Synthesis,Kinetics, and Molecular Docking of Novel 9‐(2‐Hydroxypropyl)purine Nucleoside Analogs as Ligands of Herpesviral Thymidine Kinases

In the context of broadening the knowledge on substrate specificity of Herpes simplex virus type 1 thymidine kinase (HSV‐1 TK) and Varicella‐Zoster virus thymidine kinase (VZV TK), new derivatives of 9‐(2‐hydroxypropyl)‐substituted adenine, chloropurine, hypoxanthine, guanine, thiopurine, and (methylsulfanyl)purine were synthesized and subjected to in vitro phosphorylation and binding affinity assays. The interactions between the compounds and the crystallographically determined active site residues of HSV‐1 TK have been studied by molecular modeling with the Lamarckian genetic algorithm of docking program AutoDock 3.0. All compounds mentioned bind to both enzymes in the low mM to sub‐mM range, comparable to binding affinities of existing prodrugs. Findings from the docking procedure indicate multiple binding modes for all of the compounds and are in accordance with the results of phosphorylation and binding‐affinity studies. Furthermore, the studies reveal that hypoxanthine derivatives represent a new class of TK substrates and thiopurine derivatives a new class of TK inhibitors.     

Prediction of the binding mode of N2-phenylguanine derivative inhibitors to herpes simplex virus type 1 thymidine kinase

The probable binding mode of the herpes simplex virus thymidine kinase (HSV1 TK) N²-[substituted]-phenylguanine inhibitors is proposed. A computational experiment was designed to check some qualitative binding parameters and to calculate the interaction binding energies of alternative binding modes of N²-phenylguanines. The known binding modes of the HSV1 TK natural substrate deoxythymidine and one of its competitive inhibitors ganciclovir were used as templates. Both the qualitative and quantitative parts of the computational experiment indicated that the N²-phenylguanine derivatives bind to the HSV1 TK active site in the deoxythymidine-like binding mode. An experimental observation that N²-phenylguanosine derivatives are not phosphorylated during the interaction with the HSV1 TK gives support to the proposed binding mode.     

A structure-guided approach for protein pocket modeling and affinity prediction

Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction.     

Top leads for swine influenza A/H1N1 virus revealed by steered molecular dynamics approach

Since March 2009, the rapid spread of infection during the recent A/H1N1 swine flu pandemic has raised concerns of a far more dangerous outcome should this virus become resistant to current drug therapies. Currently oseltamivir (tamiflu) is intensively used for the treatment of influenza and is reported effective for 2009 A/H1N1 virus. However, as this virus is evolving fast, some drug-resistant strains are emerging. Therefore, it is critical to seek alternative treatments and identify roots of the drug resistance. In this paper, we use the steered molecular dynamics (SMD) approach to estimate the binding affinity of ligands to the glycoprotein neuraminidase. Our idea is based on the hypothesis that the larger is the force needed to unbind a ligand from a receptor the higher its binding affinity. Using all-atom models with Gromos force field 43a1 and explicit water, we have studied the binding ability of 32 ligands to glycoprotein neuraminidase from swine flu virus A/H1N1. The electrostatic interaction is shown to play a more important role in binding affinity than the van der Waals one. We have found that four ligands 141562, 5069, 46080, and 117079 from the NSC set are the most promising candidates to cope with this virus, while peramivir, oseltamivir, and zanamivir are ranked 8, 11, and 20. The observation that these four ligands are better than existing commercial drugs has been also confirmed by our results on the binding free energies obtained by the molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) method. Our prediction may be useful for the therapeutic application.     

禽流感病毒HA蛋白与人受体的分子对接

血凝素(hemagglutinin,HA)是位于禽流感病毒表面的糖蛋白。在病毒感染过程中,HA与禽类宿主细胞表面受体结合,介导病毒膜与宿主核内体膜的融合,在传染过程中发挥关键作用。自然界中的禽流感病毒处于不断演化之中,其HA的禽受体结合位点常常发生氨基酸变异。因此,当HA变异体与人受体结合能力较强时,禽流感病毒往往会发生跨种传播而感染人。为预防禽流感的跨种传播,人们迫切需要发展大规模快速检测或预测HA变异体与人受体结合亲和力的方法,以评估各种新发禽流感病毒的跨种传播能力,提前筛选出有潜在危险的病毒株。针对此问题,本研究以H7N9亚型的HA蛋白H7为研究对象,发展了一种运用分子对接的计算方法,预测HA变异体与人受体的结合亲和力。该方法的计算结果表明,H7与人受体的结合亲和力普遍弱于有较强传染人能力的H1,说明H7N9亚型病毒的跨种传播能力普遍较弱;但是,计算分析也揭示,部分新发的H7N9毒株的HA有强的人受体结合亲和力,提示在自然演化过程中,H7N9病毒有可能演化出具有较强的感染人能力的新毒株,这与2013年禽流感疫情的实际发生情况相一致。因此,本文所发展的计算方法可用于快速预测新发禽流感病毒HA与人受体的结合亲和力,为新发禽流感病毒的跨种传播风险评估提供理论依据。     

Linear and nonlinear 3D-QSAR approaches in tandem with ligand-based homology modeling as a computational strategy to depict the pyrazolo-triazolo-pyrimidine antagonists binding site of the human adenosine A2A receptor

The integration of ligand- and structure-based strategies might sensitively increase the success of drug discovery process. We have recently described the application of Molecular Electrostatic Potential autocorrelated vectors (autoMEPs) in generating both linear (Partial Least-Square, PLS) and nonlinear (Response Surface Analysis, RSA) 3D-QSAR models to quantitatively predict the binding affinity of human adenosine A3 receptor antagonists. Moreover, we have also reported a novel GPCR modeling approach, called Ligand-Based Homology Modeling (LBHM), as a tool to simulate the conformational changes of the receptor induced by ligand binding. In the present study, the application of both linear and nonlinear 3D-QSAR methods and LBHM computational techniques has been used to depict the hypothetical antagonist binding site of the human adenosine A2A receptor. In particular, a collection of 127 known human A2A antagonists has been utilized to derive two 3D-QSAR models (autoMEPs/PLS&RSA). In parallel, using a rhodopsin-driven homology modeling approach, we have built a model of the human adenosine A2A receptor. Finally, 3D-QSAR and LBHM strategies have been utilized to predict the binding affinity of five new human A2A pyrazolo-triazolo-pyrimidine antagonists finding a good agreement between the theoretical and the experimental predictions.     

Computer-aided active-site-directed modeling of the Herpes Simplex Virus 1 and human thymidine kinase

Summary Thymidine kinase (TK), which is induced by Herpes Simplex Virus 1 (HSV1), plays a key role in the antiviral activity of guanine derivatives such as aciclovir (ACV). In contrast, ACV shows only low affinity to the corresponding host cell enzyme. In order to define the differences in substrate binding of the two enzymes on molecular level, models for the three-dimensional (3-D) structures of the active sites of HSV1-TK and human TK were developed. The reconstruction of the active sites started from primary and secondary structure analysis of various kinases. The results were validated to homologous enzymes with known 3-D structures. The models predict that both enzymes consist of a central core -sheet structure, connected by loops and -helices very similar to the overall structure of other nucleotide binding enzymes. The phosphate binding is made up of a highly conserved glycine-rich loop at the N-terminus of the proteins and a conserved region at the C-terminus. The thymidine recognition site was found about 100 amino acids downstream from the phosphate binding loop. The differing substrate specificity of human and HSV1-TK can be explained by amino-acid substitutions in the homologous regions.To achieve a better understanding of the structure of the active site and how the thymidine kinase proteins interact with their substrates, the corresponding complexes of thymidine and dihydroxypropoxyguanine (DHPG) with HSV1 and human TK were built. For the docking of the guanine derivative, the X-ray structure of Elongation Factor Tu (EF-Tu), co-crystallized with guanosine diphosphate, was taken as reference. Fitting of thymidine into the active sites was done with respect to similar interactions found in thymidylate kinase. To complement the analysis of the 3-D structures of the two kinases and the substrate enzyme interactions, site-directed mutagenesis of the thymidine recognition site of HSV1-TK has been undertaken, changing Asp¹⁶² in the thymidine recognition site into Asn. First investigations reveal that the enzymatic activity of the mutant protein is destroyed.     

SAMPL4 & DOCK3.7: lessons for automated docking procedures

The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.     

Variable selection and specification of robust QSAR models from multicollinear data: arylpiperazinyl derivatives with affinity and selectivity for α<Subscript>2</Subscript>-adrenoceptors

Two QSAR models have been identified that predict the affinity and selectivity of arylpiperazinyl derivatives for alpha1 and alpha2 adrenoceptors (ARs). The models have been specified and validated using 108 compounds whose structures and inhibition constants (Ki) are available in the literature [Barbaro et al., J. Med. Chem., 44 (2001) 2118; Betti et al., J. Med. Chem., 45 (2002) 3603; Barbaro et al., Bioorg. Med. Chem., 10 (2002) 361; Betti et al., J. Med. Chem., 46 (2003) 3555]. One hundred and forty-seven predictors have been calculated using the Cerius 2 software available from Accelrys. This set of variables exhibited redundancy and severe multicollinearity, which had to be identified and removed as appropriate in order to obtain robust regression models free of inflated errors for the beta estimates - so-called bouncing betas. Those predictors that contained information relevant to the alpha2 response were identified on the basis of their pairwise linear correlations with affinity (-log Ki) for alpha2 adrenoceptors; the remaining variables were discarded. Subsequent variable selection made use of Factor Analysis (FA) and Unsupervised Variable Selection (UzFS). The data was divided into test and training sets using cluster analysis. These two sets were characterised by similar and consistent distributions of compounds in a high dimensional, but relevant predictor space. Multiple regression was then used to determine a subset of predictors from which to determine QSAR models for affinity to alpha2-ARs. Two multivariate procedures, Continuum Regression (the Portsmouth formulation) and Canonical Correlation Analysis (CCA), have been used to specify models for affinity and selectivity, respectively. Reasonable predictions were obtained using these in silico screening tools.     

Monte Carlo and modified Tanford-Kirkwood results for macromolecular electrostatics calculations

The understanding of electrostatic interactions is an essential aspect of the complex correlation between structure and function of biological macromolecules. It is also important in protein engineering and design. Theoretical studies of such interactions are predominantly done within the framework of Debye-Hückel theory. A classical example is the Tanford-Kirkwood (TK) model. Besides other limitations, this model assumes an infinitesimally small macromolecule concentration. By comparison to Monte Carlo (MC) simulations, it is shown that TK predictions for the shifts in ion binding constants upon addition of salt become less reliable even at moderately macromolecular concentrations. A simple modification based on colloidal literature is suggested to the TK scheme. The modified TK models suggested here satisfactorily predict MC and experimental shifts in the calcium binding constant as a function of protein concentration for the calbindin D(9k) mutant and calmodulin.     

Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome

Reliable in silico prediction methods promise many advantages over experimental high-throughput screening (HTS): vastly lower time and cost, affinity magnitude estimates, no requirement for a physical sample, and a knowledge-driven exploration of chemical space. For the specific case of kinases, given several hundred experimental IC(50) training measurements, the empirically parametrized profile-quantitative structure-activity relationship (profile-QSAR) and surrogate AutoShim methods developed at Novartis can predict IC(50) with a reliability approaching experimental HTS. However, in the absence of training data, prediction is much harder. The most common a priori prediction method is docking, which suffers from many limitations: It requires a protein structure, is slow, and cannot predict affinity. (1) Highly accurate profile-QSAR (2) models have now been built for roughly 100 kinases covering most of the kinome. Analyzing correlations among neighboring kinases shows that near neighbors share a high degree of SAR similarity. The novel chemogenomic kinase-kernel method reported here predicts activity for new kinases as a weighted average of predicted activities from profile-QSAR models for nearby neighbor kinases. Three different factors for weighting the neighbors were evaluated: binding site sequence identity to the kinase neighbors, similarity of the training set for each neighbor model to the compound being predicted, and accuracy of each neighbor model. Binding site sequence identity was by far most important, followed by chemical similarity. Model quality had almost no relevance. The median R(2) = 0.55 for kinase-kernel interpolations on 25% of the data of each set held out from method optimization for 51 kinase assays, approached the accuracy of median R(2) = 0.61 for the trained profile-QSAR predictions on the same held out 25% data of each set, far faster and far more accurate than docking. Validation on the full data sets from 18 additional kinase assays not part of method optimization studies also showed strong performance with median R(2) = 0.48. Genetic algorithm optimization of the binding site residues used to compute binding site sequence identity identified 16 privileged residues from a larger set of 46. These 16 are consistent with the kinase selectivity literature and structural biology, further supporting the scientific validity of the approach. A priori kinase-kernel predictions for 4 million compounds were interpolated from 51 existing profile-QSAR models for the remaining >400 novel kinases, totaling 2 billion activity predictions covering the entire kinome. The method has been successfully applied in two therapeutic projects to generate predictions and select compounds for activity testing.     

取代脱氧脲苷的量子化学计算及分子对接

5取代6氮杂2’脱氧脲苷是一类疱疹单纯一型病毒胸苷激酶(ＨＳＶ1ＴＫ)抑制剂.研究此类化合物的合成、结构、杀菌活性及构效关系一直为科学界所关注[1-3].ＨＳＶ1ＴＫ可以催化胸苷的磷酸化反应,使生成胸苷磷酸脂,在ＤＮＡ合成的控制中起着重要的作用.其晶体结构的发表[4],使得我们能从分子水平上研究此类酶抑制剂与酶活性中心的作用机制,模拟其作用方式,从而实现ＨＳＶ1ＴＫ抑制剂的全新设计.本文从化合物(Ｉ)和(ＩＩ)(见图1)晶体结构出发,使用美国Ｔｒｉｐｏｓ公司开发的三维分子模拟软件包ＳＹＢＹＬ63[5],采用ＭＯＰＡＣ界面中ＰＭ3[6]程…     

Calculation of absolute ligand binding free energy to a ribosome-targeting protein as a function of solvent model

A comparative analysis is provided of the effect of different solvent models on the calculation of a potential of mean force (PMF) for determining the absolute binding affinity of the small molecule inhibitor pteroic acid bound to ricin toxin A-chain (RTA). Solvent models include the distance-dependent dielectric constant, several different generalized Born (GB) approximations, and a hybrid explicit/GB-based implicit solvent model. We found that the simpler approximation of dielectric screening and a GB model, with Born radii fitted to a switching-window dielectric-boundary surface Poisson solvent model, severely overpredicted the binding affinity as compared to the experimental value, estimated to range from -4.4 to -6.0 kcal/mol. In contrast, GB models that are parametrized to fit the Lee-Richards molecular surface performed much better, predicting binding free energy within 1-3 kcal/mol of experimental estimates. However, the predicted free-energy profiles of these GB models displayed alternative binding modes not observed in the crystal structure. Finally, the most rigorous and computationally costly approach in this work, which used a hybrid explicit/implicit solvent model, correctly determined a binding funnel in the PMF near the crystallographic bound state and predicted an absolute binding affinity that was 2 kcal/mol more favorable than the estimated experimental binding affinity.     

Studies of the mechanism of selectivity of protein tyrosine phosphatase 1B (PTP1B) bidentate inhibitors using molecular dynamics simulations and free energy calculations

Bidentate inhibitors of protein tyrosine phosphatase 1B (PTP1B) are considered as a group of ideal inhibitors with high binding potential and high selectivity in treating type II diabetes. In this paper, the binding models of five bidentate inhibitors to PTP1B, TCPTP, and SHP-2 were investigated and compared by using molecular dynamics (MD) simulations and free energy calculations. The binding free energies were computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodology. The calculation results show that the predicted free energies of the complexes are well consistent with the experimental data. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition analysis indicates that the residues ARG24, ARG254, and GLN262 in the second binding site of PTP1B are essential for the high selectivity of inhibitors. Furthermore, the residue PHE182 close to the active site is also important for the selectivity and the binding affinity of the inhibitors. According to our analysis, it can be concluded that in most cases the polarity of the portion of the inhibitor that binds to the second binding site of the protein is positive to the affinity of the inhibitors while negative to the selectivity of the inhibitors. We expect that the information we obtained here can help to develop potential PTP1B inhibitors with more promising specificity.     

Site-selective metal binding by designed alpha-helical peptides

It is known that the designed alpha-helical peptide family TRI [(Ac-G(LKALEEK)4G-CONH2)], containing single site substitution of a cysteine for a leucine, is capable of binding Cd(II) within a three-stranded coiled coil. The binding affinity of cadmium is dependent upon the site of substitution, with cysteine incorporated at the a site leading to cadmium complexes of higher affinity than when a d site was modified. In this work we have examined whether this differential binding affinity can be expressed in a di-cysteine-substituted peptide in order to develop site specificity within a designed system. The peptide TRI L9CL19C was used to determine whether significant differences in binding affinities at nearly proximal sites could be achieved in a short designed peptide. On the basis of 113Cd, 1H NMR, and circular dichroic spectroscopies, we have shown that 1 equiv of Cd(II) binds exclusively at the a site. Only after that position is filled does the second site become populated. Thus, the TRI system represents the first example where stoichiometrically equivalent peptides with different sequences form the framework for designing molecular assemblies that show site-specific ion recognition. We propose that the distinct metal affinities are due to the cysteine conformers at different substitution points along the peptide. Furthermore, we have shown that site selectivity in biomolecules can be encoded into relatively short peptides with helical sequences and, therefore, do not necessarily require the extensive protein scaffolds found in natural systems.     

The potent anti-HIV protein cyanovirin-N contains two novel carbohydrate binding sites that selectively bind to Man(8) D1D3 and Man(9) with nanomolar affinity: implications for binding to the HIV envelope protein gp120.

Cyanovirin-N (CVN) is a monomeric 11 kDa cyanobacterial protein that potently inactivates diverse strains of human immunodeficiency virus (HIV) at the level of cell fusion by virtue of high affinity interactions with the surface envelope glycoprotein gp120. Several lines of evidence have suggested that CVN-gp120 interactions are in part mediated by N-linked complex carbohydrates present on gp120, but experimental evidence has been lacking. To this end we screened a comprehensive panel of carbohydrates which represent structurally the N-linked carbohydrates found on gp120 for their ability to inhibit the fusion-blocking activity of CVN in a quantitative HIV-1 envelope-mediated cell fusion assay. Our results show that CVN specifically recognizes with nanomolar affinity Man(9)GlcNAc(2) and the D1D3 isomer of Man(8)GlcNAc(2). Nonlinear least squares best fitting of titration data generated using the cell fusion assay show that CVN binds to gp120 with an equilibrium association constant (K(a)) of 2.4 (+/- 0.1) x 10(7) M(-1) and an apparent stoichiometry of 2 equiv of CVN per gp120, Man(8)GlcNAc(2) D1D3 acts as a divalent ligand (2 CVN:1 Man(8)) with a K(a) of 5.4 (+/- 0.5) x 10(7) M(-1), and Man(9)GlcNAc(2) functions as a trivalent ligand (3 CVN:1 Man(9)) with a K(a) of 1.3 (+/- 0.3) x 10(8) M(-1). Isothermal titration calorimetry experiments of CVN binding to Man(9)GlcNAc(2) at micromolar concentrations confirmed the nanomolar affinity (K(a) = 1.5 (+/- 0.9) x 10(8) M(-1)), and the fitted data indicated a stoichiometry equal to approximately one (1 Man(9):1 CVN). The 1:1 stoichiometry at micromolar concentrations suggested that CVN has not only a high affinity binding site-relevant to the studies at nM concentrations-but a lower affinity site as well that facilitates cross-linking of CVN-oligomannose at micromolar concentrations or higher. The specificity of CVN for Man(8) D1D3 and Man(9) over the D1D2 isomer of Man(8) indicated that the minimum structure required for high affinity binding comprises Manalpha1 --> 2Manalpha. By following the (1)H-(15)N correlation spectrum of (15)N-labeled CVN upon titration with this disaccharide, we unambiguously demonstrate that CVN recognizes and binds to the disaccharide Manalpha1 --> 2Manalpha via two distinct binding sites of differing affinities located on opposite ends of the protein. The high affinity site has a K(a) of 7.2 (+/- 4) x 10(6) M(-1) and the low affinity site a K(a) of 6.8 (+/- 4) x 10(5) M(-1) as determined by isothermal titration calorimetry. Mapped surfaces of the carbohydrate binding sites are presented, and implications for binding to gp120 are discussed.     

Synthetic creatinine receptor: imprinting of a Lewis acidic zinc(II)cyclen binding site to shape its molecular recognition selectivity

Molecularly imprinted polymers (MIPs) from polymerizable Lewis acidic zinc(II)cyclen complexes and ethylene glycol dimethyl acrylate have been prepared. For the imprinting process the template molecule creatinine is reversibly coordinated to the zinc atom. The high strength of this interaction allows analyte binding to the MIP from aqueous solution with high affinity. Its pH dependence is used for controlled guest release with nearly quantitative analyte recovery rate. The binding capacity and selectivity profile of the MIP remains constant through several pH controlled binding and release cycles. MIPs missing a suitable metal binding site showed no significant affinity for thymine or creatinine. Flavin adsorbs nonspecifically to all polymers. The imprinting process reverses the binding selectivity of zinc(II)cyclen for creatinine and thymine from 1:34 in homogeneous solution to 3.5:1 in the MIP. Scatchard plot analysis of creatinine binding isotherms reveals uniform binding of the imprint, with fits indicating a one-site model; however, similar analysis for thymine indicate high and low affinity sites. This corresponds to unrestricted coordination sites freely accessible for thymine, e.g., at the polymer surface, and misshaped imprinted sites, which still can accommodate thymine. More than 50% of all binding sites exclusively bind creatinine and are not accessible to thymine. The binding properties of a copolymer of polymerizable zinc(II)cyclen and ethylene glycol dimethyl acrylate missing the creatinine template, which match the binding selectivity of the complex in solution, confirm that the origin of altered selectivities is the imprinting process. With binding ability at physiological pH, the MIPs are applicable for tasks in medicinal diagnostics or biotechnology. Imprinted zinc(II)cyclen complexes provide, like a metalloenzyme binding motif, high binding affinity by reversible coordination while the surrounding macromolecule determines binding selectivity.     

Effects of DNA mismatches on binding affinity and kinetics of polymerase-DNA complexes as revealed by surface plasmon resonance biosensor

In this study, surface plasmon resonance (SPR) biosensor techniques were used to obtain quantitative information on the kinetics of the DNA and polymerase I (Klenow fragment) interaction. DNA duplexes containing different base compositions at the binding site were immobilized on the SPR sensor surface via biotin-streptavidin chemistry and the subsequent binding of the polymerase was measured in real time. Various kinetic models were tested and a translocation model was shown to provide the best fit for the binding and dissociation profiles. The results revealed that the enzyme binds to DNA at both the polymerase and the exonuclease domains with different association and dissociation rates as well as affinity constants, depending on the presence of mismatches near the primer 3'-end. Introduction of unpaired bases increases the DNA binding affinity towards the exonuclease domain and promotes the translocation of DNA from the polymerase site to the exonuclease site. The results also demonstrated that SPR biosensors may be used as a sensitive technique for studying molecular recognition events such as single-base discrimination involved in protein-DNA interaction.