866  33.839  51.216  30.778  37.851  18.125  
G10C32  AT  0.400  1.868  79.605  33.008  50.347  30.450  38.110  18.125 
TA  0.500  1.034  80.384  34.230  50.938  30.722  38.414  18.125  
CG  0.200  0.182  81.436  34.074  51.523  30.789  37.975  18.658  
G11C31  CG  1.400  1.533  78.037  33.806  49.387  30.408  38.007  18.125 
AT  1.100  1.009  79.919  33.894  50.529  30.555  38.163  18.125  
TA  2.300  0.731  78.332  33.617  49.511  30.299  38.362  19.191  
C12G30  GC  2.500  3.881  78.666  33.416  50.051  30.575  38.163  16.526 
AT  2.500  2.061  78.455  33.360  49.873  30.306  37.588  17.592  
TA  2.700  2.656  78.889  33.693  50.116  30.223  38.257  17.059  
G13C29  AT  2.900  1.744  79.600  33.700  50.381  30.624  37.933  17.592 
CG  3.400  1.941  80.240  33.646  50.825  30.805  38.090  17.592  
TA  3.200  2.131  79.467  33.720  50.343  30.737  38.665  18.125  
G14G28  CG  3.700  3.688  78.059  33.822  49.562  30.408  38.425  16.526 
TA  3.700  2.495  78.334  33.204  49.700  30.231  37.996  17.592  
AT  3.100  1.703  80.426  33.916  50.878  30.593  38.467  17.592  
T15A27  GC  2.900  1.755  79.475  33.371  50.375  30.884  37.766  18.125 
AT  2.000  1.946  79.417  33.359  50.311  30.820  37.484  17.592  
CG  2.500  1.088  80.371  33.422  50.848  30.775  37.683  18.125  
G16C26  CG  0.900  1.507  79.347  33.801  50.310  30.514  37.724  17.592 
TA  1.000  1.411  79.452  33.016  50.268  30.302  37.735  18.125  
AT  0.600  1.490  79.733  33.915  50.492  30.521  38.550  18.125  
A17T25  CG  0.200  0.100  81.783  33.375  51.717  30.952  37.014  18.125 
TA  0.100  0.258  81.595  33.370  51.621  30.858  37.171  18.125  
GC  0.200  0.741  79.839  33.332  50.528  30.608  37.202  18.125  
T18A24  CG  2.000  1.549  81.158  33.893  51.430  30.971  38.090  17.592 
AT  2.800  1.747  81.528  33.712  51.592  30.487  37.735  16.526  
GC  1.100  1.382  80.583  33.977  51.012  30.627  38.195  17.592  
A19T23  TA  0.400  1.602  78.329  33.474  49.625  30.680  37.526  18.125 
CG  0.500  0.917  79.871  33.472  50.549  30.684  37.453  18.125  
GC  0.400  0.878  80.353  33.573  50.784  30.669  37.777  18.125  
wild type    0.000  1.120  79.937  33.811  50.610  30.593  38.090  18.125 
 18
single basepair mutants exhibit large (greater than 2.0 kcal/mol) unfavourable free energy of binding. Loss of hydrogen bonds contributes for 1 or 1.5 kcal/mol for mutants 14CG, 8AT, 8GC, 9AT, 18AT, 12TA, 12GC, while for other mutants the most important unfavourable contributions come mostly from Coulombic and van der Waals terms. It should be noted, however, that solvation terms are correlated with Coulombic and van der Waals terms.
This analysis is in general in line with the detailed analysis reported by Oobatake et al. [54], although the exact values of energy contributions differ.
Analysis of molecular dynamics trajectories
The procedure used for computing binding energies may suffer from incomplete relaxation and incomplete conformational sampling. An approach that has been used in the past for sampling more conformations and reduce the effect of fluctuations is to analyse snapshots from molecular dynamics runs. In many studies no scaling factor was applied at all, with good results.
RMSD and correlation coefficients (r) between experimental and calculated values obtained averaging over different time intervals, using the MM/GBSA(+ HB) model.
MM/GBSA+(HB)  

rmsd  r  
0.0–0.5 ns  0.993  0.534 
0.5–1.0 ns  1.126  0.284 
0.0–1.0 ns  1.098  0.356 
Optimal scaling factors for the MM/GBSA(+ HB) model obtained averaging over different time intervals. Standard deviations (see Methods section) are given in parentheses.
MM/GBSA+(HB)  0.0–0.5 ns  0.5–1.0 ns  0.0–1.0 ns 

x _{ Coul }  0.052 (0.028)  0.012 (0.024)  0.039 (0.029) 
x _{ vdW }  0.118 (0.049)  0.039 (0.055)  0.060 (0.064) 
x _{ GB }  0.054 (0.029)  0.014 (0.025)  0.040 (0.031) 
x _{ H }  0.001 (0.005)  0.007 (0.005)  0.004 (0.006) 
x _{ P }  0.013 (0.004)  0.001 (0.004)  0.007 (0.005) 
x _{ HP }  0.039 (0.120)  0.078 (0.107)  0.027 (0.145) 
x _{ const }  10.184 (6.002)  3.055 (4.486)  2.953 (6.527) 
In order to verify whether this problem could have been circumvented using a larger conformational sampling, the simulations of 10 mutants have been extended to 4 ns, obtaining a total of 400 snapshots for every simulation. In particular we extended the simulations of the wild type complex and the best and the worst mutants (G17C25 and T14A28 respectively) with negative results. Although the system is most probably not fully equilibrated, it is reasonable to suspect that even longer (in the range of few tens ns) molecular dynamics simulations will not improve the results obtainable on the starting structures. The main reasons of failure of this approach are probably the large conformational fluctuations developing in MD simulations and the combination of relatively short molecular dynamics simulations with snapshots energy evaluation using the MM/GBSA(+HB) continuum model. Large conformational fluctuations observed in MD simulations are reflected in energetic fluctuations in the range of tens of kcal/mol, thus posing an issue on the reliability of the free energy average values. Moreover, since we observed that the results could not be improved extending the simulation time, it is reasonable to ascribe the failure of the method, at least partially, to inaccuracies in the force field parametrization. Actually, all force fields are based on numerous approximations, in particular nucleic acid force fields could suffer from two main problems which could give rise to inaccuracies. The first is that the target experimental data used in the optimization process are typically crystal structures of DNA and RNA. However, the presence of the lattice environment in crystals is known to influence the structure of DNA, limiting the transferability of crystal data to solution. The second is the treatment of electrostatics which is crucial in these simulations, given the polyanionic nature of DNA. In particular, the electrostatic polarization, which is an effect that can significantly reduce electrostatic interactions of partial atomic charges, is very important for accurate treatment of interactions in different environments, since significant structural changes of DNA may occur in response to environment.
Correct predictions
Number of correct predictions of every model. Parenthetical data correspond to the number of prediction separated by less than 0.3 kcal/mol from the experimental data.
ΔG_{ calc }< 1.0, ΔG_{ exp }< 1.0  ΔG_{ calc }> 1.0, ΔG_{ exp }> 1.0  ΔG_{ calc }≶ 1.0, ΔG_{ exp }≷ 1.0, D < 0.5  tot  (D < 0.5)  

MM/DDDCOONS (1r)  10  23  2  35  (7) 
MM/DDDCOONS (2r)  15  24  0  39  (11) 
MM/DDDCOONS (4r)  16  22  1  39  (13) 
MM/DDDCOONS (8r)  14  23  1  38  (16) 
MM/DDDCOONS(+HB) (1r)  8  23  2  33  (9) 
MM/DDDCOONS(+HB) (2r)  14  23  1  38  (8) 
MM/DDDCOONS(+HB) (4r)  17  21  1  39  (10) 
MM/DDDCOONS(+HB) (8r)  15  22  2  39  (13) 
MM/DDDCHP (1r)  11  23  1  35  (12) 
MM/DDDCHP (2r)  16  21  4  41  (11) 
MM/DDDCHP (4r)  13  21  1  35  (12) 
MM/DDDCHP (8r)  13  24  0  37  (12) 
MM/DDDCHP(+HB) (1r)  12  23  1  36  (11) 
MM/DDDCHP(+HB) (2r)  15  22  3  40  (11) 
MM/DDDCHP(+HB) (4r)  13  21  2  36  (10) 
MM/DDDCHP(+HB) (8r)  16  24  1  38  (13) 
MM/GBSA  13  24  1  38  (10) 
MM/GBSA(+HB)  13  24  2  39  (13) 
Molecular dynamics trajectories were analysed similarly, using average values for the different contributions to the free energy of binding. In particular the lowest number of correct predictions has been obtained averaging over the time interval 0.5–1.0 ns, actually there is no cases in which both ΔG_{ exp }and ΔG_{ calc }are <1.0 kcal/mol and the number of cases in which ΔG_{ exp }and ΔG_{ calc }are separated by less than 0.3 kcal/mol has been strongly reduced.
Generally, we observed that the number of cases in which ΔG_{ exp }and ΔG_{ calc }are both <1.0 kcal/mol decreases while the number of cases in which ΔG_{ exp }and ΔG_{ calc }are both > 1.0 kcal/mol remains nearly constant; however at the same time the number of cases in which ΔG_{ exp }and ΔG_{ calc }are separated by less than 0.3 kcal/mol strongly decreases, indicating that there is a reduction of the accuracy in reproducing experimental binding energies. Overall this analysis is consistent with the analyses reported in the previous sections.
Validation of the MM/GBSA model using the CroOR1 complex
The optimal scaling coefficients are likely to depend on the complex and mutants studied. In order to verify that such coefficients do not produce wild results when applied on different complexes with similar binding features we considered the Cro OR1 complexes which was obtained from crystallographic structure (PDB id. code: 6CRO) after mutation of 14 bases. The Cro protein belongs to the same family of λ repressor but to a different domain, according to SCOP classification [67] and it has very limited similarity with λ repressor although they bind DNA in a similar fashion. This system is therefore suited for testing the overall quality of the scaling procedure. Also for Cro a set of measurements for each mutant of the OR1 sequence is available [68]. When all contributions to the binding free energy, computed according to the MM/GBSA(+HB) model, are scaled by the coefficients determined on the λ repressor complexes the computed energies show a remarkable correlation coefficient of 0.62 with the experimental values, although the binding energies are overestimated by approximately 10 kcal/mol. This fact could reflect differences in the entropic contribution to binding (arising from restriction in side chain and backbone mobility) that are likely to be different for the two systems. Indeed, the crystallized Cro protein is roughly only two thirds of the repressor sequence. Notwithstanding the differences in overall binding energy, the binding differences for the mutants are on average reproduced by the energetic model.
As a further test, we performed the reverse analysis where the scaling coefficients are obtained on the CroOR1 complex and validated on the λ repressoroperator complex. Also in this case the computed energies show a remarkable correlation coefficient of 0.69 with the experimental values, although they are all underestimated by approximately 16 kcal/mol.
In order to verify how sensitive the scaling coefficients are to the experimental data used in the fit, we calculated the binding free energies of Cro and each mutant of the OR1 sequence according to the MM/GBSA model, using Eq. 7. Finally we combined the two experimental datasets of λ repressor and Cro and we refitted the model.
As in the previous cases, we calculated the best scaling factors fitting the set of experimentally measured proteinDNA binding affinities (Table 4), then we assessed the quality of ΔG_{ calc }predictions evaluating the linear correlation coefficient r and the root mean square deviation between calculated and experimental values. Finally we verified the performance of the model, using the leaveoneout scheme. The best performance has been obtained for the MM/GBSA(+HB) model, which gives a correlation coefficient r of 0.69 and a rmsd of 0.74 for Cro and a correlation coefficient r of 0.67 and a rmsd of 0.83 for the two combined systems. The same analysis has been performed for 5000 replicas of the dataset with one third of the set left out and used for crossvalidation. The average RMSD and correlation are essentially the same reported for the leaveoneout scheme. From the same analysis variances of the coefficients have been estimated with essentially the same results as those reported in Table 4. As far as the scaling coefficients are concerned (see Table 4), by comparing the results obtained for λ, Cro and the two combined systems, we can observe that the sets of values obtained for λ and λ + Cro are all in the same range except for the constant term, probably as a consequence of the fact that the entropic contribution to binding are likely different for the two systems. However it is worth noting that the standard deviation of this term is very large in both cases. As far as the scaling coefficients obtained for Cro, they are rather different from the others, except for x_{ vdw }and x_{ HB }, which scale the Van der Waals and Hbonds contributions respectively. However we observed that the electrostatic and GB solvation terms are strongly correlated to each other (the linear correlation coefficient is 0.998), as well as the constant term and the polar and hydrophobic surface area terms (the linear correlation of the coefficients is 0.645 and 0.784). The standard deviation of the constant term is also very large (see Table 4). Overall these results validate the approach for predicting binding free energies for similar proteinDNA complexes.
Analysis of nonspecific proteinDNA binding
In order to study nonspecific proteinDNA binding one thousand random DNA sequences have been generated and each sequence has been threaded onto the DNA phosphate backbone of the crystal structure in order to obtain a set of structural models with new DNA sequences. Minimization was performed according to the protocol described in the Methods section. We refer to to this set of complexes as to the "nonspecific" set.
Binding free energies for each member of the generated nonspecific set have been computed according to the MM/GBSA(+HB) model, using the optimal scaling factors determined by fitting the 52 experimental data (see Table 4).
It is interesting to compare the computed free energies of binding for the nonspecific DNA complexes with those expected based on single mutants binding energies under the assumption of additivity. The expected free energies are higher than those computed by optimal scaling of contributions. The average difference, with respect to the specifically bound sequence, are 18.4 kcal/mol and 6.1 kcal/mol, respectively. This has been interpreted as a consequence of the fact that adjacent multiple substitutions may introduce additional energy minima compared to single mutations in a tight complex. This result is in line with the saturation effect in observed vs. predicted binding energy that has been described by Stormo and coworkers [69, 70] and recently experimentally demonstrated [71]. It is also interesting to note that the nonspecific binding energy is comparable to the energy computed by Northrup and coworkers for loosely docked complex of Cro to noncognate DNA [72], which implies that the mode of binding may substantially change for nonspecifically bound DNA sequences. This would be consistent with the capability of the protein of sliding along DNA, which would not be feasible for a tight complex.
Identification of putative transcription factor binding sites
The aim of this section is to understand whether the methods described here can be used for searching genomes for candidate transcription factor binding sites.
 i)
whether the MM/GBSA(+HB) model is able to identify transcription factor binding sites in the absence of thermodynamic data about single basepair mutants, but just knowing the recognized sequence;
ii) whether some predictions can still be afforded in the absence of thermodynamic data and of any information on recognized sequences. The latter situation could be encountered when a model of the complex is built by homology and differences in protein DNAcontacting residues imply a different specificity.
Identification of putative transcription factor binding sites knowing the bound sequence
The analysis in the previous section used knowledge about single basepair mutants which is rarely available. Here we ask what predictions can be made when no thermodynamic data on mutants or wildtype sequence binding is available, but the cognate sequence is available. One thousand random DNA sequences were generated and the corresponding structural models were built by performing mutations on the double stranded DNA in the complex crystal structure using the program WHATIF [73]. Structures were energy minimized using the same protocol used for the MM/GBSA(+HB) methodology. Assuming that random sequences will have a larger free energy of binding compared to the bound sequence, optimal scaling parameters were sought in order to make the free energy difference in binding with respect to the naturally occurring complex equal to 10.0 kcal/mol. This value is arbitrary, albeit not unrealistic. Eq. 7 is solved (in a least square sense) subtracting the row corresponding to the wildtype complex from all other rows, and fixing all the energy differences equal 10.0. The differences in Coulombic, van der Waals, GB solvation energy, polar and apolar surface area and number of hydrogen bonds, with respect to wildtype complex, have been tabulated and the optimal scaling parameters have been determined.
The free energies computed on the random sequences have been used to compute single basepair mutant free energies as described in the Methods section. The single basepair mutant energies for the wild type sequence have been reset to 0.0 (this assumes that the specific bound sequence is known) and the lowest computed single basepair mutant binding energy has been subtracted from all other values.
The plot of computed single basepair mutant energies vs. experimental energies (computed under the hypothesis of additivity) shows a good correlation (0.58) but seems insufficient for predictive purposes. However, when the bacteriophage λ genome is scanned using the corresponding free energy matrix (see Methods), highaffinity binding sites are correctly recognized, and in general the energies computed using the matrix and those predicted based on addition of single basepair mutation effects are well correlated (corr. coeff. 0.74).
We asked what is the advantage of such computation compared to the simpler model that assigns a constant energy penalty to each mutation over the specific bound sequence. In such case the correlation between the computed and reference binding energies is slightly lower, but still significant (0.72). The advantage of using computational results over a much simpler single parameter approach seems therefore very limited, although the 1% best sites predicted by the MM/GBSA(+HB) energy and the simple mutation models display only 15% common sites, proving that the two methods are largely uncorrelated.
Identification of putative transcription factor binding sites without knowing the bound sequence
As a last test we simulate a realistic situation in which no thermodynamic data or information on the recognized sequences are available. We considered the set of one thousand random DNA sequences and the corresponding structural models built by performing mutations on the double stranded DNA in the complex crystal structure as the only information available. Obviously the crystallographic complex does contain information on the specific sequence because protein and DNA conformations are fitting each other in the complex. If nonspecific complexes were to be built by homology without knowing the exact DNA sequence bound, it is likely that side chains would be placed differently with different results. Finally, structures were energy minimized using the same protocol used for the MM/GBSA(+HB) methodology. As in the tests above we found optimal scaling factors in order to make all (nonspecific) binding free energies equal to 10.0 kcal/mol.
In order to avoid a trivial solution to the fitting problem with all coefficients equal 0.0 except the constant term equal 10.0, we follow a twostep procedure. In the first step we assume a reasonable value (30 kcal/mol) for the constant term which must be brought to the lefthand side of Eq. 7. Coulombic, van der Waals, GB solvation energy, polar and apolar surface area and number of hydrogen bonds have been evaluated, Eq. 7 is then solved (in a least square error sense) and the optimal scaling parameters have been determined. The lowest binding energy sequence according to the scaling parameters is determined. The row corresponding to this complex is subtracted from all other rows thus removing the constant term. In the second step the newly obtained matrix, which does not include the constant term anymore is used to find the best coefficients to make all the energy differences equal 10.0 kcal/mol. Therefore all energies are expressed relative to the lowest computed energy at the first step.
Conclusion
In the present work physical effective energy functions are used to estimate the free energy of binding of λ repressor to the DNA operator and single basepair mutants, for which thermodynamic data are available. Thermodynamic data allow one to study the best results achievable, with the modeling approach and energy functions presented here, with models that assume that the binding energy is a linear combination of different contributions.
Simple models that use a distance dependent dielectric constant and simple terms for surface area proportional energy contributions and for hydrogen bonding perform surprisingly well for values of ε ranging from 2r to 8r.
A twoparameter model for surface area proportional energy contributions performs better than the more complex model of Oobatake et al. [55], which was however not derived for usage in the more complex energy functions employed here.
The performance of MM/GBSA(+HB) and to a lesser extent MM/GBSA model is comparable to or superior to other models. A conclusion for the MM/GBSA model is that electrostatic energies should be reduced by a proper scaling factor corresponding to dielectric constants in the range of 6. This conclusion is reached also by a similar analysis of protein Crooperator mutants.
The effect of molecular dynamics on the computed binding free energies is in general negative and the reproducibility of the experimental values decreases with the increase of simulation time considered. This may be a consequence of the large fluctuations developing in MD simulations which probably would require a much longer simulation time. Moreover it is reasonable to take into account that the poor performance of the method can be partially caused by the errors in the force field used in MD simulations. Another plausible source of inaccuracy is the mismatch between the energy model and system representation used in MD simulation and those used for minimization and energy evaluation. It appears therefore that it is worth to invest more time in optimizing the starting structure, rather than for sampling the conformational space by molecular dynamics simulations, or, alternatively, to adopt different strategies for sampling protein and DNA flexibility [75].
The analysis of nonspecific complexes using the best performing energetic model with properly scaled coefficients allows to evaluate a nonspecific binding energy difference, with respect to the specific bound sequence, of 6.06 ± 2.17 kcal/mol, definitely lower than what expected based on an additive model (18.1 kcal/mol for the single basepair mutants computed energies). This result is in line with the saturation effect described by Stormo and coworkers [69, 70] and with the theoretical analysis of Bakk and Melzer [66].
Although the results presented on single basepair mutants are not exciting, using computational methods may be very useful for identifying transcription factor binding sites.
When no thermodynamic data are available but the specific bound sequence is known the computed MM/GBSA(+HB) free energies are slightly more predictive than a simple substitution profile which assigns a penalty for any point mutation.
The most interesting test performed here considers a realistic scenario where no information on the bound sequence is available. Even in this case MM/GBSA(+HB) energies are predictive.
This result has important consequences for the prediction of transcription factor binding sites which often use consensus methods. A prerequisite for the usefulness of consensus methods is that these are as independent of each other as possible. Since most methods use common prior knowledge and often related statistical methods, independence is not guaranteed. Methods which are based on completely independent principles, like those based on physical effective energy functions and free energy computations, offer a completely complementary methodology for deriving profile matrices for scanning entire genomes. The results reported here, with much caution because the structural model for the specific bound sequence is known and not modeled by homology or other methods, support usage of these methods for the identification of DNAbinding protein target sequences. In view of the very recent impressive results reported by the group of Baker [76] it is apparent that significant improvements to the approach described in this paper may be obtained by extensive refinement and screening of protein side chain conformation at proteinDNA interface.
Methods
Model building
Molecular Dynamics Simulations
Hydrogen atoms have been added using the program pdb2gmx of the GROMACS package [79, 80]. Every structure has been optimized performing 200 steps of energy minimization using the NAMD program, fixing all C_{ α }carbons and phosphate groups coordinates. A dielectric constant of 10 has been employed with a cutoff of 12 Å for nonbonded interactions.
The net charge of the system (36) has been neutralized placing a corresponding number of sodium counterions in energetically favourable positions. The electrostatic potential was calculated via numerical solutions of the PoissonBoltzmann equation using the University of Houston Brownian Dynamics (UHBD, version 6.x) program [81, 82]. A counterion was placed at the lowest potential position at 7.0 Å from any heavy atom of the solute. The cycle was repeated until the net charge of the system was 0.
The complex and counterions were solvated in a box of TIP3P [83] water molecules using the solvate module in the program VMD [84]. The resulting system contained about 4200 solute atoms and 50400 solvent atoms. The coordinates of the solute were fixed and the solvent was energy minimized using 100 steps of conjugate gradient. A solvent equilibration was carried out by performing molecular dynamics for 50 ps using a 1 fs time step to let the water molecules move to adjust to the conformation of the solute. The system was then energy minimized using 100 steps of conjugate gradient and, after 100 ps equilibration, 1ns MD simulations was performed using a 2fs timestep. A snapshot of the trajectory was stored every 10 ps for later analysis. The shakeH algorithm was used in order to fix bond length between each hydrogen and its mother atom to its nominal value and to extend the simulation timestep [85]. All molecular dynamics simulations of the complex were run under constant NPT conditions using the NAMD program [86]. The pressure of the system was coupled, through a Berendsenthermostat [87], to a pressure bath with target pressure 1.01325 bar and time constant 100 fs. The temperature has been kept to 300 K by simple velocity rescaling every picosecond. Longrange electrostatic interactions were treated by particle mesh Ewald (PME) method [88] employing a grid of 128 × 128 × 128 points. The cutoff was 12 Å and the tolerance was 10^{6} which resulted in an Ewald coefficient of 0.257952. The order for PME interpolation was 4.
The simulations were performed on a cluster composed by ten dualprocessor nodes based on Intel XeonTM 2.8 GHz, with hyperthreading technology.
Free energy calculations
It has been assumed that the entropy restriction in internal degrees of freedom and overall rotation and translation degrees of freedom is the same for all complexes.
The effect that association has on intramolecular energy has been neglected. Moreover no extended conformational search has been performed for protein side chains and DNA, partly because this task is not easily accomplished and partly because large conformational changes often result in large molecular mechanics energy changes, so we aimed at keeping the systems to be compared as close as possible. The free energy of binding has been calculated using different methodologies detailed below. For all models alternative versions in which an energy term proportional to the number of hydrogen bonds has been added have been considered.
Except where noted, all contributions to the free energy of binding have been optimally scaled in order to best reproduce available experimental data (see later).
MM/DDDCOONS
In this method [54] electrostatic interactions have been estimated using a distance dependent dielectric constant (DDDC) while the solvation energy is proportional to the solvent accessible surface area through the atomic solvation parameters of Oobatake, Ooi, Nemethy and Scheraga (OONS) [55].
The proportionality constants ${g}_{i}^{hyd}$ have been determined from thermodynamic data on the transfer of small molecules from the gas phase into aqueous environment assuming the additivity of contributions from individual groups [55].
MM/DDDCHP
In a very similar approach the OONS 9parameter solvation model has been replaced by a simpler 2parameter hydrophobic, polar (HP) solvation model. Energy minimization protocol and tested values are the same as for the MM/DDDCOONS for proper comparison.
MM/GBSA
The polar term is computed using the Generalized Born approach [92]. All complexes have been energy minimized by 200 conjugate gradients minimization steps using the generalized Born model as implemented in the CHARMM program, then the solute and solvation energy terms have been computed for both the complex and the isolated molecules. The binding energy was then computed by subtraction. Doubling the number of minimization steps does not affect significantly the results.
The nonpolar term G^{nonpolar}, which takes into account the tendency of the nonpolar parts of the molecule to collapse, is taken to be proportional to the solventaccessible surface area A, i.e. G^{nonpolar}= γA, where the surface tension coefficient γ has been empirically determined to be equal to 20 cal Å ^{2} mol^{1} for this kind of applications [93].
A variant of this methodology including splitting the solvent accessible surface area into a polar and a hydrophobic contribution (i.e. using two different surface tension coefficients), and including a term proportional to the number of hydrogen bonds has been considered here.
Finding optimal scaling factors
where Δ represents the difference between the complex and the isolated protein and DNA molecules. Coefficients x_{1},...,x_{ n }have been found in order to best reproduce the 52 experimentally available free energies of binding. Contributions have been arranged in a 52 × n matrix A where each row corresponds to each structural model and each column corresponds to a different contribution to the free energy of binding. The experimental binding free energies have been arranged in a 52component vector ΔG_{ exptl }. The linear systemAx = ΔG_{ exptl }
where x is the ncomponent vector of coefficients, has been solved (in a least square sense) using singular value decomposition [94] and the best x_{ i }coefficients have been used to calculate binding energies ΔG_{ calc }. A constant term takes into account the entropy loss upon complexation and other possible contributions identical for all complexes.
A linear model, compared to more sophisticated methods, has the advantage that the number of adjustable parameters is limited and easily interpretable in physical terms.
In the following we detail the contributions considered for each energetic model.
The free energy of binding has been computed for the MM/DDDCOONS model according to the following equation:ΔG_{MM/DDDCOONS}= x_{ vdW }ΔE_{ vdW }+ x_{ DDDC }ΔE_{elec,DDDC}+ x_{ OONS }ΔG_{ OONS }+ x_{const.}(+x_{ HB }N_{ HB })
where ΔE_{ vdW }is the van der Waal contribution, ΔE_{elec,DDDC}is the Coulombic energy, computed with a distance dependent dielectric constant, ΔE_{ OONS }is the solvation energy according to the Oobatake et al. model [55] and N_{ HB }is the number of intermolecular hydrogen bonds.
As mentioned above, the coefficients bear physical meaning. For instance the term x_{ const }should account for rotational and translational entropy loss upon binding and it can be expected to be in the range 20–40 kcal/mol.
The term proportional to the number of hydrogen bonds was alternatively added in order to take into account possible inaccuracies in the treatment of these interactions by molecular mechanics and solvation terms. In practice every time this term is added the coefficients of molecular mechanics and solvation terms are greatly reduced thus avoiding double counting of hydrogen bond interactions.
A similar expression for the free energy of binding has been used for the MM/DDDCHP model:ΔG_{MM/DDDCHP}= x_{ vdW }ΔE_{ vdW }+ x_{ DDDC }ΔE_{elec,DDDC}+ x_{ H }ΔA_{ H }+ x_{ P }ΔA_{ P }+ x_{const.}(+x_{ HB }N_{ HB })
Here the coefficients x_{ H }and x_{ P }represent the surface tension coefficients multiplying hydrophobic and polar solvent accessible surface areas ΔA_{ H }and ΔA_{ P }, respectively. We expect these coefficients to be in the range of tens of cal Å^{2} mol^{1}.
The solvent accessible area has been also splitted in polar and hydrophobic area for finding optimal scaling parameters for the MM/GBSA methodology:ΔG_{MM/GBSA}= x_{ vdW }ΔE_{ vdW }+ x_{ Coul }ΔE_{ Coul }+ x_{ GB }ΔG_{ GB }+ x_{ H }ΔA_{ H }+ x_{ P }ΔA_{ P }+ x_{const.}(+x_{ HB }N_{ HB })
where ΔG_{ GB }is the generalized Born solvation energy. The coefficients x_{ Coul }and x_{ GB }are exactly and roughly, respectively, inversely proportional to the effective dielectric constant and are thus expected to be in the range 0.05 to 1.0.
Possible pitfalls of the method
Scaling energy terms for free energy evaluation of models which have been minimized without scaling such terms is clearly inconsistent. A correct procedure would be to iteratively find the optimal scaling factors, minimizing the energy using such scaling factors and repeating these two steps until convergence. This procedure faces some difficulties because an important term like the hydrogen bond term is discrete and does not have a counterpart in standard forcefields, where such interactions are described typically through electrostatic and van der Waals terms. Similarly the minimization of terms proportional to the solvent accessible surface area requires algorithms which are rarely available in molecular mechanics packages. A further difficulty is that any unbalance among forcefield terms might introduce distortions in molecular structure, notably of hydrogen bond lengths. Although the issue of iteratively fitting optimal scaling factors is worth being further investigated, here the approach of scaling factors has been applied in a more rough way. We have matched as far as possible the energetic model used for minimization with that used for fitting scaling factors, as mentioned above, but we have not minimized again the models using the scaling factors. A similar mismatch between conformational sampling and energy evaluation is implicit in the analysis of molecular dynamics snapshots. Other sources of error in this case are the large conformational (and energetic) fluctuations molecules undergo during simulation and in general the inaccuracy of implicit solvent methods (used in energy evaluation) where small energy differences arise from subtraction of rather large values. It should be noted that for molecular dynamics snapshots inaccuracies do not cancel out because there are no restrained parts in the molecules.
DNA sequence dependent deformability
An important aspect of proteinDNA interaction, addressed quantitatively by Olson and coworkers [39], is the capability of DNA sequences to adopt specific local conformations. The statistics of parameters and pairwise parameter correlations shows definite preferences. In the approach described above, changes in intramolecular energy terms are disregarded altogether by the assumption of rigid docking. The strains introduced in complex molecular structures, however, are typically relaxed over the structure and should have consequences on the intermolecular energy terms too. In order to assess the effect of DNA sequence dependent deformability we followed the approach of Olson and coworkers [39], who made available average parameters for the six parameters describing local geometry of a basepair step in BDNA, the force constant parameters for all pairwise deviation from equilibrium values and a program to analyse DNA structures [95].
The analysis was performed for the native structure parameters, simply replacing the identity of the basepair mutated, and on the mutated structures, minimized using the generalized Born model. For both cases poor correlation with experimental binding data was found. Remarkably, however, the native sequence was the third lowest energy sequence among all 52 sequences. Energy minimization in general increases the energy associated with the deformability of DNA. Computation of the fitness of a sequence to local geometry parameters gives important informations although it is likely that the computed energy is not accurate for conformations far from equilibrium. Inclusion of the DNA sequence dependent deformability energy in the analyses detailed below did not improve results significantly, notwithstanding the additional scaling parameter introduced for this purpose. For this reason this term was not considered further.
Performance analysis
After fitting scaling factors to experimental data, the root mean square difference between calculated and experimental data was computed. This quantity can provide however a poor evaluation of the predictive power of the calculations when the test systems are very similar. Therefore the correlation coefficient between calculated and experimental data was also computed. Optimal scaling factors were computed taking all the data available.
 i)
a leaveoneout scheme has been adopted. All but one of the data were taken and the root mean square difference and correlation coefficient were computed using the set of data not used in the fitting procedure. The same scheme has been applied to 5000 replicates with one third of the data left out of the fitting procedure and used for RMSD and correlation coefficient computation.
The different models considered employ a different number of fitting parameters and therefore different performances are expected. Although these parameters are often correlated, the analysis of the variance gives an immediate clue as to which variables are more important.
iii) analysis of variance (ANOVA) calculations have been performed and a significance test based on the Fstatistic and the corresponding confidence level has been computed [96].
iv) one thousand replicates of the original data has been generated with the column elements containing the experimental data randomly swapped. The average of the correlation coefficient between swapped experimental data and fitted data has been computed together with the standard deviation. The results of this computation (not reported) fully supports the results of the statistical analyses described above; Finally, a useful alternative to assess the quality of ΔG_{ calc }predictions and to compare the different models from a qualitative point of view, consists in determining the number of "correct predictions", defined as the number of cases in which both ΔG_{ exp }and ΔG_{ calc }are <1.0 kcal/mol, or >1.0 kcal/mol, or else separated by less than 0.3 kcal/mol. The threshold value of 1.0 kcal/mol requires some explanations. The experimental values of the free energy change relative to the wildtype operator O_{ R }1 have been calculated using the equation ΔG_{ exp }= 0.546 ln (K_{ d }of substituted sequence)/(K_{ d }of O_{ R }1) after having determined the dissociation constant of every mutant. It is simple to verify that the threshold value of 1.0 kcal/mol corresponds to a remarkable reduction in the dissociation constant of the mutant (ca. 5fold), with respect to the dissociation constant of the wildtype operator (K_{ d }of O_{ R }1 = 10^{9}), whereas values of ΔG higher than 1.0 kcal/mol correspond to a reduction in the dissociation constant from 5 (ΔG_{ exp }= 1.0 kcal/mol) to 25fold (ΔG_{ exp }= 3.4, which is the maximum value of ΔG_{ exp }). Therefore it is reasonable to define ΔG_{ calc }as correct, if both ΔG_{ exp }and ΔG_{ calc }are in one of the defined intervals or even if the difference D = ΔG_{ exp } ΔG_{ calc } is lower than 0.3 kcal/mol, which corresponds to a ratio between the dissociation constant of a mutant and the dissociation constant of the wildtype complex lower than 2.0.
Analysis of nonspecific proteinDNA binding
Averages are performed over the one thousand random sequences. A large Zscore implies that the specific bound sequence can be distinguished from other nonspecific bound sequences. The structures were energy minimized using the same protocol used for MM/GBSA free energy estimation. For all minimized complexes the Coulombic energy, van der Waals energy, GB solvation energy, polar and apolar surface accessible area and intermolecular hydrogen bonds number were tabulated. For each model i of the 1000 random DNA sequence complexes the binding energy G(i) has been computed using different amounts of the experimental information available. Different analyses, detailed in the Results section, were performed. The possibility of using the data computed on the set of nonspecific complexes for defining a profile of the recognized DNA sequences has been explored as follows. The calculated binding energy values for the set of nonspecific complexes were summarised in a set of 68 values corresponding to the average contribution to the binding free energy of each possible of the 4 bases at each of the possible 17 bound sequence positions. These 68 values have been derived as follows. Possible substitutions are indexed from 1 to 4 for A, C, G and T, respectively. A 1000 × 68 matrix A was set where each element A(i, j) is 1.0 or 0.0 if the base at position j/4 (rounded at the closer upper integer) has index j mod 4 in sequence i. The set of 68 substitution free energies x(j) were found by solving (in a root mean square error sense) the overdetermined equation Ax = G. The resulting 68element vector x was arranged in a 17 × 4 matrix. Variants on this procedure are described in the Results section according to the level of information available included in the analysis.
Scanning of bacteriophage λ genome
The free energy matrix derived from the analysis of nonspecific proteinDNA complexes was used to score all 17mer subsequences in the bacteriophage λ genome (Accession number: NC_001416.1, 48502 basepairs) on both strands. In principle the score represents the free energy of binding of the 17mer considered.
Reference "experimental" binding free energy values, for comparison with computed data, were obtained under the hypothesis of additivity [69, 70] using experimental data on single basepair mutants.
Declarations
Acknowledgements
We wish to thank Drs. G. Tecchiolli and P. Zuccato of Exadron, the HPC Division of the Eurotech Group, for providing hardware and expert technical assistance.
Dr. M. Isola of the University of Udine is gratefully acknowledged for helpful discussions on statistical aspects of multiple regressions.
EM wishes to thank Profs. C. Destri, G. Marchesini and F. Rapuano for helpful discussions.
Part of the research was funded by FIRB grant RBNE03B8KK from the Italian Ministry for Education, University and Research.
Authors’ Affiliations
References
 Pabo C, Sauer RT: Transcription factors: structural families and principles of DNA recognition. Ann Rev Biochem 1992, 61: 1053–1095.View ArticlePubMedGoogle Scholar
 Matthews B: ProteinDNA interaction. No code for recognition. Nature 1998, 335: 294–5.View ArticleGoogle Scholar
 Pabo CO, Sauer RT: ProteinDNA recognition. Ann Rev Biochem 1984, 53: 293–321.View ArticlePubMedGoogle Scholar
 Harrison SC, Aggarwal AK: DNA recognition by proteins with the helixturnhelix motif. Ann Rev Biochem 1990, 59: 933–969.View ArticlePubMedGoogle Scholar
 Gromiha MM, Siebers JG, Selvaraj S, Kono H, Sarai A: Intermolecular and intramolecular readout mechanisms in proteinDNA recognition. J Mol Biol 2004, 337: 285–294.View ArticleGoogle Scholar
 Kono H, Sarai A: Structurebased prediction of DNA target sites by regulatory proteins. Proteins 1999, 35: 114–131.View ArticlePubMedGoogle Scholar
 Wassermann WW, Sandelin A: Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet 2004, 5: 276–287.View ArticleGoogle Scholar
 Pennacchio LA, Rubin EM: Genomic strategies to identify mammalian regulatory sequences. Nat Rev Genet 2001, 2: 100–109.View ArticlePubMedGoogle Scholar
 Sinha S, Tompa M: Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2002, 30: 5549–5560.PubMed CentralView ArticlePubMedGoogle Scholar
 Sinha S, Tompa M: YMF: A program for discovery of novel transcription factor binding sites by statistical overrepresentation. Nucleic Acids Res 2003, 31: 3586–3588.PubMed CentralView ArticlePubMedGoogle Scholar
 Birnbaum K, Benfey PN, Shasha DE: cis element/transcription factor analysis (cis/TF): a method for discovering transcription factor/cis element relationships. Genome Res 2001, 11: 1567–1573.PubMed CentralView ArticlePubMedGoogle Scholar
 Wolfsberg TG, Gabrielian AE, Campbell MJ, Cho RJ, Spouge JL, Landsman D: Candidate regulatory sequence elements for cell cycledependent transcription in Saccharomyces cerevisiae. Genome Res 1999, 9: 775–792.PubMed CentralPubMedGoogle Scholar
 Caselle M, Di Cunto F, Provero P: Correlating overrepresented upstream motifs to gene expression: a computational approach to regulatory element discovery in eukaryotes. BMC Bioinformatics 2002, 3: 7.PubMed CentralView ArticlePubMedGoogle Scholar
 Cora' D, Di Cunto F, Provero P, Silengo L, Caselle M: Computational identification of transcription factor binding sites by functional analysis of sets of genes sharing overrepresented upstream motifs. BMC Bioinformatics 2004, 5: 57.View ArticleGoogle Scholar
 van Helden J, Andre B, ColladoVides J: Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J Mol Biol 1998, 281: 827–842.View ArticlePubMedGoogle Scholar
 Jensen LJ, Knudsen S: Automatic discovery of regulatory patterns in promoter regions based on whole cell expression data and functional annotation. Bioinformatics 2000, 16: 326–333.View ArticlePubMedGoogle Scholar
 Lawrence CE, Reilly AA: An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences. Proteins 1990, 7: 41–51.View ArticlePubMedGoogle Scholar
 Thijs G, Marchal K, Lescot M, Rombauts S, Moor BD, Rouze P, Moreau Y: A Gibbs sampling method to detect overrepresented motifs in the upstream regions of coexpressed genes. J Comput Biol 2002, 9: 447–464.View ArticlePubMedGoogle Scholar
 Thompson W, Rouchka EC, Lawrence CE: Gibbs Recursive Sampler: finding transcription factor binding sites. Nucleic Acids Res 2003, 31: 3580–3585.PubMed CentralView ArticlePubMedGoogle Scholar
 Hughes JD, Estep PW, Tavazoie S, Church GM: Computational identification of cisregulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol 2000, 296: 1205–14.View ArticlePubMedGoogle Scholar
 Hardison R: Conserved noncoding sequences are reliable guides to regulatory elements. Trends Genet 2000, 16: 369–372.View ArticlePubMedGoogle Scholar
 Duret L, Dorkeld F, Gautier C: Strong conservation of noncoding sequences during vertebrates evolution: potential involvement in posttranscriptional regulation of gene expression. Nucleic Acid Res 1993, 21: 2315–2322.PubMed CentralView ArticlePubMedGoogle Scholar
 Loots GG, Locksley RM, Blankespoor CM, Wang ZE, Miller W, Rubin EM, Frazer KA: Identification of a coordinate regulator of interleukins 4, 13, and 5 by crossspecies sequence comparisons. Science 2000, 288: 136–140.View ArticlePubMedGoogle Scholar
 Goettgens B, Barton L, Gilbert J, Bench A, Sanchez M, Bahn S, Mistry S, Grafham D, McMurray A, Vaudin M, Amaya E, Bentley D, Green A, Sinclair A: Analysis of vertebrate scl loci identifies conserved enhancers. Nat Biotechnol 2000, 18: 181–186.View ArticleGoogle Scholar
 Flint J, Tufarelli C, Peden J, Clark K, Daniels R, Hardison R, Miller W, Philipsen S, TanUn K, McMorrow T, Frampton J, Alter B, Frischauf A, Higgs D: Comparative genome analysis delimits a chromosomal domain and identifies key regulatory elements in the alpha globin cluster. Hum Mol Genet 2001, 10: 371–382.View ArticlePubMedGoogle Scholar
 Lenhard B, Sandelin A, Mendoza L, Engström P, Jareborg N, Wasserman WW: Identification of conserved regulatory elements by comparative genome analysis. J Biol 2003, 2: 13.PubMed CentralView ArticlePubMedGoogle Scholar
 Zhang Z, Gerstein M: Of mice and men: phylogenetic footprinting aids the discovery of regulatory elements. J Biol 2003, 2: 11.PubMed CentralView ArticlePubMedGoogle Scholar
 Cora' D, Herrmann C, Dieterich C, Di Cunto F, Provero P, Caselle M: Ab initio identification of putative human transcription factor binding sites by comparative genomics. BMC Bioinformatics 2005, 6: 110.View ArticleGoogle Scholar
 Sandelin A, Wasserman WW, Lenhard B: ConSite: webbased prediction of regulatory elements using crossspecies comparison. Nucleic Acids Res 2004, 32: W249W252.PubMed CentralView ArticlePubMedGoogle Scholar
 Prakash A, Blanchette M, Sinha S, Tompa M: Motif discovery in heterogeneous sequence data. Pac Symp Biocomput 2004, 348–59.Google Scholar
 Tompa M, Li N, Bailey TL, Church GM, Moor BD, Eskin E, Favorov AV, Frith MC, Fu Y, Kent WJ, Makeev VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Regnier M, Simonis N, Sinha S, Thijs G, van Helden J, Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu Z: Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 2005, 23: 137–144.View ArticlePubMedGoogle Scholar
 Lazaridis T, Karplus M: Effective energy functions for protein structure prediction. Curr Op Struct Biol 2000, 10: 139–45.View ArticleGoogle Scholar
 Sippl M: Calculation of conformational ensembles from potentials of mean force. An approach to the knowledgebased prediction of local structures in globular proteins. J Mol Biol 1990, 213: 859–883.View ArticlePubMedGoogle Scholar
 Lustig B, Jernigan RL: Consistencies of individual DNA baseamino acid interactions in structures and sequences. Nucleic Acids Res 1995, 23: 4707–4711.PubMed CentralView ArticlePubMedGoogle Scholar
 Kaplan T, Friedman N, Margalit H: Ab initio prediction of transcription factor targets using structural knowledge. PLoS Comput Biol 2005, 1(1):e1.PubMed CentralView ArticlePubMedGoogle Scholar
 MandelGutfreund Y, Margalit H: Quantitative parameters for amino acid base interaction: implications for prediction of protein DNA binding sites. Nucleic Acids Res 1998, 26: 2306–2312.PubMed CentralView ArticlePubMedGoogle Scholar
 Liu Z, Mao F, Guo J, Yan B, Wang P, Qu Y, Xu Y: Quantitative evaluation of proteinDNA interactions using an optimized knowledgebased potential. Nucleic Acids Res 2005, 33: 546–558.PubMed CentralView ArticlePubMedGoogle Scholar
 Zhang C, Liu S, Zhu Q, Zhou Y: A knowledgebased energy function for proteinligand, proteinprotein and proteinDNA complexes. J Med Chem 2005, 48: 2325–2335.View ArticlePubMedGoogle Scholar
 Olson WK, Gorin AA, Lu X, Hock LM, Zhurkin VB: DNA sequencedependent deformability deduced from proteinDNA crystal complexes. Proc Natl Acad Sci USA 1998, 95: 11163–11168.PubMed CentralView ArticlePubMedGoogle Scholar
 Zhou H, Zhou Y: Distancescaled, finite idealgas reference state improves structurederived potentials of mean force for structure selection and stability prediction. Protein Sci 2002, 11: 2714–2726.PubMed CentralView ArticlePubMedGoogle Scholar
 SchuelerFurman O, Wang C, Bradley P, Misura K, Baker D: Progress in modeling of protein structures and interactions. Science 2005, 310: 638–642.View ArticlePubMedGoogle Scholar
 Wang W, Donini O, Reyes C, Kollman P: Simulations of enzyme catalysis, proteinligand, proteinprotein, and proteinnucleic acid noncovalent interactions. Ann Rev Biophys Biomol Struct 2001, 30: 211–43.View ArticleGoogle Scholar
 Kollman P, Massova I, Reyes C, Kuhn B, Huo S, Chong L, Lee M, Duan Y, Wang W, Donini O, Cieplak P, Srnivasan J, Case D, Cheatham T: Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc Chem Res 2000, 33: 889–897.View ArticlePubMedGoogle Scholar
 Zacharias M, Luty B, Davis M, McCammon J: PoissonBoltzmann analysis of the lambda repressoroperator interaction. Biophys J 1992, 63: 1280–1285.PubMed CentralView ArticlePubMedGoogle Scholar
 Misra V, Hecht J, Sharp K, Friedman R, Honig B: Salt effects on proteinDNA intractions. The lambda cI repressor and EcoRI endonuclease. J Mol Biol 1994, 238: 263–80.Google Scholar
 Fogolari F, Elcock A, Esposito G, Viglino P, Briggs J, McCammon J: Electrostatics in the homeodomainDNA interaction. J Mol Biol 1997, 267: 368–381.View ArticlePubMedGoogle Scholar
 Baginski M, Fogolari F, Briggs J: Electrostatic and nonelectrostatics contributions to the binding free energies of anthracycline antibiotics to DNA. J Mol Biol 1997, 274: 253–267.View ArticlePubMedGoogle Scholar
 Froloff N, Windemuth A, Honig B: On the calculation of binding free energies using continuum methods: application to MHC class I proteinpeptide interactions. Protein Sci 1997, 6: 1293–1301.PubMed CentralView ArticlePubMedGoogle Scholar
 Misra V, Hecht J, Yang A, Honig B: Electrostatic contributions to the binding free energy of the lambdacI repressor to DNA. Biophys J 1998, 75: 2262–2273.PubMed CentralView ArticlePubMedGoogle Scholar
 Wojciechowski M, Fogolari F, Baginski M: Thermodynamic and electrostatic properties of ternary Oxytricha nova TEBPDNA complex. J Struct Biol 2005, 152: 169–184.View ArticlePubMedGoogle Scholar
 Jayaram B, McConnell K, Dixit SB, Beveridge DL: Free energy analysis of proteinDNA binding: the EcoRI endonucleaseDNA complex. J Comput Phys 1999, 151: 333–357.View ArticleGoogle Scholar
 Gorfe AA, Jelesarov I: Energetics of sequencespecific proteinDNA association: computational analysis of integrase Tn916 binding to its target DNA. Biochemistry 2003, 42: 11568–11576.View ArticlePubMedGoogle Scholar
 Endres RG, Schulthess TC, Wingreen NS: Toward an atomistic model for predicting transcriptionfactor binding sites. Proteins 2004, 57: 262–268.View ArticlePubMedGoogle Scholar
 Oobatake M, Kono H, Wang Y, Sarai A: Anatomy of specific interactions between lambda repressor and operator DNA. Proteins 2003, 53: 33–43.View ArticlePubMedGoogle Scholar
 Oobatake M, Ooi T: Hydration and heat stability effects on protein unfolding. Prog Biophys Mol Biol 1993, 59(3):237–84.View ArticlePubMedGoogle Scholar
 Havranek JJ, Duarte CM, Baker D: A simple physical model for the prediction and design of proteinDNA interactions. J Mol Biol 2004, 344: 59–70.View ArticlePubMedGoogle Scholar
 Morozov A, Havranek J, Baker D, Siggia E: ProteinDNA binding specificity predictions with structural models. Nucleic Acids Res 2005, 33: 5781–5798.PubMed CentralView ArticlePubMedGoogle Scholar
 Beamer L, Pabo C: Refined 1.8 Åcrystal structure of the lambda repressor operator complex. J Mol Biol 1992, 227: 177–196.View ArticlePubMedGoogle Scholar
 Johnson AD, Poteete AR, Lauer G, Sauer RT, Ackers GK, Ptashne M: λrepressor and cro components of an efficient molecular switch. Nature 1981, 294: 217–223.View ArticlePubMedGoogle Scholar
 Brennan R, Matthews B: The helixturnhelix DNA binding motif. J Biol Chem 1989, 264: 1903–1906.PubMedGoogle Scholar
 Sarai A, Takeda Y: λrepressor recognizes the approximately 2fold symmetric halfoperator sequences asymmetrically. Proc Natl Acad Sci USA 1989, 86: 6513–6517.PubMed CentralView ArticlePubMedGoogle Scholar
 Ptashne M: A Genetic Switch: Phage Lambda and Higher Organisms. 2nd edition. Cambridge, MA: Cell Press and Blackwell Scientific Publications; 1992.Google Scholar
 Ackers G, Johnson A, Shea M: Quantitative model for gene regulation by λ phage repressor. Proc Natl Acad Sci USA 1982, 79: 1129–1133.PubMed CentralView ArticlePubMedGoogle Scholar
 Shea MA, Ackers GK: The O_{R}control system of bacteriophage lambda. A physicalchemical model for gene regulation. J Mol Biol 1985, 181: 211–230.View ArticlePubMedGoogle Scholar
 Aurell E, Brown S, Johanson J, Sneppen K: Stability Puzzles in Phage Lambda. Phys Rev E Stat Nonlin Soft Matter Phys 2002, 65(5 Pt 1):051914.View ArticlePubMedGoogle Scholar
 Bakk A, Melzer R: In vivo nonspecific binding of lambda CI and Cro repressors is significant. FEBS letters 2004, 563: 66–68.View ArticlePubMedGoogle Scholar
 Structural Classification of Proteins[http://scop.mrclmb.cam.ac.uk/scop/]
 Takeda Y, Sarai A, Rivera VM: Analysis of sequencespecific interaction between Cro repressor and operator DNA by systematic base substitution experiments. Proc Natl Acad Sci USA 1989, 86: 439–443.PubMed CentralView ArticlePubMedGoogle Scholar
 Benos PV, Bulyk ML, Stormo GD: Additivity in proteinDNA interactions: how good an approximation is it? Nucleic Acids Res 2002, 30(20):4442–51.PubMed CentralView ArticlePubMedGoogle Scholar
 Benos PV, Lapedes AS, Stormo GD: Is there a code for proteinDNA recognition? Probab(ilistical)ly... Bioessays 2002, 24: 466–475.View ArticlePubMedGoogle Scholar
 Maerkl SJ, Quake SR: A systems approach to measuring the binding energy landscapes of transcription factors. Science 2007, 315: 233–237.View ArticlePubMedGoogle Scholar
 Thomasson KA, Ouporov IV, Baumgartner T, Czaplinski J, Kaldor T, Northrup SH: Free energy of nonspecific binding of Cro repressor protein to DNA. Phys Chem 1997, 101: 9127–9136.View ArticleGoogle Scholar
 Vriend G: WHAT IF: A molecular modeling and drug design program. J Mol Graph 1990, 8: 52–54.View ArticlePubMedGoogle Scholar
 Crooks GE, Hon G, Chandonia JM, Brenner SE: WebLogo: A sequence logo generator. Genome Res 2004, 14: 1188–1190.PubMed CentralView ArticlePubMedGoogle Scholar
 van Dijk M, van Dijk ADJV, Hsu V, Boelens R, Bonvin AMJJ: Informationdriven proteinDNA docking using HADDOCK:it is a matter of flexibility. Nucleic Acids Res 2006, 34: 3317–3325.PubMed CentralView ArticlePubMedGoogle Scholar
 Ashworth J, Havranek JJ, Duarte CM, Sussman D, Monnat RJ, Stoddard BL, Baker D: Computational redesign of endonuclease DNA binding and cleavage specificity. Nature 2006, 441: 656–659.PubMed CentralView ArticlePubMedGoogle Scholar
 Berman H, Westbrook J, Feng Z, Gilliand G, Bhat T, Weissig H, Shindyalov I, Bourne P: The Protein Data Bank. Nucleic Acids Res 2000, 28: 235–242.PubMed CentralView ArticlePubMedGoogle Scholar
 Dr. Andrew C.R. Martin's Group[http://www.bioinf.org.uk/software/]
 Berendsen H, der Spoel D, van Drunen R: GROMACS: a messagepassing parallel MD implementation. Comp Phys Comm 1995, 91: 43–56.View ArticleGoogle Scholar
 Lindahl E, Hess B, van der Spoel D: GROMACS 3.0: A package for molecular simulation and trajectory analysis. J Mol Mod 2001, 7: 306–317.Google Scholar
 Madura J, Davis M, Gilson M, Wade R, Luty B, McCammon J: Biological applications of electrostatics calculations and Brownian dynamics simulations. Rev Comp Chem 1994, 5: 229–267.Google Scholar
 Madura J, Briggs J, Wade R, Davis M, Luty B, Ilin A, Antosiewicz J, Gilson M, Bagheri B, Scott SLR, McCammon J: Electrostatics and diffusion of molecules in solution: simulations with the University of Houston Brownian Dynamics program. Comp Phys Comm 1995, 91: 57–95.View ArticleGoogle Scholar
 Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML: Comparison of simple potential functions for simulating liquid water. J Chem Phys 1983, 79: 926–935.View ArticleGoogle Scholar
 Humphrey W, Dalke A, Schulten K: VMD Visual Molecular Dynamics. J Mol Graph 1996, 14: 33–38.View ArticlePubMedGoogle Scholar
 Andersen HC: Rattle: a velocity version of the shake algorithm for molecular dynamics calculations. J Comp Phys 1983, 52: 24–34.View ArticleGoogle Scholar
 Kale L, Skeel R, Bhandarkar M, Brunner R, Gursoy A, Krawetz N, Phillips J, Shinozaki A, Varadarajan K, Schulten K: NAMD2: greater scalability for parallel molecular dynamics. J Comp Phys 1999, 151: 283–312.View ArticleGoogle Scholar
 Berendsen HJC, Postma JPM, van Gunsteren WF, Di Nola A, Haak JR: Molecular dynamics with coupling to an external bath. J Chem Phys 1984, 81: 3684–3690.View ArticleGoogle Scholar
 Darden T, York D, Pedersen L: Particle Mesh Ewald. An N.log(N) method for Ewald sums in large systems. J Chem Phys 1993, 98: 10089–10092.View ArticleGoogle Scholar
 Gilson M, Given JA, Bush BL, McCammon J: The statisticalthermodynamic basis for computation of binding affinities: a critical review. Biophys J 1997, 72: 1047–1069.PubMed CentralView ArticlePubMedGoogle Scholar
 Brooks B, Bruccoleri R, Olafson B, States D, Swaminathan S, Karplus M: CHARMM: a program for macromolecular energy minimization and dynamics calculations. Comput Chem 1983, 4: 187–217.View ArticleGoogle Scholar
 MacKerell A, Bashford D, Bellott M, Dunbrack R, Evanseck J, Field M, Fischer S, Gao J, Guo H, Ha S, JosephMcCarthy D, Kuchnir L, Kuczera K, Lau F, Mattos C, Michnick S, Ngo T, Nguyen D, Prodhom B, Reiher W, Roux B, Schlenkrich M, Smith J, Stote R, Straub J, Watanabe M, WiorkiewiczKuczera J, Yin D, Karplus M: Allatom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 1998, 102: 3586–3616.View ArticlePubMedGoogle Scholar
 Qiu D, Shenkin P, Hollinger F, Still W: The GB/SA Continuum Model for Solvation. A Fast Analytical Method for the Calculation of Approximate Born Radii. J Phys Chem 1997, 101: 3005–3014.View ArticleGoogle Scholar
 Fogolari F, Brigo A, Molinari H: Protocol for MM/PBSA molecular dynamics simulations of proteins. Biophys J 2003, 85: 159–166.PubMed CentralView ArticlePubMedGoogle Scholar
 Press WH, Teukolsky SA, Vetterling WT, Flannery BP: Numerical Recipes in C. 2nd edition. Cambridge University Press; 1995.Google Scholar
 Lu XJ, Olson WK: 3DNA: a software package for the analysis, rebuilding and visualization of threedimensional nucleic acid structures. Nucleic Acids Res 2003, 31: 5108–5121.PubMed CentralView ArticlePubMedGoogle Scholar
 Berenson ML, Levine DM, Goldstein M: Intermediate statistical methods and applications. NJ, PrenticeHall, Inc., Englewood Cliffs; 1983.Google Scholar
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