Validation of Automated Docking Programs for Docking and Database Screening against RNA Drug Targets

Validation of Automated Docking Programs for Docking and DatabaseScreening against RNA Drug Targets

Carsten Detering and Gabriele Varani*

Departments of Chemistry and Biochemistry, University of Washington, Seattle, Washington 98195-1700

Received December 30, 2003

The increasing awareness of the essential role of RNA in controlling viral replication and inbacterial protein synthesis emphasizes the potential of ribonucleoproteins as targets fordeveloping new antibacterial and antiviral drugs. RNA forms well defined three-dimensionalstructures with clefts and binding pockets reminiscent of the active sites of proteins.Furthermore, it precedes proteins in the translation pathway; inhibiting the function of a singleRNA molecule would result in inhibition of multiple proteins. Thus, small molecules that bindRNA specifically would combine the advantages of antisense and RNAi strategies with themuch more favorable medicinal chemistry of small-molecule therapeutics. The discovery ofsmall-molecule inhibitors of RNA with attractive pharmacological potential would be facilitatedif we had available effective computational tools of structure-based drug design. Here, wesystematically test automated docking tools developed for proteins using existing three-dimensional structures of RNA-small molecule complexes. The results show that the nativestructures can generally be reproduced to within 2.5 Å more than 50-60% of the time. Formore than half of the test complexes, the native ligand ranked among the top 10% compoundsin a database-scoring test. Through this work, we provide parameters for the validatedapplication of automated docking tools to the discovery of new inhibitors of RNA function.

Introduction

The explosion in the number of structures of proteinsand their complexes and advances in computationalmethods over the past decade have allowed greatprogress to be made in the development of automaticdocking methods. Such programs now provide an es-tablished set of tools to discover novel inhibitors forprotein drug targets.1-11 Combined with high-through-put screening methods and with new ways to use NMRand X-ray crystallography in the early phases of drugdiscovery,12-17 these methods have resulted in thediscovery of new lead compounds.6,18-21 An importantaspect of the successful application of these programshas been the validation of docking tools and the estab-lishment of tested parameters for their application.22-24

In sharp contrast, rational drug design methods directedat DNA and RNA have received much less attention.In significant part, this has been due to the paucity of3D structures of small molecule-nucleic acid complexes.Until recently, we simply did not know enough aboutRNA recognition to apply rational methods to thediscovery of new RNA-binding compounds. It is none-theless likely that the application of computationaldocking methods will provide very valuable approachesto increase the likelihood of discovering druglike RNA-binding compounds. Because existing computationalapproaches and scoring functions have been developedfor proteins, we do not know how well they work in theRNA environment. It is the goal of this work to evaluatethe performance of automated docking tools againstRNA.

RNA is an attractive target for infectious diseasebecause of unique resistance patterns and mechanismof action of potential RNA-binding drugs. Because RNAis located upstream from proteins in the gene-expressionpathway, blocking one RNA molecule would inhibit thefunction of multiple proteins by affecting their synthe-sis.25-28 RNA has long been a target of antimicrobialtherapy; many existing antibiotics bind to the ribo-some. Among them are the aminoglycosides,26,29-37

pharmaceutically more important drugs such as themacrolide erythromycin,38 and a recent new class ofantibiotics (oxazolidinones39,40). While the potential ofRNA as a drug target has been advocated for sometime,25,26,28,33,41-45 progress has not been easy. ExistingRNA-binding antibiotics do not provide attractive para-digms. Most are natural products; they tend to be highlypositively charged, have multiple torsional bonds, andare often relatively large compounds. They generallyviolate “rules” that describe the features of successfuldrugs established empirically through the analysis ofexisting therapeutic compounds.46 They also tend to bepoorly specific; they target not only the bacterial ribo-somal RNA but many other RNAs as well with compa-rable activity in vitro. Clearly, there is a need to discovernew scaffolds and functional groups suitable for RNArecognition. While the high-throughput screening ofproprietary and combinatorial libraries has providedinsight into RNA recognition and a number of chemi-cally attractive RNA ligands,47-51 it is necessary toexplore chemical space more widely in search of com-pounds suitable for binding to RNA.

RNA forms well-defined tertiary structures with deeppockets and clefts lined by hydrogen-bonding groupsreminiscent of the active site of protein enzymes.26,28,44

* To whom correspondence should be addressed. E-mail: [email protected]. Phone: 206-543-7113. Fax: 206-685-8665.

4188 J. Med. Chem. 2004, 47, 4188-4201

10.1021/jm030650o CCC: $27.50 © 2004 American Chemical SocietyPublished on Web 07/13/2004

It should therefore be possible to apply rational drugdesign methods to discover new small-molecule inhibi-tors of RNA and RNA-protein complexes. Following avery early study on perfect double helical RNA,52 animportant proving ground has been represented by theHIV-1 TAR RNA.41,47,48,50 Other targets have been theribosomal A-site53 (the binding site for aminoglycosidesand other antibiotics) and a functional subdomain of 23SrRNA involved in the GTPase activity of the ribosome.54

The considerable recent progress in understandingRNA structure and folding, as well as recognition byother ligands,26,33,35,55-59 makes it now possible to applystructure-based design and docking methods to discovernew RNA-binding compounds. Our long-term objectiveis to develop reliable methods to screen chemicaldatabases to find druglike molecules that bind to RNAtightly and specifically and that retain attractive phar-macological characteristics. Here, we investigate whetherautomated docking methods successfully used in protein-based drug design can be applied to RNA as well. Forthis purpose, we have tested two docking tools (Auto-Dock60 and Dock61) to establish whether such programswould provide an effective platform for RNA-targeteddatabase screening.

Methods

Database Construction. We have tested the per-formance of AutoDock and Dock on a data set of 16selected ligand-RNA structures derived from the PDBfor which experimental binding constants are alsoavailable (Table 1). Approximately half of them areNMR structures of RNA aptamers; only two are crystal-lographic structures. Ribosomal complexes were notincluded in this test set because binding constants arenot available. We retained these structures as anindependent validation set (Table 2). The complexescover several logs in affinity (∆G varies from -4.09 to-12.34 kcal/mol) and are diverse in structure. Theligands range from small, very flexible aliphatic mol-ecules with 12 heavy atoms to large flexible and cyclicmolecules (30-40 heavy atoms, 3-4 rings) and rigidaromatic molecules with and without flexible side chains(13-30 atoms, 2 and 3 rings).

Molecule Preparation. The program MOE (Chemi-cal Computing Group Inc., Montreal; http://www.chemcomp.com) was used to separate the ligand fromthe RNA, to model missing residues, and to removewater molecules and counterions. For the NMR struc-tures we used the first one in the set unless a different

Table 1. Database of RNA-Ligand Complexes

PDB code compd technique Kd (µM) ∆G (kcal/mol) ref

Rigid and Aromatic1AM0 AMP aptamer NMR 2.7 -7.59 1081EHT theophylline aptamer NMR 0.4 -8.73 1091F1T malachite green aptamer X-ray/2.8 Å 0.04 -10.09 1101F27 biotin aptamer X-ray/1.3 Å 6 -7.12 1111FMN FMN aptamer NMR 0.5 -8.59 1121LVJ TAR-acetylpromazine NMR 0.1 -9.55 47

Weak Binders1AJU HIV-2 TAR argininamide NMR 1000 -4.09 1131KOC arginine aptamer NMR 60 -5.76 1141KOD citrulline aptamer NMR 68 -5.68 114

Aminoglycosides1BYJ gentamycin ribosome A-site NMR 0.01 -10.91 1151EI2 neomycin splice regulator NMR 1 -8.18 581NEM neomycin aptamer NMR 0.1 -9.55 301PBR paromomycin ribosome A-site NMR 0.2 -9.14 1161QD3 neomycin HIV 1 TAR NMR 1 -8.18 1171TOB tobramycin aptamer NMR 0.0009 -12.34 1182TOB tobramycin aptamer NMR 0.0012 -12.17 32

Table 2. Data Set of Antibiotic Structures

PDB code resoln (Å) ref

1FJG thermophilus 30S ribosomal subunit + streptomycin, spectinomycin, and paromomycin 3.0 1191HNW thermophilus 30S ribosomal subunit + tetracycline 3.4 1201HNX thermophilus 30S ribosomal subunit + pactamycin 3.4 1201HNZ thermophilus 30S ribosomal subunit + hygromycin B 3.3 1201J7T 16S-rRNA A-site + paromomycin 2.5 1211JZX 50S ribosomal subunit + clindamycin 3.1 381JZY 50S ribosomal subunit + erythromycin 3.5 381JZZ 50S ribosomal subunit + roxithromycin 3.8 381K01 50S ribosomal subunit + chloramphenicol 3.5 381K8A 50S ribosomal subunit of Haloarcula marismortui + carbomycin 3.0 1221K9M 50S ribosomal subunit of Haloarcula marismortui + tylosin 3.00 1221KD1 50S ribosomal subunit of Haloarcula marismortui + spiramycin 3.0 1221LC4 16S-rRNA A-site + tobramycin 2.54 1231M90 50S ribosomal subunit + sparsomycin + CCA-phe-caproic acid 2.8 1241MWL 16S-rRNA A-site + geneticin 2.4 1251NJM 50S large ribosomal subunit + sparsomycin 3.6 1261NJN 50S large ribosomal subunit + sparsomycin 3.7 1261NJO 50S large ribosomal subunit + accupuromycin (ACCP) 3.7 1261NWY Large ribosomal subunit + azithromycin 3.3 1271OND 50S ribosomal subunit + troleandomycin 3.4 128

Validation of Docking Programs Journal of Medicinal Chemistry, 2004, Vol. 47, No. 17 4189

structure was specified as the minimum energy struc-ture in the PDB file. Hydrogen atoms where added tothe X-ray structures using standard geometrical pa-rameters as implemented in MOE. Partial charges forthe RNA were added according to Kollman ’94.62 Forthe ligands, we used PEOE (partial equalization oforbital electronegativities) charges.63,64 It should benoted that the AutoDock scoring function was calibratedusing these two same sets of charges for the receptorand the ligands.60 We evaluated other charges for theRNA (CFF91, Charmm and Gasteiger), but the afore-mentioned combination gave the best results in cor-relating ∆Gexp with ∆Gpred (data not shown) and wastherefore used throughout this study.

Electrostatics and Solvation. Force field scoring,as used in Dock, tends to overestimate electrostaticenergy.65 To reduce the overall negative charge on thebackbone of the RNA (there are no counterions presentin any of the NMR structures), we increased the chargeon the phosphorus atom from 1.166 to 2.166 to simulatethe presence of a sodium counterion. The advantage ofthis is that the charge on the phosphorus is reduced.James and colleagues similarly scaled the negativecharge on the phosphate groups to 20-30%.66,67 Solva-tion parameters for AutoDock were derived from similaratom types in amino acids (in Supporting Information).In addition, nitrogen atoms accepting and not acceptinghydrogen bonds were separately defined for the gridmap calculation. A 12-6 Lennard-Jones potential wasused for nitrogen-carrying groups that do not formhydrogen bonds, while a 12-10 potential was used forhydrogen-bond-forming nitrogens.

Dock. Version 4.0.1 of the Dock suite of programs isstill regarded as state of the art68,69 and was used inthis study. Connolly surfaces70,71 were constructed usingMS (no. 429 in QCPE, Indiana University). Dock usesa parameter (dotlim in the INSPH file) to facilitatesphere construction for wide active sites (such as theDNA or RNA major grooves); it was set to -1. Thebinding site was represented in each case through 20-100 overlapping spheres constructed with sphgen.72 Thethree-dimensional grid was then calculated with aspacing of 0.3 Å, centered in the active sites andextended by 10 Å in each direction. Energy scoring gridswere obtained by using the all-atom model and adistance-dependent dielectric function with a 10 Å cutoffand a dielectric factor of 4. A bump filter was used withan overlap of 0.75. For rigid docking, we used automatedmatching with a maximum of 5000 orientations; zerobumps were allowed. For rigid database docking, weused manual matching61 with a maximum of 5000orientations, a distance tolerance of 0.5 Å, and a bumpfilter with zero bumps allowed. For flexible docking ofsingle compounds, we used no anchor search, clashoverlap of 0.5 Å, and a conformation cutoff factor of 5.For flexible database docking, we used anchor searchwith torsion drive and 50 configurations per cycle, aflexible bond maximum of 20, and a distance toleranceof 0.5 Å.

AutoDock. AutoDock, version 3.05, was used in thisstudy. The grid for energy evaluation was set in thecenter of gravity of the ligand with dimensions of 60points × 60 points × 60 points and spacing of 0.375 Å.Initial translation, quaternion, and torsion steps of 2.0

Å, 50.0°, and 50.0°, respectively, were chosen with areduction factor of 1 per cycle. Standard Lamarckiangenetic algorithm parameters were used. We used aunited atom representation for the ligand, which gavea better correlation of predicted and experimentalbinding energies when compared to the all-atom rep-resentation (data not shown). The united atom repre-sentation also speeds up the search. Each dockingsimulation consisted of 100 independent docking runs.For database docking, self-written shell scripts dockedeach ligand consecutively into the active site andextracted the ranking and scoring parameters from thedocking log file.

Docking Accuracy and Reliability. An effectivedocking program must be reliable and accurate (mul-tiple docking runs generate the target or a very similarstructure), must be able to rank compounds accurately,and must identify putative ligands from random com-pound sets to minimize the number of false positives.It is also useful for the program to provide at leastqualitative estimates of the binding constant. To estab-lish reliability parameters, 100 independent dockingruns were performed with both programs. We definedocking accuracy as how well the native pose of theligand is reproduced by the combination of algorithmand scoring function in multiple independent runs. Anarbitrary value of a 2.5 Å rmsd from the experimentalstructure was chosen to separate successful and unsuc-cessful docking poses. Cutoff values between 1.0 and 3.0Å root-mean-square deviation (rmsd) between dockedand X-ray pose have been used in past studies to definesuccessfully docked poses.2,65,73,74 While a value of 2 Åis most often used,23,24,75,76 we increased the cutoff to2.5 Å because most test complexes are NMR structuresand are therefore less precise and accurate. In testinghow well the experimental binding constant is repro-duced by AutoDock, we used the epdb function toevaluate the energy of the native structures in the activesite of the RNA. For each NMR-derived coordinate set,10 independent structures were extracted from theNMR ensemble to estimate how the coordinate erroraffects the energy evaluations.

Database Docking. A primary characteristic of aneffective database-docking tool is the ability to rankcognate ligands highly while at the same time rankingnonbinding ligands unfavorably. This is crucial tominimize the number of false positives. We conductedtwo tests. In the cross-docking experiment, every ligandof the test set was docked against each of the RNApresent in the test set. This test is highly demanding.The native ligand should ideally to be ranked as thehighest compound among other RNA binding ligands.In the second test, we spiked a database of 49 drugsrandomly chosen from the comprehensive medicinalchemistry database, CMC (MDL, San Leandro) witheach RNA-binding ligand. This test represents a real-istic if limited in size simulation of a real databasedocking experiment. For Dock, all ligands were savedin a single multi-mol2 file that was used as the ligandinput file. Since AutoDock does not provide a routinefor database docking, each ligand was docked separatelyinto the receptor and the results were then collected ina single file.

4190 Journal of Medicinal Chemistry, 2004, Vol. 47, No. 17 Detering and Varani

Results

In seeking to validate automated docking tools for usewith RNA, we selected two programs that use differentalgorithms and two different approaches to evaluate theinteraction energy. Dock77-80 uses a shape-based match-ing algorithm and a force-field based scoring functionderived from Amber.62,81,82 The interaction energy com-prises van der Waals and electrostatic interactions butlacks explicit hydrogen bonding, solvation, and hydro-phobicity, as well as entropic terms describing solvationand freezing of rotational degrees of freedom. Thisapproach is inherently unable to provide absoluteestimates of binding constant and can only rank com-pounds. AutoDock83 uses a genetic algorithm for globalsearch (Lamarckian genetic algorithm) and a localsearch algorithm.84 The program uses a semiempiricalenergy function calibrated on 30 protein-ligand com-plexes.85 It comprises van der Waals, hydrogen bond,electrostatics, torsional terms, and a solvation termbased on a sigmoidal distance-dependent dielectricfunction.86,87 Unfortunately, other programs that usedifferent algorithms are at present unsuitable for work-ing with nucleic acids. FlexX, for example, lacks atomtypes definition for nucleotides.88-90

The binding free energy of RNA-ligand com-plexes can be qualitatively reproduced by asemiempirical scoring function. We first evaluatedhow well the inhibition constants of RNA-ligand com-plexes can be predicted qualitatively through the semi-empirical approach implemented in AutoDock. Althoughthe database of 16 complexes available at the presenttime is small and the available KD range not vast, thesmall molecules are diverse in structure. We expectedthat the experimental results would be reproduced onlyafter recalibration of the weight of different componentsof the scoring function. However, when measured andpredicted binding constants are compared, we obtain abest-fit line (excluding the two outliers) with a slope veryclose to ideal (1) and intercept close to the origin (Figure1). Because many of the structures in the database areNMR-derived, multiple copies of the same coordinateset are generally available. By calculating the predictedfree energy of binding for the same complex usingdifferent coordinate sets, we were able to evaluate howthe uncertainty on the coordinates affects the freeenergy prediction. When considering the experimentaluncertainty both in the measured (error bars in Figure1a, left) and predicted (dots in Figure 1b, right) bindingenergies, and the very limited data set of structures(only 16), we concluded that recalibration of the scoringfunction would be unwarranted. The data of Figure 1suggest that the AutoDock scoring function appears toreproduce existing binding constants for the RNA-smallmolecule constant semiquantitatively. A larger data setof structures will be required to improve the model andprovide a more accurate description of the bindingenergy of RNA-small molecule complexes.

The outlier structures (1NEM and 1F1T, encircled inFigure 1) provide interesting insight into the limitationsof the scoring function. Nitrogen atoms on the RNAbases are treated by AutoDock as hydrogen bond ac-ceptors and are represented by a spherical potentialaround the atom center,91 leading to a region of un-realistically favorable interaction energy both above and

below the plane of the base (Figure 2). Thus, the lackof directionality for hydrogen bond acceptors appearsto be a limitation of the simple model used to deriveenergy estimates.

Given the highly charged nature of RNA molecules,we also examined whether different charge sets wouldimprove the predictive value of the semiempiricalpotential. Different combinations of partial charges forthe receptor and the ligands were independently evalu-ated. We found that the combination of Kollman9462

charges for the receptor and PEOE63,64 charges for theligand gave the best results based on the correlation ofpredicted and experimental free energy of binding (datanot shown). This result is probably attributable to thefact that AutoDock was calibrated using this exactcombination of partial charges.60

Reliability of the Docking Algorithms and Scor-ing Functions. A strongly performing docking programshould be able to reproduce the target structure reliablyand accurately. To examine this feature, we evaluatedhow many independent poses cluster to within a presetdeviation from the experimentally determined target

Figure 1. Predicted vs experimental free energy of bindingfor the 16 training complexes as calculated by AutoDock. (a)Predicted vs experimental binding energies. The error barsrepresent either reported uncertainties on measured Kd (when-ever available) or a 20% uncertainty (whenever experimentalerror estimates were not reported). A line of best fit (shownin red, excluding the circled outliers) has a standard deviationof 2.12 kcal/mol. (b) Uncertainties in the experimental coor-dinates affect the prediction of the free energy of binding. Foreach complex, the binding free energy was calculated inde-pendently and plotted for the 10 best structures enclosed inthe NMR-derived coordinate set.


structures. An example of the performance of AutoDockon different sets of ligands is provided in Figure 3, whichshows superposition of multiple independently gener-ated docking poses for three complexes. Quantitativeestimates are shown in Figure 4, where we report the“successful docking rate”: the number of structures in100 independent docking runs that cluster within 2.5Å of the target structure.

Dock reproduces the native structure to within 2.5 Åfor complexes of rigid aromatic ligands but performspoorly with weak-binding ligands (1AJU, 1KOC, 1KOD)and with aminoglycosides (Figure 4a). The three weak-binding complexes (argininamide-TAR 1AJU, arginineaptamer 1KOC, and citrulline-aptamer 1KOD) are alsovery flexible ligands. The poor performance of thealgorithm is understandable given the relatively weakbinding constant and flexible nature of the ligands.Concerning 1AJU, it has also been shown that multiplebinding sites on TAR exist.51 For aminoglycosides, onlyin some cases (paromomycin 1PBR and tobramycin Iaptamer 1TOB) can the target structure be reproducedsuccessfully. Complexes where hydrophobic interactionspredominate (for example, complexes where intercala-tion is observed such as 1AM0, 1EHT, 1F1T, 1FMN)were instead docked successfully more than 50% of thetime. Dock has been reported to perform better withhydrophobic binding sites and to be less effective withhydrophilic target structures.22 In a test conducted withherpes simple virus thymidine kinase, a receptor witha wide binding site and high accessibility to water (notunlike RNA), Dock was unable to find the correct posefor 3 out of 10 known ligands. It was also able to identifyonly 1 out of 10 known ligands from a database of 990molecules (among the top 5% scorers).22 An additionallimitation is of a technical nature. Dock generates animage of the active site through a set of spheres; thequality of the constructed spheres is critical to itssuccess. The major groove of RNA is much more openand wide than a “classical” active site pocket, makingsphere construction less rigorous.

AutoDock reproduces the conformation observed inthe target complex (Figure 4b) much more often thanDock. More than 50% of the docked structures lie withinthe cutoff value for most ligands, the exceptions beingagain two of the weak binders (1AJU, 1KOD) and someaminoglycosides (1EI2, 1QD3, 1PBR), the same struc-tures that performed worse with Dock.

A complementary way to evaluate the reliability ofdocking programs is to plot the results of each inde-pendent docking run after they have been ordered forincreasing deviation from the target (Figure 5). Ideally,the line should be knee-shaped, with a majority ofdocked structures falling below the rmsd cutoff value.This pattern is seen for AutoDock in a majority of cases(the most successful being 1EHT, 1F1T, 1TOB, 2TOB,1BYJ, 1F27, 1AM0, 1KOC, 1NEM, and 1FMN). For

Figure 2. Close-up of 1NEM highlighting out-of-plane elec-trostatic interactions between neomycin amines and adenosinebase nitrogens. Contacts with base nitrogen atoms above andbelow the base plane are identified with cyan lines. The RNAbackbone is represented by the tube, and residue A13 of theRNA and the ligand are rendered in ball-and-stick representa-tion. Ligand hydrogen atoms have been omitted for clarity.The figure was produced with MolScript129 and rendered withRaster3D.130-132

Figure 3. Superposition of representative structures frommultiple independent docking runs (AutoDock): (a) 1EHT; (b)1AM0; (c) 1AJU. Native ligands are in atom color. For 1AM0and 1EHT, all docking runs cluster within 2.5 Å of the targetstructure. For argininamide (1AJU, a major groove binder withlow affinity), the ligand is found to cover a wide section of thebinding pocket. Clustering of the ligand in preferred locationsis nonetheless observed even in this least successful example.


Dock, only a few test structures (1EHT, 1F1T, 1F27,and 1FMN) yield rmsd-ordered curves comparable tothose observed with AutoDock.

Docking and Ranking Accuracy. We next evalu-ated whether structures that cluster the closest to thetarget are also ranked most favorably. In a stronglyperforming docking program, poses that reproduce theexperimental structure the best would also correspondto the best scoring docking runs. Ideally, one would liketo observe a plot in which the structures closest to thetarget cluster closely in space and are ranked amongthe best scoring poses, thus generating a sector-shapedplot.

When the rank of each independent pose is plottedversus the rmsd from the native structure (Figure 1 inSupporting Information), Dock generally provides a poorcorrelation between ranking and docking except for1F27, 1FMN, and 1F1T (where most poses cluster atlow rmsd from the target). In contrast, the rigidaromatic compounds (1AM0, 1EHT, 1F1T, 1F27) and1TOB can be docked by AutoDock to within a verynarrow rmsd range near the target. Satisfactory behav-ior is observed for 1FMN and 1NEM and, less clearly,for 1PBR, 1QD3, 2TOB, and 1BYJ. The sector shape ofthe plot shows the clustering of low-rmsd docking runsamong the higher-ranking compounds, while high-rmsd

compounds generally tend to rank worse. No correlationbetween ranking and scoring is observed instead for theweak binding compounds 1AJU and 1KOD, for 1LVJ(a rigid compound), and for 1EI2 (an aminoglycoside).For 1EI2, for example, the pose closest to the target (1.1Å rmsd) ranks 16/100, while the best scoring structureis 4.73 Å away from the target. For 1KOD the closest-docked structure (1.78 Å rmsd from the native) ranks44, while the best scoring structure is 10.28 Å away fromthe target.

A rigorous way to identify test structures with strongcorrelation between ranking and rmsd is provided bySpearman’s rank correlation coefficient.92 Most test setsare positively correlated (as desired) (Table 3), butgenerally the correlation is weak, indicating the in-ability of the programs to favorably rank structures thatcluster close to the target for these complexes.

Database Docking and Cross-Docking Tests. Thefirst and most stringent test of the performance of thedocking algorithms in a database search was the cross-docking experiment (Figure 6a). In this test, each of the16 ligands competes with the other 15 to rank first forits cognate receptor. This test is very demandingbecause the programs have to sort the native ligandfrom a set of RNA binding molecules that generallyshare common RNA-binding characteristics (such asfavorable electrostatics). Furthermore, the aminoglyco-

Figure 4. Reproducibility of the target structures in multipleindependent docking runs. For each test structure, the ligandwas independently docked 100 times into the active site andthe resulting poses were compared to the experimentallydetermined native structure. Structures that cluster within2.5 Å rmsd of the test structure were regarded as successfullydocked. (a) Dock: blue bars show the result for rigid bodydocking, while green represents flexible ligand docking. (b)AutoDock.

Figure 5. The rmsd from the target structure for independentdocking runs. The figure reports the rmsd-ordered deviationfrom the target of successively docked structures for Dock (a)and AutoDock (b). For most test structures, many more posescluster near the target structure when AutoDock is used. Each“docking run” represents an independent docking experiment.


sides are known to bind multiple RNA targets equallywell and with activity significantly stronger than re-ported for the weak binding compounds in the data set.They should be expected to score favorably even againstnoncognate targets and to outrank weak-binding com-pounds such as arginineamide.

AutoDock positions the correct ligand near the top(rank 1 or 2) in 7 out of 16 cases. As expected, thealgorithm has difficulties finding the correct ligand forthe weak binders 1AJU, 1KOC, and 1KOD, but itperforms surprisingly well for most aminoglycosides.Except for 1EI2 and 1QD3 (both neomycin complexes),all aminoglycoside complexes are correctly ranked asthe top or second-best ligand. Dock found the cognateligand in the top 2 positions in 4 out of 16 cases usingflexible docking (bonds are allowed to rotate accordingto a rotamer library) and 9 out of 16 times using rigiddocking (the conformation of the ligand is maintainedand only its orientation is probed on the surface of thereceptor). Because the orientation is predetermined andcorrect, it is easier for the ligand to find its cognate site;thus, executing this test in the “rigid” mode over-represents the success rate of the algorithm. None ofthe aminoglycosides were positioned satisfactorily inflexible docking, while three (1EI2, 1TOB, and 1PBR)were positioned correctly using rigid docking (data notshown). As in other tests, Dock produced better resultsfor ligands with hydrophobic ring systems.

In the database-docking test (Figure 6b), we simu-lated the process of screening a database. A small testgroup of 49 drugs taken from the CMC (ComprehensiveMedicinal Chemicals, MDL; San Leandro, CA) wasspiked with each of the 16 native ligands one at a time.The algorithms were asked to dock the native ligandand each of the 49 noncognate molecules and to comparethe respective ranks. An arbitrary 10% cutoff value(rank 1-5) was used to provide intuitive guidance. Inthe flexible docking test, 10 out of 16 native ligands areidentified among the five best ranking compounds byboth Dock and AutoDock. AutoDock ranked the entireset of the aminoglycosides among the best-ranking

ligands, but the weak binders were problematic (asexpected) and so were several rigid aromatic compounds(surprisingly). Neomycin (1QD3) is well ranked byAutoDock but not by Dock. This is likely to be due to apartially closed binding site with one base partly enclos-ing the ligand, requiring a more sophisticated searchmethod (as implemented in AutoDock) to position theligand correctly.

Consensus Scoring. Consensus scoring (i.e., com-bining different scoring functions to rank compounds)can drastically enhance the outcome of database screen-ing by reducing the number of false positives.22,75,93,94

Consensus scoring can be executed through “rank-by-rank” (the score is the average rank of each compoundcalculated from each participating scoring function),“rank-by-number” (each scoring function contributes tothe score with its individual predicted value, and theconsensus score is simply the resulting average), and“rank-by-vote” (if a compound is ranked among the topX% of the database, it gets a vote from that scoringfunction; the final score is the number of votes from allparticipating scoring functions). Rank-by-number is notappropriate given the completely different nature of the

Table 3. Spearman’s Rank Correlation Coefficient Calculatedfor the Results from 100 Docking Experiments for EachComplex in the Test Set (See Also Figure 1 in SupportingInformation)

rank/rmsd correlation coefficient for

complex Dock AutoDock

Rigid and Aromatic1AM0 0.039 53 0.371 281EHT 0.356 82 -0.199 321F1T 0.196 62 0.258 221F27 -0.222 82 -0.256 911FMN 0.267 10 0.522 311LVJ 0.112 97 0.512 99

Weak Binders1AJU -0.140 32 -0.205 671KOC 0.095 48 0.178 351KOD 0.608 99 -0.519 27

Aminoglycosides1BYJ 0.069 55 0.740 391EI2 -0.090 92 -0.124 551NEM 0.050 80 0.935 051PBR 0.408 17 0.671 411QD3 -0.117 40 0.456 421TOB -0.032 96 -0.105 492TOB 0.063 09 0.729 89

Figure 6. Cross-docking and database docking tests. (a) Asmall database containing all the ligands in the test set wasconstructed and screened against each of the target structurescontained in the RNA-ligand database of Table 1. (b) In thedatabase docking test, the programs are asked to recognizethe native ligand in a test database of 49 non-RNA bindingcompounds. In both tests, AutoDock (red) performs better withaminoglycosides while Dock (green) is most effective with rigidaromatic compounds. The vertical lines represent the top 10%cutoff line.


two scoring functions (force field versus empirical) whilerank-by-vote is regarded as less reliable and is notrecommended.94 Therefore, we used “rank-by-rank”.

We wished to estimate which combination of scoringfunctions and docking algorithms is likely to provide thesmallest number of false positives. If we were to pickcompounds at random without any knowledge of theirsuitability for the target, there would be no “enrich-ment” of cognate ligands among the top ranking com-pounds (Figure 7, column 1). The combination of scoringand docking that provides the highest enrichment rate(the largest number of compounds known to bind amongthe top ranking compounds, i.e., the smallest numberof false positives) would maximize the effectiveness ofthe protocol by placing the largest number of cognateligands among those to be physically screened (the topranking compounds, Figure 7). Because we do not haveeven a single example of several known inhibitorstargeting the same receptor, it was not possible togenerate a meaningful test through this approach.Instead, we generated a larger number of results bycombining the results of the 16 database dockingexperiments against all 16 target RNAs (16 independentlibraries), each containing the 49 known drugs and 1 ofthe ligands from our test case. For example, gentamycinwas included in the “gentamycin” library with the 49known drugs and this library was then docked againstthe RNA from the gentamycin-RNA complex. Thisprocedure was repeated for each of the 16 complexes,and the results were evaluated together.

When compounds are picked at random, there isobviously no enrichment (Figure 7, column 1, randomscore). Some enrichment of native ligands among thetop scoring compounds (a line is arbitrarily drawn forthe top five scores to guide the eye) can be observed inthe Dock contact score (second column from the left)

compared with the random result, but the spread is stillreminiscent of a random distribution and the improve-ment is not significant. Contact scoring favors nonpolarinteractions,95 while RNA and most of our ligands areobviously charged. Energy scoring results in muchbetter enrichment (column 3, Dock energy score); com-pounds known to bind the target are generally foundwithin the best 20-30% scoring compounds in thedatabase with the exception of 1LVJ, 1QD3, 2TOB (thelast two are aminoglycosides). A similar pattern emergeswhen compounds are ranked on the basis of the Auto-Dock score (column 4), but the results are not asencouraging as with Dock. Combining the Dock energyscore (column 3) with AutoDock (column 4) does notsignificantly improve the enrichment rate (column 5,consensus score AutoDock/Dock) in comparison to Dockalone. The highest level of enrichment is observedinstead when compounds are docked first using Dockand then redocked by using AutoDock (column 6,redocking Dock with AutoDock). In this approach, weredocked the top five scoring compounds using theAutoDock scoring function and algorithm. If the nativeligand was not in the top five, we redocked all com-pounds that ranked better than the native ligand. Fortwo cases (1EHT and 1EI2) only, the ligand’s rank didnot improve, and in two other cases it remained thesame (1AJU and 1AM0). In all other cases, the rankingof the native ligand was improved.

Docking of Ribosome-Binding Antibiotics. Theantibiotic structures (Table 2) were docked using Auto-Dock to evaluate whether docking could be executed onthis well-validated drug target as well. Because thisdata set comprises X-ray structures of different resolu-tion (2.4-3.8 Å), this test also provides us an op-portunity to evaluate the effect of the quality of thestructure on docking. In docking ribosome-bindingcompounds, the binding site was defined to encompassa radius of 30 Å around the ligand binding site, and eachligand was docked 100 times to evaluate how welldocking poses could be reproduced and ranked. Theresults for two successful examples (accupuromycin andgeneticin) are shown in Figure 8, where the top-rankingposes are superimposed on the X-ray structure (rmsdvalues are 1.92 and 1.13 Å, respectively). When theribosome-ligand complexes were repeatedly docked toevaluate how reliably the program could identify thecorrect docking pose, however, the results were lesssatisfactory compared to the 16 compounds in theoriginal data set (Figure 9). Only two compounds(geneticin (1MWL) and tobramycin (1LC4)) docked nearthe correct pose more than 50% of the time. For sixcompounds (paromomycin 1J7T, hygromycin B 1HNZ,accupuromucin 1NJO, spiramycin 1KD1, carbomycin1K8A, and paromomycin 1FJG), the success rate was30-40%. For all remaining compounds (streptomycinand spectinomycin 1FJG, tetracycline 1HNW, pacta-mycin 1HNX, clyndamycin 1JZX, erythromycin 1JZY,chloramphenicol 1K01, tylosin 1K9M, azithromycin1NWY, and sparsomycin 1M90) a pose close to thetarget structure was found less than 30% of the time.For sparsomycin (1NJN, 1NJM) and troleandromycin(1OND), we could not find a single docking solutionwithin 2.5 Å of the target. In the three cases where theligand was most reliably docked into its native binding

Figure 7. Consensus scoring for database docking. The figureshows the rank (the position out of 50 compounds after scoring)of each native ligand from our test set in the test database asgenerated at random (leftmost column, random score) and byindividual scoring functions: (contact) Dock contact score;(energy score) Dock energy score; (AutoDock score) AutoDocksemiempirical free energy score. The result of combiningAutoDock and Dock energy scoring in a consensus score basedon the rank-by-rank approach is shown in column five (con-sensus score AutoDock/Dock). The rightmost column shows theresult of using AutoDock to rescore compounds that wereprescreened with Dock (redocking Dock with AutoDock).


site, the ligands were aminoglycosides (geneticin 1MWL,tobramycin 1LC4, and spyramycin 1KD1). Surprisingly,the six ligands that exhibit stacking interactions be-tween the ligand and the ribosome (e.g., sparsomycin

1M90) had low success rates of 9, 10, 11, 0, 0, and 36%(1HNW, 1K01, 1M90, 1NJM, 1NJN, and 1NJO, respec-tively).

We attempted to correlate these results with proper-ties of the ligand or of the binding site, but the onlysignificant correlation was with the crystallographicresolution. Structures of higher quality were dockedmore reliably (on average) than lower resolution struc-tures (data not shown). It is to be remarked that in theribosome test set, molecules are generally larger andmore flexible than in the test database of Figure 1. Forexample, tylosin (1K9M) with 64 heavy atoms could onlybe ranked accurately about 14% of the time (Figure 9).Troleandomycin (1OND) has 57 heavy atoms and couldnever be docked successfully in our test (below 2.5 Årmsd). In contrast, accupuromycin (1NJO), a moleculewith 37 heavy atoms, was docked 36 times out of 100times within 2.5 Å of the target structure. We be-lieve that the difficulty of docking algorithms to findcorrect solutions for large molecules with many flexiblebonds may be responsible for the less satisfactoryperformance of the programs with the ribosome set ofstructures.

Discussion

Before database-screening experiments can be ex-ecuted on RNA targets of pharmaceutical interest, it isimportant to establish how reliable and accurate dock-ing tools are when used with RNA. James and co-workers have recently described the very successfuldiscovery of a new class of ligands directed againstHIV-1 TAR RNA from database screening.47,48,50 How-ever, no attempt has so far been made, as far as we areaware, to analyze whether docking tools developed fordatabase docking against protein receptors provideeffective ligand docking and screening approaches forRNA as well. We chose two algorithms and scoringfunctions (Dock and AutoDock) that are widely availablein the academic community and that provide a diverseapproach to docking and scoring. While AutoDock usesa semiempirical scoring function and genetic program-ming,60 Dock uses a simpler ranking score derived fromthe Amber force field and a shape-matching dockingalgorithm.61 Dock handles large databases rapidly andhandily, while AutoDock is designed for use with oneligand at a time. Additional functionalities (specificallysolvation and simulation of electrostatics using thegeneralized Born approach or the Poisson-Boltzmannequation) have recently been implemented in Dock 5.However, the â version of Dock 5 was released whilethese studies were already well underway and theseimprovements were not evaluated here. The resultspresented here concern two different, easily availabledocking programs that could be readily adapted to workwith nucleic acids. Other attractive software packages(e.g., FlexX88-90) and scoring functions (e.g., Chem-score97 and Drugscore98,99) lack at the moment thecapability handling nucleic acids.

We were surprised to observe that the empiricalscoring function implemented within AutoDock ap-proximates at least in a qualitative sense the energy ofbinding of RNA-ligand complexes even if the trainingset used to optimize the parameters of the scoringfunction is composed of protein-drug complexes60,85,96

Figure 8. Ribosome docking test: (a) accupuromycin dockedagainst the 50S ribosomal subunit D site (1NJ0), where theresidue A2581 was omitted for clarity; (b) geneticin dockedagainst the eubacterial ribosomal RNA A-site (1MWL). Nativeligands are given in atom color. The figures were producedwith MolScript129 and rendered with Raster3D.130-132

Figure 9. Reproducibility of automated docking poses for theribosome complexes. For each structure (see Table 2), theligand was independently docked 100 times into the active siteand the resulting poses were compared to the experimentallydetermined structures. Poses that cluster within 2.5 Å of thetarget structure were considered successfully docked (see alsoFigure 4).


(Figure 1). We expected to have to reparametrize thescoring function. When the uncertainties on the bindingconstant, the small size of the training set, and espe-cially the limited precision of the coordinate set (mostcomplexes are NMR structures, Figure 1b) are consid-ered, it is questionable whether the existing datawarrant an improvement of the free energy model.

We considered testing other scoring functions devel-oped over the past several years, for example, Chem-Score,97 DrugScore,98,99 or XScore.24,100 However, nonecould be applied to RNA as currently implemented.Furthermore, all represent other examples of semi-empirical scoring functions (as used in AutoDock)trained on different sets of protein-ligand complexes.It is very likely that a genuinely superior semiempiricalmodel could be generated by training a semiempiricalscoring function on an RNA-only data set and byintroducing new terms to describe stacking and cat-ion-π interactions (which are known to be especiallyimportant in RNA- and DNA-ligand recognition).101-103

This remains an important goal for future studies, butmore experimental information will have to becomeavailable before it can be conducted effectively. It wouldalso be highly valuable to test the performance ofscoring functions that are neither force field derived(like Dock) nor semiempirical, for example, knowledge-based potentials. If this were to be done, scoringfunctions of genuinely different physical origin wouldbecome available, allowing consensus scoring to beconducted more effectively than currently possible.

An effective database-docking tool should identify themost favorable binding pose reliably and rank this modeof interaction highly. We addressed these features ofDock and AutoDock by repeatedly docking each of thecomplexes in the training set, thereby generating mul-tiple (100) independent poses. AutoDock identifies thetarget structure to within 2.5 Å for all ligands exceptvery weak binders (1AJU and 1KOD) and some amino-glycosides (1QD3, 1PBR, and 1EI2) (Figures 4b and 5b).In our tests, Dock was found to work best with nonpolarand small compounds perhaps because its force fieldbased scoring function underemphasizes solvent effectsand hydrogen-bonding interactions. These are moreextensively treated in AutoDock, perhaps explaining theimproved docking with aminoglycoside drugs, sincethese molecules form many NH and OH hydrogen bondswith nucleic acids. The performance with rigid aromaticcompounds (which are most attractive for genuine drugdiscovery efforts) is particularly encouraging. Dock isalso capable of docking these rigid compounds ac-curately and reliably into their cognate binding sites,but the performance with weak binders and amino-glycosides is very poor (Figures 4a and 5a). While theseresults may superficially indicate a substantial limita-tion of both sets of docking tools, a more careful con-sideration of the results is reassuring. It is not surpris-ing that weak binding ligands are docked poorly, andthe same is true for aminoglycosides; they are large,highly charged, and flexible molecules. Although amino-glycosides have provided a source of chemical in-spiration for many studies aimed at discovering newRNA-binding ligands, these molecules are quite unlikemost successful drugs and do not exhibit druglikeproperties.46,104-106

The results for docking accuracy obtained in thisstudy are comparable to those obtained with proteins.For example, in a study of 200 protein-small moleculecomplexes, Dock succeeded in 105/200 cases in dockingthe ligand within 2.0 Å of the target structure.107 Inanother test aimed at evaluating the effect of ligandflexibility on docking accuracy, 6/12 ligands were dockedwithin 2.0 Å of the experimental structure.61 AutoDockwas compared with 10 other scoring functions withrespect to docking accuracy. By use of the same cutoffrate used in the present study (2.5 Å), AutoDock had asuccess rate of 62%.24 The docking accuracies obtainedwith RNA in the present study are comparable to thoseobtained with proteins. This result is the more pleasingwhen it is considered that algorithms and, especially,scoring functions were trained on proteins.

The ribosomal test set included higher-quality X-raystructures compared to the mostly NMR structurescontained in the data set of Table 1, and we expectedincreased success. However, docking of ribosomal ligandswas generally less successful compared to the testdatabase results (Figure 9). Although there was a weakcorrelation between the quality of the X-ray structureand the success of the docking test, the generally largerand more flexible character of the ribosome-bindingantibiotics probably led to the inferior performance. Itis more difficult for the algorithm to find a correctsolution for a larger molecule with many flexible bonds.In a genuine database screening experiment, moleculesof the size and chemical complexity of the naturalproducts that bind the ribosome will not be likely to behighly represented nor would they generally provideattractive leads for further studies.

The results of Figures 4 and 5 indicate that both Dockand AutoDock can identify the target structure in thelarge majority of cases to within 2.5 Å for the type ofdruglike rigid molecules that are of most interest indrug discovery. In a real database screening experiment,however, the target structure is unknown. Success in arealistic database screen relies on the ability of theprogram to rank poses that correspond to more favor-able orientations of the ligand in the receptor as havingbetter interaction energy than incorrect poses. Ourresults indicate that many compounds in the testdatabase show a good correlation between scoring rankand accuracy of the pose when compounds are dockedwith AutoDock. A sector-shape plot is generated whenscoring rank is correlated with deviation from the targetstructure for the “rigid aromatic” class of ligands andeven for several aminoglycosides (1NEM, 1TOB, 1PBR,and 2TOB). In other words, favorably scoring poses tendto cluster nearer the target structure than higher-ranking poses. The performance of Dock is much lesssatisfactory; for a few cases only (1F1T, 1F27, and1FMN), there is a good correlation between ranking anddocking accuracy (see Supporting Information).

The tests of greatest importance for database dockingare reported in Figure 6. First, we established how wellthe program can discriminate between several relatedligands by asking the algorithm to identify the cognatecompound in a test database containing all other RNA-binding ligands present in the data set. This test is verydemanding, since most compounds in the data set tendto be basic molecules with some affinity for all RNA


molecules. Furthermore, aminoglycosides are known tobind a variety of RNA molecules with approximatelymicromolar affinity and there is every reason to expectthat at least some of them (if not all) will bind some ofthe RNAs as well as their cognate ligand, particularlyin the case of the weaker ligands 1AJU, 1KOC, and1KOD. It is also well-known that larger compounds tendto be docked more favorably by many database dockingprograms.22 To our surprise (Figure 6a), Dock was moresuccessful in identifying the cognate ligand among thetop scoring compounds for the “rigid aromatic” class ofligands. Perhaps AutoDock overemphasizes electrostaticinteractions, thereby allowing the basic aminoglycosidesto outscore the cognate ligand. Alternatively or inaddition, the genetic algorithm may allow aminoglyco-sides to find favorable docking poses, while the simplerapproach used by Dock may not.

Figure 6b reports the simulation of a database dock-ing experiment. We generated a test library by spikinga common set of 49 compounds with each of the 16 RNA-binding ligands and ranked the 50 compounds afterdocking them against the 16 target structures. Com-plexes were sorted as rigid aromatic compounds, weakbinders, and aminoglycosides for ease of comparison.The almost opposite pattern of success for the twoprograms is noteworthy; AutoDock performs best withaminoglycosides, and Dock performs best with “rigidplanar” compounds. As in other tests, Dock producedbetter results for ligands with hydrophobic ring systems.Its shape-based algorithm works better with hydropho-bic pockets, while shallow and wide binding sites areproblematic, as are large flexible molecules.

The final set of tests was aimed to assess whichcombination of docking and scoring would provide thehighest level of enrichment, in other words, the highestlikelihood of ranking the known RNA-binding com-pounds near the top of the database (Figure 7). This testessentially measures how useful in silico screening isin prefiltering large databases to identify potentialligands for the receptor of choice. The Dock energy score(column 2) locates a majority of ligands within the top10% of the database and performs considerably betterthan AutoDock (column 4). The combination of the Dockand AutoDock score does not significantly improve theenrichment level (column 5). However, using AutoDockto redock and rerank the compounds identified by Dockas the top ligands (top 10%) leads to the highest levelof enrichment (column 6).

The results reported in Figure 7 indicate that thehighest likelihood of identifying RNA-binding ligandsfrom database screens are obtained by using Dock toprefilter the initial library and then by redocking thetop ranking compounds using AutoDock. This approachprovides the highest likelihood of enriching RNA-binding molecules at or near the top of the ranking list.An analogous approach was demonstrated to worksuccessfully in a genuine database screening experimentconducted with HIV-1 TAR RNA.47,48,50 The effectivenessof the Dock algorithm in handling large database andof the docking/scoring of rigid druglike molecules willlikely identify a reduced library enriched with druglikeRNA-binding molecules. These preselected moleculeswill then be rescreened using AutoDock to reduce falsenegatives by ranking molecules through an independ-

ent, empirical scoring function (Figure 1). This approachhas several additional advantages. First is the speed ofDock. Second, the two algorithms and scoring functionsappear to perform best on different classes of com-pounds. Furthermore, they take advantage of the su-perior ability of AutoDock in finding poses close to thetarget structure (Figure 4) and in correlating high-scoring poses with orientations with low rmsd from thetarget (see Figure 1, Supporting Information).

In summary, we have examined the validity ofprotocols to automatically dock large databases againstRNA drug targets that optimally enriches the databasefor potential ligands. The results show that it is possibleto use automated docking tools developed for proteinsto increase the likelihood of discovering molecules thatbind to RNA in chemical databases. The protocol wehave tested uses docking tools that are widely availableand take advantage of the different characteristics ofthe two algorithms to optimally use their respectivestrengths. While large and flexible molecules are clearlyproblematic, the protocol works best with rigid planarmolecules that are of the greatest interest in realisticdrug discovery efforts.

Acknowledgment. We thank Dr. David Goodselland Dr. Garret Morris from Scripps Research Institutefor expert advice on AutoDock, Raelene Lawrence(Chemical Computing Group, Montreal) for many help-ful discussions on MOE, and Demetri Moustakas fromUCSF for help with Dock. We also thank Prof. Chris-tophe Verlinde (University of Washington) for criticalreading of the manuscript. The work was supported bya Royalty Research Fund grant (University of Wash-ington) and by a grant from NIH-NIAID.

Supporting Information Available: One figure withrank/rmsd correlation of docking results and one table withsolvation parameters for nucleic acids. This material is avail-able free of charge via the Internet at http://pubs.acs.org.

References(1) Bohacek, R. S.; McMartin, C.; Guida, W. C. The art and practice

of structure-based drug design: a molecular modeling perspec-tive. Med. Res. Rev. 1996, 16, 3-50.

(2) Taylor, R. D.; Jewsbury, P. J.; Essex, J. W. A review of protein-small molecule docking methods. J. Comput.-Aided Mol. Des.2002, 16, 151-166.

(3) Boehm, H.-J.; Klebe, G. What Can We Learn from MolecularRecognition in Protein-Ligand Complexes for the Design of NewDrugs? Angew. Chem., Int. Ed. Engl. 1996, 35, 2588-2614.

(4) Muegge, I.; Martin, Y. C.; Hajduk, P. J.; Fesik, S. W. Evaluationof PMF scoring in docking weak ligands to the FK506 bindingprotein. J. Med. Chem. 1999, 42, 2498-2503.

(5) Bartolucci, C.; Perola, E.; Pilger, C.; Fels, G.; Lamba, D. Three-dimensional Structure of a Complex of Galanthamine (Ni-valin(R)) with Acetylcholinesterase from Torpedo californica:Implications for the Design of New Anti-Alzheimer Drugs.Proteins 2001, 42, 182-191.

(6) Bohm, H.-J.; Boehringer, M.; Bur, D.; Gmuender, H.; Huber, W.;et al. Novel Inhibitors of DNA Gyrase: 3D Structure BasedBiased Needle Screening, Hit Validation by Biophysical Meth-ods, and 3D Guided Optimization. A Promising Alternative toRandom Screening. J. Med. Chem. 2000, 43, 2664-2674.

(7) Constantino, G.; Macchiarulo, A.; Camainoni, E.; Pelliciari, R.Modeling of Poly(ADP-ribose)polymerase (PARP) Inhibitors.Docking of Ligands and Quantitative Structure-Activity Rela-tionship Analysis. J. Med. Chem. 2001, 44, 3786-3794.

(8) DesJarlais, R. L.; Dixon, J. S. A shape- and chemistry-baseddocking method and its use in the design of HIV-1 proteaseinhibitors. J. Comput.-Aided Mol. Des. 1994, 8, 231-242.

(9) Mahmoudian, M. The cannbinoid receptor: Computer-aidedmolecular modelling and docking of ligand. J. Mol. GraphicsModell. 1997, 15, 149-153.


(10) Penzotti, J. E.; Lamb, M. L.; Evensen, E.; Grootenhuis, P. D. Acomputational ensemble pharmacophore model for identifyingsubstrates of P-glycoprotein. J. Med. Chem. 2002, 45, 1737-1740.

(11) van Dongen, M. J.; Uppenberg, J.; Svensson, S.; Lundback, T.;Akerud, T.; et al. Structure-based screening as applied to humanFABP4: a highly efficient alternative to HTS for hit generation.J. Am. Chem. Soc. 2002, 124, 11874-11880.

(12) Hajduk, P. J.; Gerfin, T.; Boehlen, J. M.; Haberli, M.; Marek,D.; et al. High-throughput nuclear magnetic resonance-basedscreening. J. Med. Chem. 1999, 42, 2315-2317.

(13) Hajduk, P. J.; Meadows, R. P.; Fesik, S. W. Discovering high-affinity ligands for proteins. Science 1997, 278, 497, 499.

(14) Hajduk, P. J.; Burns, D. J. Integration of NMR and high-throughput screening. Comb. Chem. High Throughput Screening2002, 5, 613-621.

(15) Hajduk, P. J.; Betz, S. F.; Mack, J.; Ruan, X.; Towne, D. L.; etal. A strategy for high-throughput assay development usingleads derived from nuclear magnetic resonance-based screening.J. Biomol. Screening 2002, 7, 429-432.

(16) Hajduk, P. J.; Meadows, R. P.; Fesik, S. W. NMR-based screeningin drug discovery. Q. Rev. Biophys. 1999, 32, 211-240.

(17) Muchmore, S. W.; Hajduk, P. J. Crystallography, NMR andvirtual screening: integrated tools for drug discovery. Curr.Opin. Drug Discovery Dev. 2003, 6, 544-549.

(18) Burkhard, P.; Hommel, U.; Sanner, M.; Walkinshaw, M. D. TheDiscovery of Steroids and Other Novel FKBP Inhibitors Usinga Molecular Docking Program. J. Mol. Biol. 1999, 287, 853-858.

(19) Burkhard, P.; Taylor, P.; Walkinshaw, M. D. An Example of aProtein Ligand Found by Database Mining: Description of theDocking Method and Its Verification by a 2.3 Å X-ray Structureof a Thrombin-Ligand Complex. J. Mol. Biol. 1998, 277, 449-466.

(20) Boyd, D. B. Success of Computer-Assisted Molecular Design.Reviews in Computational Chemistry; Wiley-VCH, John Wileyand Sons: New York, 1991; pp 355-371.

(21) Varady, J.; Wu, X.; Fang, X.; Min, J.; Hu, Z.; et al. MolecularModeling of the Three-Dimensional Structure of Dopamine 3(D(3)) Subtype Receptor: Discovery of Novel and Potent D(3)Ligands through a Hybrid Pharmacophore- and Structure-BasedDatabase Searching Approach. J. Med. Chem. 2003, 46, 4377-4392.

(22) Bissantz, C.; Folkers, G.; Rognan, D. Protein-Based VirtualScreening of Chemical Databases. 1. Evaluation of DifferentDocking/Scoring Combinations. J. Med. Chem. 2000, 43, 4759-4767.

(23) Baxter, C. A.; Murray, C. W.; Waszkowycz, B.; Li, J.; Sykes, R.A.; et al. New Approach to Molecular Docking and Its Applicationto Virtual Screening of Chemical Databases. J. Chem. Inf.Comput. Sci. 2000, 40, 254-262.

(24) Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoringfunctions for molecular docking. J. Med. Chem. 2003, 46, 2287-2303.

(25) Ecker, D. J.; Griffey, R. H. RNA as a small-molecule drugtarget: doubling the value of genomics. Drug Discovery Today1999, 4, 420-429.

(26) Hermann, T.; Westhof, E. RNA as a drug target: chemical,modelling, and evolutionary tools. Curr. Opin. Biotechnol. 1998,9, 66-73.

(27) Michael, K.; Tor, Y. Designing Novel RNA Binders. Chemistry1998, 4, 2091-2098.

(28) Pearson, N. D.; Prescott, C. D. RNA as a drug target. Chem.Biol. 1997, 4, 409-414.

(29) Fourmy, D.; Recht, M. I.; Puglisi, J. D. Binding of Neomycin-class Aminoglycoside Antibiotics to the A-side of 16 S rRNA. J.Mol. Biol. 1998, 277, 347-362.

(30) Jiang, L.; Majumdar, A.; Hu, W.; Jaishree, T. J.; Xu, W.; et al.Saccharide-RNA recognition in a complex formed betweenneomycin B and an RNA aptamer. Structure 1999, 7, 817-827.

(31) Botto, R. E.; Coxon, B. Nitrogen-15 Nuclear Magnetic ResonanceSpectroscopy of Neomycin B and Related Aminoglycosides. J.Am. Chem. Soc. 1983, 105, 1021-1028.

(32) Jiang, L.; Patel, D. J. Solution structure of the tobramycin-RNAaptamer complex. Nat. Struct. Biol. 1998, 5, 769-774.

(33) Tor, Y. RNA and the Small Molecule World. Angew. Chem., Int.Ed. 1999, 38, 1579-1582.

(34) Hermann, T. Strategies for the Design of Drugs Targeting RNA-and RNA-Protein Complexes. Angew. Chem., Int. Ed. 2000, 39,1890-1905.

(35) Hermann, T.; Westhof, E. Saccharide-RNA Recognition. Biopoly-mers 1998, 48, 155-165.

(36) Hermann, T.; Westhof, E. Aminoglycoside Binding to the Ham-merhead Ribozyme: A General Model for the Interaction ofCationic Antibiotics with RNA. J. Mol. Biol. 1998, 276, 903-912.

(37) Hermann, T.; Westhof, E. Docking of Cationic Antibiotics toNegatively Charged Pockets in RNA Folds. J. Med. Chem. 1999,42, 1250-1261.

(38) Schluenzen, F.; Zarivach, R.; Harms, J.; Bashan, A.; Tocilj, A.;et al. Structural basis for the interaction of antibiotics with thepeptidyl transferase centre in eubacteria. Nature 2001, 413,814-821.

(39) Matassova, N. B.; Rodnina, M. V.; Endermann, R.; Kroll, H. P.;Pleiss, U.; et al. Ribosomal RNA is the target for oxazolidinones,a novel class of translational inhibitors. RNA 1999, 5, 939-946.

(40) Bhavnani, S. M.; Ballow, C. H. New Agents for Gram-PositiveBacteria. Curr. Opin. Microbiol. 2001, 3, 528-534.

(41) Drysdale, M. J.; Lentzen, G.; Matassova, N.; Murchie, A. I.;Aboul-Ela, F.; et al. RNA as a drug target. Prog. Med. Chem.2002, 39, 73-119.

(42) Xavier, K. A.; Eder, P. S.; Giordano, T. RNA as a drug target:methods for biophysical characterization and screening. TrendsBiotechnol. 2000, 18, 349-356.

(43) Wallis, M. G.; Schroeder, R. The Binding of Antibiotics to RNA.Prog. Biophys. Mol. Biol. 1997, 67, 141-154.

(44) Gallego, J.; Varani, G. Targeting RNA with Small-MoleculeDrugs: Therapeutic Promise and Chemical Challenges. Acc.Chem. Res. 2001, 34, 836-843.

(45) Afshar, M.; Prescott, C. D.; Varani, G. Structure-based andcombinatorial search for new RNA-binding drugs. Curr. Opin.Biotechnol. 1999, 10, 59-63.

(46) Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J.Experimental and computational approaches to estimate solubil-ity and permeability in drug discovery and development settings.Adv. Drug Delivery Rev. 1997, 23, 3-25.

(47) Du, Z.; Lind, K. E.; James, T. L. Structure of TAR RNAcomplexed with a Tat-TAR interaction nanomolar inhibitor thatwas identified by computational screening. Chem. Biol. 2002,9, 707-712.

(48) Filikov, A. V.; Mohan, V.; Vickers, T. A.; Griffey, R. H.; Cook, P.D.; et al. Identification of ligands for RNA targets via structure-based virtual screening: HIV-1 TAR. J. Comput.-Aided Mol. Des.2000, 14, 593-610.

(49) James, T. L.; Lind, K. E.; Filikov, A. V.; Mujeeb, A. Three-Dimensional RNA Strucutre-Based Drug Discovery. J. Biomol.Struct. Dyn. 1999, 11, 201-205.

(50) Lind, K. E.; Du, Z.; Fujinaga, K.; Peterlin, B. M.; James, T. L.Structure-based computational database screening, in vitroassay, and NMR assessment of compounds that target TARRNA. Chem. Biol. 2002, 9, 185-193.

(51) Davis, B.; Afshar, M.; Varani, G.; Murchie, A. I.; Karn, J.; et al.Rational design of inhibitors of HIV-1 TAR RNA through thestabilisation of electrostatic “hot spots”. J. Mol. Biol. 2004, 336,343-356.

(52) Chen, Q.; Shafer, R. H.; Kuntz, I. D. Structure-Based Discoveryof Ligands Targeted to the RNA Double Helix. Biochemistry1997, 36, 11402-11407.

(53) Yu, L.; Oost, T. K.; Schkeryantz, J. M.; Yang, J.; Janowick, D.;et al. Discovery of aminoglycoside mimetics by NMR-basedscreening of Escherichia coli A-site RNA. J. Am. Chem. Soc.2003, 125, 4444-4450.

(54) Swayze, E. E.; Jefferson, E. A.; Sannes-Lowery, K. A.; Blyn, L.B.; Risen, L. M.; et al. SAR by MS: a ligand based technique fordrug lead discovery against structured RNA targets. J. Med.Chem. 2002, 45, 3816-3819.

(55) Westhof, E.; Fritsch, V. RNA folding: beyond Watson-Crickpairs. Struct. Fold Des. 2000, 8, R55-65.

(56) Varani, G. RNA-Protein Intermolecular Recognition. Acc. Chem.Res. 1997, 30, 189-195.

(57) Varani, G.; Nagai, K. RNA Recognition by RNP Proteins duringRNA Processing. Annu. Rev. Biomol. Struct. 1998, 27, 407-445.

(58) Varani, L.; Spillantini, M. G.; Goedert, M.; Varani, G. Structuralbasis for recognition of the RNA major groove in the tau exon10 splicing regulatory element by aminoglycoside antibiotics.Nucleic Acids Res. 2000, 28, 710-719.

(59) Schuster, P.; Stadler, P. F.; Renner, A. RNA structures andfolding: from conventional to new issues in structure predictions.Curr. Opin. Struct. Biol. 1997, 7, 229-235.

(60) Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart,W. E.; et al. Automated Docking Using a Lamarckian GeneticAlgorithm and an Empirical Binding Free Energy Funcition. J.Comput. Chem. 1998, 19, 1639-1662.

(61) Ewing, T. J. A.; Makino, S.; Skillman, A. G.; Kuntz, I. D. DOCK4.0: Search strategies for automated molecular docking offlexible molecule databases. J. Comput.-Aided Mol. Des. 2001,15, 411-428.

(62) Cornell, W. D.; Ciepak, P.; Bayly, C. I.; Gould, I. R.; Merz, J.,Kenneth, M.; et al. A Second Generation Force Field for theSimulation of Proteins, Nucleic Acids, and Organic Molecules.J. Am. Chem. Soc. 1995, 117, 5179-5197.

(63) Gasteiger, J.; Marsili, M. A New Model for Calculating AtomicCharges in Molecules. Tetrahedron Lett. 1978, 34, 3181-3184.


(64) Gasteiger, J.; Marsili, M. Iterative Partial Equalization of OrbitalElectronegativitysA Rapid Access to Atomic Charges. Tetrahe-dron 1980, 36, 3219-3288.

(65) Muegge, I.; Rarey, M. Small Molecule Docking and Scoring.Reviews in Computational Chemistry; Wiley-VCH: New York,2001; pp 1-60.

(66) Schmitz, U.; James, T. L. How to generate accurate solutionstructures of double-helical nucleic acid fragments using nuclearmagnetic resonance and restrained molecular dynamics. Meth-ods Enzymol. 1995, 261, 3-44.

(67) Mujeeb, A.; Kerwin, S. M.; Kenyon, G. L.; James, T. L. Solutionstructure of a conserved DNA sequence from the HIV-1 ge-nome: restrained molecular dynamics simulation with distanceand torsion angle restraints derived from two-dimensional NMRspectra. Biochemistry 1993, 32, 13419-13431.

(68) Kontoyianni, M.; McClellan, L. M.; Sokol, G. S. Evaluation ofdocking performance: comparative data on docking algorithms.J. Med. Chem. 2004, 47, 558-565.

(69) Erickson, J. A.; Jalaie, M.; Robertson, D. H.; Lewis, R. A.; Vieth,M. Lessons in molecular recognition: the effects of ligand andprotein flexibility on molecular docking accuracy. J. Med. Chem.2004, 47, 45-55.

(70) Connolly, M. L. Solvent-Accessible Surface of Proteins andNucleic Acids. Science 1983, 221, 709-713.

(71) Connolly, M. L. Analytical Molecular Surface Calculation. J.Appl. Crystallogr. 1983, 16, 548-558.

(72) DesJarlais, R. L.; Sheridan, R. P.; Dixon, J. S.; Kuntz, I. D.;Venkataraghavan, R. Docking flexible ligands to macromolecularreceptors by molecular shape. J. Med. Chem. 1986, 29, 2149-2153.

(73) Halperin, I.; Ma, B.; Wolfson, H.; Nussinov, R. Principles ofdocking: An overview of search algorithms and a guide to scoringfunctions. Proteins 2002, 47, 409-443.

(74) Abagyan, R.; Totrov, M. High-throughput docking for leadgeneration. Curr. Opin. Chem. Biol. 2001, 5, 375-382.

(75) Clark, R. D.; Strizhev, A.; Leonard, J. M.; Blake, J. F.; Matthew,J. B. Consensus scoring for ligand/protein interactions. J. Mol.Graphics Modell. 2002, 20, 281-295.

(76) Gohlke, H.; Hendlich, M.; KLebe, G. Knowledge-based ScoringFunction To Predict Protein-Ligand Interactions. J. Mol. Biol.2000, 295, 337-356.

(77) Kuntz, I. D.; Blaney, J. M.; Oatley, S. J.; Langridge, R.; Ferrin,T. E. A geometric approach to macromolecule-ligand interac-tions. J. Mol. Biol. 1982, 161, 269-288.

(78) Kuntz, I. D. Structure-Based Strategies for Drug Design andDiscovery. Science 1992, 257, 1078-1082.

(79) Kuntz, I. D.; Meng, E. C.; Shoichet, B. K. Structure-BasedMolecular Design. Acc. Chem. Res. 1994, 27, 117-123.

(80) Ewing, T. J. A.; Kuntz, I. D. Critical Evaluation of SearchAlgorithms for Automated Molecular Docking and DatabaseScreening. J. Comput. Chem. 1997, 18, 1175-1189.

(81) Weiner, S. J.; Kollmann, P. A.; Nguyen, D. T.; Case, D. A. AnAll Atom Force Field for Simulations of Proteins and NucleicAcids. J. Comput. Chem. 1986, 7, 230-252.

(82) Weiner, S. J.; Kollmann, P. A.; Case, D. A.; Singh, U. C.; Ghio,C.; et al. A New Force Field for Molecular Mechanical Simulationof Nucleic Acids and Proteins. J. Am. Chem. Soc. 1984, 106,765-784.

(83) Goodsell, D. S.; Olson, A. J. Automated docking of substrates toproteins by simulated annealing. Proteins 1990, 8, 195-202.

(84) Solis, F. J.; Wets, R. J.-B. Minimization by random searchtechniques. Math. Oper. Res. 1981, 6, 19-30.

(85) Morris, G. M.; Goodsell, D. S.; Huey, R.; Hart, W. E.; Halliday,S.; et al. AutoDock User’s Guide. Automated Docking of FlexibleLigands to Receptors; Molecular Graphics Laboratory, TheScripps Research Institute: La Jolla, CA, 1998.

(86) Mehler, E. L.; Solmajer, T. Electrostatic effects in proteins:comparison of dielectric and charges models. Protein Eng. 1991,4, 903-910.

(87) Solmajer, T.; Mehler, E. L. Electrostatic screening in moleculardynamics simulations. Protein Eng. 1991, 4, 911-917.

(88) Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A Fast FlexibleDocking Method Using an Incremental Construction Algorithm.J. Mol. Biol. 1996, 261, 470-489.

(89) Kramer, B.; Rarey, M.; Lengauer, T. Evaluation of the FLEXXincremental construction algorithm for protein-ligand docking.Proteins 1999, 37, 228-241.

(90) Rarey, M.; Lengauer, T. A recursive algorithm for efficientcombinatorial library docking. Perspect. Drug Discovery Des.2000, 20, 63-81.

(91) Goodsell, D. S. Personal communication.(92) Wagner, S. F. Introduction to Statistics; HarperCollins College

Outline; HarperPerennial: New York, 1992; p 384.(93) Charifson, P. S.; Corkery, J. J.; Murcko, M. A.; Walters, P. W.

Consensus Scoring: A Method for Obtaining Improved Hit Ratesfrom Docking Databases of Three-Dimensional Structures intoProteins. J. Med. Chem. 1999, 42, 5100-5109.

(94) Wang, R.; Wang, S. How Does Consensus Scoring Work forVirtual Library Screening? An Idealized Computer Experiment.J. Chem. Inf. Comput. Sci. 2001, 41, 1422-1426.

(95) Oshiro, C. M. Dock User Manual; UCSF: San Francisco, 1998.(96) Morris, G. M.; Goodsell, D. S.; Huey, R.; Olson, A. J. Distributed

automated docking of flexible ligands to proteins: Parallelapplications of AutoDock 2.4. J. Comput.-Aided Mol. Des. 1996,10, 293-304.

(97) Eldridge, M. D.; Murray, C. W.; Auton, T. R.; Paolini, G. V.; Mee,R. P. Empirical scoring functions: I. The development of a fastempirical scoring function to estimate the binding affinity ofligands in receptor complexes. J. Comput.-Aided Mol. Des. 1997,11, 425-445.

(98) Gohlke, H.; Klebe, G. DrugScore meets CoMFA: adaptation offields for molecular comparison (AFMoC) or how to tailorknowledge-based pair-potentials to a particular protein. J. Med.Chem. 2002, 45, 4153-4170.

(99) Sotriffer, C. A.; Gohlke, H.; Klebe, G. Docking into knowledge-based potential fields: a comparative evaluation of DrugScore.J. Med. Chem. 2002, 45, 1967-1970.

(100) Wang, R.; Lai, L.; Wang, S. Further development and validationof empirical scoring functions for structure-based binding affinityprediction. J. Comput.-Aided Mol. Des. 2002, 16, 11-26.

(101) Rooman, M.; Lievin, J.; Buisine, E.; Wintjens, R. Cation-pi/H-bond Stair Motifs at Protein-DNA Interfaces. J. Mol. Biol. 2002,319, 67-76.

(102) Sponer, J.; Leszczynski, J.; Hobza, P. Electronic properties,hydrogen bonding, stacking, and cation binding of DNA and RNAbases. Biopolymers 2001, 61, 3-31.

(103) Magnuson, E. C.; Koehler, J. A.; Lamm, G.; Pack, G. R.Mg(H2O)6

2+-π (Cytosine) Interactions in a DNA Dodecamer. Int.J. Quantum Chem. 2002, 88, 236-243.

(104) Muegge, I.; Heald, S. L.; Brittelli, D. Simple Selection Criteriafor Drug-like Chemical Matter. J. Med. Chem. 2001, 44, 1841-1846.

(105) Bemis, G. W.; Murcko, M. A. The Properties of Known Drugs.1. Molecular Frameworks. J. Med. Chem. 1996, 39, 2887-2893.

(106) Muegge, I. Pharmacophore features of potential drugs. Chemistry2002, 8, 1976-1981.

(107) Tao, P.; Lai, L. Protein ligand docking based on empirical methodfor binding affinity estimation. J. Comput.-Aided Mol. Des 2001,15, 429-446.

(108) Jiang, F.; Kumar, R. A.; Jones, R. A.; Patel, D. J. Structural basisof RNA folding and recognition in an AMP-RNA aptamercomplex. Nature 1996, 382, 183-186.

(109) Zimmermann, G. R.; Jenison, R. D.; Wick, C. L.; Simorre, J. P.;Pardi, A. Interlocking structural motifs mediate moleculardiscrimination by a theophylline-binding RNA. Nat. Struct. Biol.1997, 4, 644-649.

(110) Baugh, C.; Grate, D.; Wilson, C. 2.8 Å crystal structure of themalachite green aptamer. J. Mol. Biol. 2000, 301, 117-128.

(111) Nix, J.; Sussman, D.; Wilson, C. The 1.3 Å crystal structure ofa biotin-binding pseudoknot and the basis for RNA molecularrecognition. J. Mol. Biol. 2000, 296, 1235-1244.

(112) Fan, P.; Suri, A. K.; Fiala, R.; Live, D.; Patel, D. J. Molecularrecognition in the FMN-RNA aptamer complex. J. Mol. Biol.1996, 258, 480-500.

(113) Brodsky, A. S.; Williamson, J. R. Solution structure of the HIV-2TAR-argininamide complex. J. Mol. Biol. 1997, 267, 624-639.

(114) Yang, Y.; Kochoyan, M.; Burgstaller, P.; Westhof, E.; Famoluk,M. Strucutral Basis of Ligand Discrimination by Two RelatedRNA Aptamers Resolved by NMR Spectroscopy. Science 1996,272, 1343-1347.

(115) Yoshizawa, S.; Fourmy, D.; Puglisi, J. D. Structural originsof gentamicin antibiotic action. EMBO J. 1998, 17, 6437-6448.

(116) Fourmy, D.; Recht, M. I.; Blanchard, S. C.; Puglisi, J. D.Structure of the A site of Escherichia coli 16S ribosomal RNAcomplexed with an aminoglycoside antibiotic. Science 1996, 274,1367-1371.

(117) Faber, C.; Sticht, H.; Schweimer, K.; Rosch, P. Structuralrearrangements of HIV-1 Tat-responsive RNA upon binding ofneomycin B. J. Biol. Chem. 2000, 275, 20660-20666.

(118) Jiang, L.; Suri, A. K.; Fiala, R.; Patel, D. J. Saccharide-RNArecognition in an aminoglycoside antibiotic-RNA aptamercomplex. Chem. Biol. 1997, 4, 35-50.

(119) Carter, A. P.; Clemons, W. M.; Brodersen, D. E.; Morgan-Warren,R. J.; Wimberly, B. T.; et al. Functional insights from thestructure of the 30S ribosomal subunit and its interactions withantibiotics. Nature 2000, 407, 340-348.

(120) Brodersen, D. E.; Clemons, W. M., Jr.; Carter, A. P.; Morgan-Warren, R. J.; Wimberly, B. T.; et al. The structural basis forthe action of the antibiotics tetracycline, pactamycin, andhygromycin B on the 30S ribosomal subunit. Cell 2000, 103,1143-1154.


(121) Vicens, Q.; Westhof, E. Crystal structure of paromomycin dockedinto the eubacterial ribosomal decoding A site. Structure (Lon-don) 2001, 9, 647-658.

(122) Hansen, J. L.; Ippolito, J. A.; Ban, N.; Nissen, P.; Moore, P. B.;et al. The structures of four macrolide antibiotics bound to thelarge ribosomal subunit. Mol. Cell 2002, 10, 117-128.

(123) Vicens, Q.; Westhof, E. Crystal structure of a complex betweenthe aminoglycoside tobramycin and an oligonucleotide containingthe ribosomal decoding a site. Chem. Biol. 2002, 9, 747-755.

(124) Hansen, J. L.; Schmeing, T. M.; Moore, P. B.; Steitz, T. A.Structural insights into peptide bond formation. Proc. Natl.Acad. Sci. U.S.A. 2002, 99, 11670-11675.

(125) Vicens, Q.; Westhof, E. Crystal structure of geneticin bound toa bacterial 16S ribosomal RNA A site oligonucleotide. J. Mol.Biol. 2003, 326, 1175-1188.

(126) Bashan, A.; Agmon, I.; Zarivach, R.; Schluenzen, F.; Harms, J.;et al. Structural basis of the ribosomal machinery for peptidebond formation, translocation, and nascent chain progression.Mol. Cell 2003, 11, 91-102.

(127) Schlunzen, F.; Harms, J. M.; Franceschi, F.; Hansen, H. A.;Bartels, H.; et al. Structural basis for the antibiotic activity ofketolides and azalides. Structure (London) 2003, 11, 329-338.

(128) Berisio, R.; Schluenzen, F.; Harms, J.; Bashan, A.; Auerbach,T.; et al. Structural insight into the role of the ribosomal tunnelin cellular regulation. Nat. Struct. Biol. 2003, 10, 366-370.

(129) Kraulis, P. J. MOLSCRIPT: a program to produce both detailedand schematic plots of protein structures. J. Appl. Crystallogr.1991, 24, 946-950.

(130) Merritt, E. A.; Bacon, D. J. Raster3D: Photorealistic MolecularGraphics. Methods Enzymol. 1997, 277, 505-524.

(131) Merritt, E. A.; Murphy, M. E. P. Raster3D Version 2.0: AProgram for Photorealistic Molecular Graphics. Acta Crystallogr.1994, D50, 869-873.

(132) Bacon, D. J.; Anderson, W. F. A Fast Algorithm for RenderingSpace-Filling Molecule Pictures. J. Mol. Graphics 1988, 6,219-220.

JM030650O


Documents

Validation of Automated Docking Programs for Docking and Database Screening against RNA Drug Targets