- Open Access
iGEMDOCK: a graphical environment of enhancing GEMDOCK using pharmacological interactions and post-screening analysis
© Hsu et al; licensee BioMed Central Ltd. 2011
Published: 15 February 2011
Pharmacological interactions are useful for understanding ligand binding mechanisms of a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). An integrated VS environment, which provides the friendly interface to seamlessly combine these VS stages and to identify the pharmacological interactions directly from screening compounds, is valuable for drug discovery.
We developed an easy-to-use graphic environment, i GEMDOCK, integrating VS stages (from preparations to post-screening analysis). For post-screening analysis, i GEMDOCK provides biological insights by deriving the pharmacological interactions from screening compounds without relying on the experimental data of active compounds. The pharmacological interactions represent conserved interacting residues, which often form binding pockets with specific physico-chemical properties, to play the essential functions of a target protein. Our experimental results show that the pharmacological interactions derived by i GEMDOCK are often hot spots involving in the biological functions. In addition, i GEMDOCK provides the visualizations of the protein-compound interaction profiles and the hierarchical clustering dendrogram of the compounds for post-screening analysis.
We have developed i GEMDOCK to facilitate steps from preparations of target proteins and ligand libraries toward post-screening analysis. i GEMDOCK is especially useful for post-screening analysis and inferring pharmacological interactions from screening compounds. We believe that i GEMDOCK is useful for understanding the ligand binding mechanisms and discovering lead compounds. i GEMDOCK is available at http://gemdock.life.nctu.edu.tw/dock/igemdock.php.
Structure-based drug design is widely used to identify lead compounds with the growing availability of protein structures [1–3]. Many tools (e.g., GEMDOCK , DOCK , AutoDock , and GOLD  ) have been developed for virtual screening (VS) and successfully identified lead compounds for some target proteins. However, the accuracy of these docking tools remained intensive because of the incomplete understandings of ligand binding mechanisms [1–3]. In addition, most of scoring functions are lack of pharmacological interactions that are essential for ligand binding or biological functions . Recently, some approaches have been proposed to derive pharmacological interactions from known compounds [8–10]. These approaches apparently increase hit rates for identifying the active compounds which are often similar to the known compounds. In addition, these approaches are often unable to be applied for new targets, which have no known active compounds.
Generally, a VS procedure consists of four main steps: preparations of the target protein and the compound library, docking and post-screening analysis (e.g., clustering compounds and pharmacological interactions). Most docking programs (e.g. DOCK  and AutoDock ) only provide docked poses or loosely coupled these steps. They often provided limit ability for post-screening analysis. Therefore, a VS framework, providing an easy-to-use graphic and integrated environment, is an emergent task for drug discovery.
To address these issues, we have developed a structure-based VS framework, named i GEMDOCK, from preparations through to post-screening analysis. i GEMDOCK is an integrated environment, which integrates the heavily modified and enhanced in-house tool GEMDOCK, protein-ligand profiles, pharmacological interactions, and compound clusters. GEMDOCK was comparative to several docking tools (e.g. DOCK  and GOLD ) and has been successfully applied to identify new inhibitors and new binding sites for some targets [4, 8, 11–14]. Notably, i GEMDOCK derives the pharmacological interactions from screening compounds without using a set of known active compounds. The pharmacological interactions, which often form binding pockets with specific physico-chemical properties of the target protein, can represent conserved interactions between the interacting residues and the screening compounds. We initially validated the pharmacological interactions on three therapeutic protein targets, including estrogen receptor α for antagonists and agonists and thymidine kinase. Our experimental results show that the derived pharmacological interactions are often essential for the ligand binding or maintaining biological functions for these targets. In addition, i GEMDOCK provided a post-screening analysis module, which is convenient for clustering compounds and visualizing the pharmacological interactions by interaction profiles. We believe that i GEMDOCK is useful for drug discovery and identifying essential residues and interactions for understanding the binding mechanisms.
Preparations of proteins and compound sets
To initially validate the pharmacological interactions, we selected three therapeutic protein targets, including estrogen receptor α for agonists (ERA, PDB code 1gwr ), estrogen receptor α for antagonists (ER, PDB code 3ert ), and thymidine kinase (TK, PDB code 1kim ) because these proteins were well studied. The catalytic mechanisms, biological functions, key functional residues, and active compounds of the three targets were available in the literatures. Estrogen receptor is an important therapeutic target for osteoporosis and breast cancer , and TK is a drug target for the therapy of herpes simplex virus type-1 . Moreover, we also evaluate the docking and screening accuracy of i GEMDOCK. For docking, a highly diverse dataset comprising 305 protein-compound complexes (i.e., CCDC/Astex set ) was selected; for screening, we prepared 10 known active compounds and 990 compounds were randomly selected from Available Chemical Directory (ACD) proposed by Bissantz et al. for each therapeutic protein target.
Mining pharmacological interactions
where p i , j is a binary value (0 or 1) for the compound i interacting to the residue group j. In the E and H profiles, the p i , j is set to 1 (green) if hydrogen-bonding or electrostatic interactions are yielded between the compound i and the residue j (energy ≤ -2.5 kcal/mol); otherwise, p i , j =0 (black). For the V profile, p i , j = 1 if the interacting energy is less than -4 kcal/mol (Fig. 2A).
where W j is the interaction conservation of the residue group j related to the largest z-score (z max ) among all of interacting groups in the binding site. Here, an interaction conservation is viewed as a pharmacological preference and an interaction is considered as the pharmacological interaction if W j ≥0.4. For example, for the hydrogen profile of the target ERA, the pharmacological preferences of E353 and R394 are 0.64 and 0.80, respectively; for the V profile, the preferences of L387, L391, and F404 are 1.00, 0.61, and 0.90, respectively (Fig. 2B). In this case, over 300 (>30%) screening compounds form hydrogen bonds with the residues E353 or R394 by polar moieties (e.g., hydroxyl group (27%), carboxyl group (20%), sulfuric acid monoester (9%), ketone (8%), and phosphoric acid monoester (6%)). Moreover, the aromatic rings of the screening compounds are often sandwiched by vdW interacting residues L387, L391, and F404 (Fig. 2D).
where e j is the energy obtained by the GEMDOCK scoring function for the residue group j. Finally, i GEMDOCK provides the ranks of energy-based and pharmacological scoring functions for all screening compounds.
Implementation of iGEMDOCK
i GEMDOCK is an easy-to-use VS environment and includes three main modules (Fig. 1): docking and virtual screening tool (GEMDOCK); post-screening analysis methods; and visualization tools (RasMol  and Java Treeview ). We employed ERA as an example to present these modules, procedures and features of i GEMDOCK.
For protein-ligand docking/screening module, i GEMDOCK provides an interactive interface for the preparations of the binding site and compound library; setting docking parameters; and monitoring progress status (Fig. 1B). For most docking tools, users usually need to prepare the binding site structure and compound library through complicated steps (e.g., add hydrogen atoms and generate the grids of the protein). Here, i GEMDOCK provides a straightforward method to derive the binding site from the bounded ligand. For example, the binding site of ERA (PDB code 1gwr) was obtained from the estradiol (Fig. 1C). i GEMDOCK is able to automatically consider the effects of hydrogen atoms when preparing the binding site and the compound library. In addition, i GEMDOCK allows users to visualize and refine the binding site of the target protein. Additionally, i GEMDOCK offers the similar way to prepare screening compounds and docking parameters (e.g., the population size and the number of generations).
After the screening process, i GEMDOCK utilizes the post-screening analysis module to infer pharmacological interactions and cluster screening compounds based on protein-ligand complexes and compound structures (Fig. 1D). First, i GEMDOCK generates interaction profiles and calculates the pharmacological preference (W j ) of each interacting group for deriving the pharmacological interactions (Fig. 2). These pharmacological preferences and interactions are shown in an interactive window (Fig. 2G); furthermore, RasMol displays the pharmacological interactions with conserved interacting residues and functional groups of compounds (Fig. 2H). Additionally, i GEMDOCK supports a hierarchical clustering method to cluster screening compounds according to interaction profiles and the atomic composition (Fig. 1E). The atomic composition, which is similar to the amino acid composition of a protein sequence, is useful for measuring compound similarity. i GEMDOCK provides an interactive interface for visualizing compound similarity with a hierarchical tree by Java Treeview. Finally, i GEMDOCK ranks and visualizes the screening compounds by combining the pharmacological interactions and the energy-based scoring function.
Results and discussion
Pharmacological interactions and consensus interaction ratio on estrogen receptor α and thymidine kinase
Predicted pharmacological interactions
Consensus interaction ratio a
Form hydrogen bonds with thymidine; activity was decreased over 90% if Q125 mutated 
Sandwich the thymine moiety of substrates 
Constitute a pocket for ligand binding 
We also examined the pharmacological interactions by their biological functions or binding mechanisms. For estrogen receptor α, H524 (hydrogen-bonding preferences are 1.0 and 0.42 for ERA and ER, respectively) is involved in a hydrogen-bonding network ; similarly, E353 and R394 (hydrogen-bonding preferences ≥ 0.5 for both ERA and ER) interact the structural water to form the hydrogen bonding network (Table 1 and Fig. 3) . These two hydrogen bonding networks are essential for estrogen receptor modulators to trigger the responses of estrogen receptor α[26, 27]. For ER and ERA, hydrophobic interacting residues, L346, L387, F404, and L525 with high vdW interaction preferences, contact with the sterols or flavones scaffolds of the active compounds. These residues contribute the major vdW interactions for the ligand binding of estrogen receptor α [28, 29].
For TK, R222 and R163 play major roles for inhibitor and substrate binding [30, 31], and their hydrogen-bonding preferences are 1.0 and 0.99, respectively (Table 1). Our method identified the electrostatic interactions of R222 and R163 (preferences are 1.0 and 0.4, respectively), which help to transfer phosphate in the substrate phosphorylation . However, these two electrostatic interactions are not observed from 10 active compounds (Fig. 3). For the residue Q125 (H preference 0.40), the TK activity was decreased over 90% if Q125 mutated into Asp, Glu, or Asn . The residues M128, Y172, H58, R163, and Y88 constitute a pocket to fix the substrate, and their vdW preferences are 0.58, 1.00, 0.68, 0.56, and 0.87, respectively (Table 1). For the substrate binding, M128 and Y172 sandwich the thymine moiety and W88 is a part of the quasi-helical motif [33, 34]. These results demonstrated that the pharmacological interactions derived by i GEMDOCK are often involved in the biological functions and the ligand binding.
Molecular docking and virtual screening
To initially evaluate the utility of i GEMDOCK for docking and virtual screening, we selected a highly diverse dataset with 305 protein-ligand complexes (i.e., CCDC/Astex set  ) and ERA, ER, and TK with 1000 compounds as test sets. Please note that the docking and screening tool of i GEMDOCK is GEMDOCK which was well-studied for VS and some applications [4, 8, 11–14]. In order to compare with previous works, we followed the docking procedures and performance indices proposed by Nissink, et al. A docked result was considered as a success solution if the root-mean-square derivation (RMSD) ≤2.0 Å between the docked solutions and X-ray crystal structures. For these 305 complexes, the success rates of i GEMDOCK and GOLD are 78% and 68%, respectively (Table S1 in additional file 1).
The pharmacological scoring function was then applied to identify the active compounds from the 1000 compounds of ERA, ER, and TK. Furthermore, we compared the screening results with those of using the energy-based scoring function of GEMDOCK. These two approaches were tested on the same datasets. The true hits of the three testing sets were used to access the screening accuracy of the two approaches (Fig. S1 in additional file 1). The screening accuracy was generally improved when i GEMDOCK considered the pharmacological interactions.
We compared i GEMDOCK (pharmacological scoring function) with three screening methods (DOCK, GOLD, and FlexX) on the ER and TK sets (Table S2 in additional file 1). To compare with previous works, we followed the experiment design and performance indices used by Bissantz et al. When true-positive rate is 80%, the false positive rates were 2.3% (i GEMDOCK), 13.3% (DOCK), 57.8% (FlexX), and 5.3% (GOLD), for ER. The false positive rates were 7.8% (i GEMDOCK), 23.4% (DOCK), 8.8% (FlexX), and 8.3% (GOLD) for TK.
To identify leads from vast amount of docked poses generated during the virtual screening procedure is the key step for the drug discovery. In addition, the top-ranked compounds based on the scoring functions are not advisable since these compounds may be similar in structures or physico-chemical properties. For these two issues, i GEMDOCK provides a post-screening analysis module to cluster compounds based on the interactions profiles and the atomic compositions. Selecting representative compounds from each cluster is able to maintain compound diversity and then reduces the false positives. Further, when active compounds are available, users can choose the similar compounds in the same cluster of the actives based on hierarchical trees (Fig. 1E). Notably, i GEMDOCK visualizes the interaction profiles of the compounds, and thereby the top-ranked compounds with pharmacological interactions can be selected according to the interaction table (Fig. 2G).
In summary, i GEMDOCK can mine the pharmacological interactions from the screening compounds without known active compounds. Therefore, i GEMDOCK can provide a good starting point for deriving pharmacological interactions (residues) and identifying new potential active compounds for a new protein structure. In addition, i GEMDOCK offers the visualization of the interaction profiles, pharmacological interactions, and the hierarchical clustering dendrogram. Users are able to easily observe and select compounds for post-screening analysis to enrich accuracies.
We have developed a structure-based VS framework i GEMDOCK from the preparations through to the post-screening analysis. i GEMDOCK is an integrated and easy-to-use environment which is especially useful for post-screening analysis and inferring pharmacological interactions from screening compounds. The friendly user interface is helpful to biologically oriented nonexperts. The experimental results show that the pharmacological interactions are often essential for the binding of the active compounds and involved in biological mechanisms. The pharmacological interactions can reduce the ill effects of energy-based scoring functions to enrich the hit rates in VS. We believe i GEMDOCK is useful for drug discovery and understanding protein-ligand mechanisms.
J-M. Yang was supported by National Science Council and partial support of the ATU plan by MOE. Authors are grateful to both the hardware and the software supports of the Structural Bioinformatics Core Facility at National Chiao Tung University.
This article has been published as part of BMC Bioinformatics Volume 12 Supplement 1, 2011: Selected articles from the Ninth Asia Pacific Bioinformatics Conference (APBC 2011). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/12?issue=S1.
- Lyne PD: Structure-based virtual screening: an overview. Drug Discovery Today 2002, 7: 1047–1055. 10.1016/S1359-6446(02)02483-2View ArticlePubMedGoogle Scholar
- Tanrikulu Y, Schneider G: Pseudoreceptor models in drug design: bridging ligand- and receptor-based virtual screening. Nature Reviews Drug Discovery 2008, 7: 667–677. 10.1038/nrd2615View ArticlePubMedGoogle Scholar
- Kitchen DB, Decornez H, Furr JR, Bajorath J: Docking and scoring in virtual screening for drug discovery: methods and applications. Nature Reviews Drug Discovery 2004, 3: 935–949. 10.1038/nrd1549View ArticlePubMedGoogle Scholar
- Yang J-M, Chen C-C: GEMDOCK: a generic evolutionary method for molecular docking. Proteins 2004, 55: 288–304. 10.1002/prot.20035View ArticlePubMedGoogle Scholar
- Kramer B, Rarey M, Lengauer T: Evaluation of the flexX incremental construction algorithm for protein-ligand docking. Proteins 1999, 37: 228–241. 10.1002/(SICI)1097-0134(19991101)37:2<228::AID-PROT8>3.0.CO;2-8View ArticlePubMedGoogle Scholar
- Morris GM, Goodsell DS, Huey R, Olson AJ: Distributed automated docking of flexible ligands to proteins: parallel applications of AutoDock 2.4. Journal of Computer-Aided Molecular Design 1996, 10: 293–304. 10.1007/BF00124499View ArticlePubMedGoogle Scholar
- Jones G, Willett P, Glen RC, Leach AR, Taylor R: Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology 1997, 267: 727–748. 10.1006/jmbi.1996.0897View ArticlePubMedGoogle Scholar
- Yang JM, Shen TW: A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators. Proteins 2005, 59(2):205–220. 10.1002/prot.20387View ArticlePubMedGoogle Scholar
- Tafi A, Bernardini C, Botta M, Corelli F, Andreini M, Martinelli A, Ortore G, Baraldi PG, Fruttarolo F, Borea PA, et al.: Pharmacophore based receptor modeling: the case of adenosine A3 receptor antagonists. An approach to the optimization of protein models. Journal of Medicinal Chemistry 2006, 49: 4085–4097. 10.1021/jm051112+View ArticlePubMedGoogle Scholar
- Wolber G, Seidel T, Bendix F, Langer T: Molecule-pharmacophore superpositioning and pattern matching in computational drug design. Drug Discovery Today 2008, 13: 23–29. 10.1016/j.drudis.2007.09.007View ArticlePubMedGoogle Scholar
- Yang JM, Chen YF, Tu YY, Yen KR, Yang YL: Combinatorial computational approaches to identify tetracycline derivatives as flavivirus inhibitors. PLoS One 2007, 2: e428. 10.1371/journal.pone.0000428PubMed CentralView ArticlePubMedGoogle Scholar
- Chin KH, Lee YC, Tu ZL, Chen CH, Tseng YH, Yang JM, Ryan RP, McCarthy Y, Dow JM, Wang AH, et al.: The cAMP receptor-like protein CLP is a novel c-di-GMP receptor linking cell-cell signaling to virulence gene expression in Xanthomonas campestris. Journal of Molecular Biology 2010, 396: 646–662. 10.1016/j.jmb.2009.11.076View ArticlePubMedGoogle Scholar
- Hung HC, Tseng CP, Yang JM, Ju YW, Tseng SN, Chen YF, Chao YS, Hsieh HP, Shih SR, Hsu JT: Aurintricarboxylic acid inhibits influenza virus neuraminidase. Antiviral Research 2009, 81: 123–131. 10.1016/j.antiviral.2008.10.006View ArticlePubMedGoogle Scholar
- Yang M-C, Guan H-H, Yang J-M, Ko C-N, Liu M-Y, Lin Y-H, Chen C-J, Mao SJT: Rational design for crystallization of beta-lactoglobulin and vitamin D-3 complex: revealing a secondary binding site. Crystal Growth & Design 2008, 8: 4268–4276. 10.1021/cg800697sView ArticleGoogle Scholar
- Warnmark A, Treuter E, Gustafsson JA, Hubbard RE, Brzozowski AM, Pike AC: Interaction of transcriptional intermediary factor 2 nuclear receptor box peptides with the coactivator binding site of estrogen receptor alpha. Journal of Biological Chemistry 2002, 277: 21862–21868. 10.1074/jbc.M200764200View ArticlePubMedGoogle Scholar
- Shiau AK, Barstad D, Loria PM, Cheng L, Kushner PJ, Agard DA, Greene GL: The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen. Cell 1998, 95: 927–937. 10.1016/S0092-8674(00)81717-1View ArticlePubMedGoogle Scholar
- Champness JN, Bennett MS, Wien F, Visse R, Summers WC, Herdewijn P, de Clerq E, Ostrowski T, Jarvest RL, Sanderson MR: Exploring the active site of herpes simplex virus type-1 thymidine kinase by X-ray crystallography of complexes with aciclovir and other ligands. Proteins 1998, 32: 350–361. 10.1002/(SICI)1097-0134(19980815)32:3<350::AID-PROT10>3.0.CO;2-8View ArticlePubMedGoogle Scholar
- Zhou HB, Sheng S, Compton DR, Kim Y, Joachimiak A, Sharma S, Carlson KE, Katzenellenbogen BS, Nettles KW, Greene GL, et al.: Structure-guided optimization of estrogen receptor binding affinity and antagonist potency of pyrazolopyrimidines with basic side chains. Journal of Medicinal Chemistry 2007, 50: 399–403. 10.1021/jm061035yView ArticlePubMedGoogle Scholar
- Manikowski A, Verri A, Lossani A, Gebhardt BM, Gambino J, Focher F, Spadari S, Wright GE: Inhibition of herpes simplex virus thymidine kinases by 2-phenylamino-6-oxopurines and related compounds: structure-activity relationships and antiherpetic activity in vivo. Journal of Medicinal Chemistry 2005, 48: 3919–3929. 10.1021/jm049059xPubMed CentralView ArticlePubMedGoogle Scholar
- Nissink JW, Murray C, Hartshorn M, Verdonk ML, Cole JC, Taylor R: A new test set for validating predictions of protein-ligand interaction. Proteins 2002, 49: 457–471. 10.1002/prot.10232View ArticlePubMedGoogle Scholar
- Bissantz C, Folkers G, Rognan D: Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations. Journal of Medicinal Chemistry 2000, 43: 4759–4767. 10.1021/jm001044lView ArticlePubMedGoogle Scholar
- Sayle RA, Milner-White EJ: RASMOL: biomolecular graphics for all. Trends in Biochemical Sciences 1995, 20: 374. 10.1016/S0968-0004(00)89080-5View ArticlePubMedGoogle Scholar
- Saldanha AJ: Java Treeview--extensible visualization of microarray data. Bioinformatics 2004, 20: 3246–3248. 10.1093/bioinformatics/bth349View ArticlePubMedGoogle Scholar
- Yang J-M, Shen T-W: A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators. Proteins 2005, 59: 205–220. 10.1002/prot.20387View ArticlePubMedGoogle Scholar
- Fradera X, Knegtel RM, Mestres J: Similarity-driven flexible ligand docking. Proteins 2000, 40: 623–636. 10.1002/1097-0134(20000901)40:4<623::AID-PROT70>3.0.CO;2-IView ArticlePubMedGoogle Scholar
- Qin Z, Kastrati I, Chandrasena RE, Liu H, Yao P, Petukhov PA, Bolton JL, Thatcher GR: Benzothiophene selective estrogen receptor modulators with modulated oxidative activity and receptor affinity. Journal of Medicinal Chemistry 2007, 50: 2682–2692. 10.1021/jm070079jView ArticlePubMedGoogle Scholar
- Manas ES, Xu ZB, Unwalla RJ, Somers WS: Understanding the selectivity of genistein for human estrogen receptor-beta using X-ray crystallography and computational methods. Structure 2004, 12: 2197–2207. 10.1016/j.str.2004.09.015View ArticlePubMedGoogle Scholar
- Brzozowski AM, Pike AC, Dauter Z, Hubbard RE, Bonn T, Engstrom O, Ohman L, Greene GL, Gustafsson JA, Carlquist M: Molecular basis of agonism and antagonism in the oestrogen receptor. Nature 1997, 389: 753–758. 10.1038/39645View ArticlePubMedGoogle Scholar
- Shadnia H, Wright JS, Anderson JM: Interaction force diagrams: new insight into ligand-receptor binding. Journal of Computer-aided Molecular Design 2009, 23: 185–194. 10.1007/s10822-008-9250-3View ArticlePubMedGoogle Scholar
- Wild K, Bohner T, Folkers G, Schulz GE: The structures of thymidine kinase from herpes simplex virus type 1 in complex with substrates and a substrate analogue. Protein Sci 1997, 6: 2097–2106. 10.1002/pro.5560061005PubMed CentralView ArticlePubMedGoogle Scholar
- Kussmann-Gerber S, Kuonen O, Folkers G, Pilger BD, Scapozza L: Drug resistance of herpes simplex virus type 1--structural considerations at the molecular level of the thymidine kinase. European Journal of Biochemistry /FEBS 1998, 255: 472–481. 10.1046/j.1432-1327.1998.2550472.xView ArticlePubMedGoogle Scholar
- Hinds TA, Compadre C, Hurlburt BK, Drake RR: Conservative mutations of glutamine-125 in herpes simplex virus type 1 thymidine kinase result in a ganciclovir kinase with minimal deoxypyrimidine kinase activities. Biochemistry 2000, 39: 4105–4111. 10.1021/bi992453qView ArticlePubMedGoogle Scholar
- Pilger BD, Perozzo R, Alber F, Wurth C, Folkers G, Scapozza L: Substrate diversity of herpes simplex virus thymidine kinase. Impact Of the kinematics of the enzyme. The Journal of Biological Chemistry 1999, 274: 31967–31973. 10.1074/jbc.274.45.31967View ArticlePubMedGoogle Scholar
- Evans JS, Lock KP, Levine BA, Champness JN, Sanderson MR, Summers WC, McLeish PJ, Buchan A: Herpesviral thymidine kinases: laxity and resistance by design. The Journal of General Virology 1998, 79: 2083–2092.View ArticlePubMedGoogle Scholar
- Pan Y, Huang N, Cho S, MacKerell AD Jr.: Consideration of molecular weight during compound selection in virtual target-based database screening. J Chem Inf Comput Sci 2003, 43(1):267–272.View ArticlePubMedGoogle Scholar
- Sulpizi M, Schelling P, Folkers G, Carloni P, Scapozza L: The rational of catalytic activity of herpes simplex virus thymidine kinase. a combined biochemical and quantum chemical study. The Journal of Biological Chemistry 2001, 276: 21692–21697.View ArticlePubMedGoogle Scholar
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