Protein-Ligand Docking in Drug Design: Performance Assessment and Binding-Pose Selection

  • Flavio BallanteEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1824)


Main goal in drug discovery is the identification of drug-like compounds capable to modulate specific biological targets. Thus, the prediction of reliable binding poses of candidate ligands, through molecular docking simulations, represents a key step to be pursued in structure-based drug design (SBDD). Since the increasing number of resolved three-dimensional ligand-protein structures, together with the expansion of computational power and software development, the comprehensive and systematic use of experimental data can be proficiently employed to validate the docking performance. This allows to select and refine the protocol to adopt when predicting the binding pose of trial compounds in a target. Given the availability of multiple docking software, a comparative docking assessment in an early research stage represents a must-use step to minimize fails in molecular modeling. This chapter describes how to perform a docking assessment, using freely available tools, in a semiautomated fashion.

Key words

Drug design Drug discovery Molecular docking Molecular modeling Docking assessment Structure-based drug design (SBDD) 

1 Introduction

Molecular recognition (generally referred as the non-covalent interactions between two or more molecules) is a key event in many biological systems, and its optimization represents one of the most challenging problems in drug discovery when targeting a certain protein by using small molecules [1]. Prediction or analysis of ligand-protein interactions can be performed with different computer-aided drug design (CADD) tools, generally classified as ligand-based (LB, which depend on the information of diverse molecules that bind to the biological target) and structure-based (SB, which rely on knowledge of three-dimensional structural information from biological targets) methods [2]. Among others, molecular docking is one of the most used structure-based drug design (SBDD) tools in medicinal chemistry to predict ligands’ binding pose in a protein, allowing to evaluate molecular interactions, induced conformational changes, and binding energetics, as well as to perform virtual screening applications [3]. In addition, docked poses can be proficiently used in classic (ligand-based) [4] or per-residue (structure-based) [5] three-dimensional quantitative structure-activity relationship (3D QSAR) studies [6, 7]. When ligand-protein structural data from experimental methods (such as X-ray crystallography or NMR spectroscopy) are available, assessment of the docking protocol is required to estimate the reliability of the designed procedure in predicting compounds’ binding pose without experimental information, for a selected target. Different strategies can be adopted to evaluate a docking procedure: docking accuracy (DA) calculation [8], enrichment factor (EF) analysis [9], correlation between experimental and predicted binding affinities [10], and distance between a metal ion in the active site (if present) and the ligands’ metal-binding moieties [11], among others.

This chapter describes how to benchmark and evaluate a docking method by:
  1. 1.

    Performing re-docking (or self-docking) and cross-docking (or ensemble docking) simulations

  2. 2.

    Computing the relative docking accuracies based on root-mean square deviation (RMSD) values between the predicted (docked) and the experimental binding poses

The protocol is characterized by four steps:
  1. 1.

    Setup of working directories

  2. 2.

    Preparation of the structures and input files

  3. 3.

    Docking simulation, cluster analysis, and DA calculation

  4. 4.

    Analysis of the results


UCSF Chimera [12], LigandBox, and MGLTools [13] are used to perform step 2, AutoDock Vina [14] to perform molecular docking on a small set of human coagulation factor Xa (FXa) inhibitor complexes [15, 16, 17, 18, 19], and Clusterizer-DockAccessor [20] to perform cluster analysis and DA calculation (step 3). The whole protocol is intended to run on a Linux environment: a series of basic shell commands to be written in the Linux terminal are provided, step-by-step, to semiautomate the process (see Note 1).

2 Materials

  1. 1.

    AutoDock Vina (Version 1.1.2). Download AutoDock Vina [14] (for Linux, version 1.1.2) from For installation instructions, see (see Note 2).

  2. 2.

    AutoDockTools (version 1.5.6). Go to the “MGLTools Web Portal” website (, and download MGLTools [13] (MGLTools version 1.5.6; 32 or 64 bit, according to the Linux system in use). Install following the instructions available from (see Note 3).

  3. 3.

    Clusterizer-DockAccessor (Version 1.1). Download Clusterizer-DockAccessor [20] from Select “Clusterizer-DockAccessor 1.1 Software,” fill the registration form, and click “Send.” An e-mail will be sent reporting a link for downloading the software. To install the software, follow the instructions reported in the user manual available from (see Note 4).

  4. 4.

    DOCK 6 (version 6.8). Request DOCK 6 [21] license from the UCSF DOCK website ( An e-mail will be sent when the DOCK 6 license is accepted. Follow the instructions provided in the received e-mail to download the last release of DOCK 6. Follow the installation instructions available from the DOCK 6 manual (, see Note 5).

  5. 5.

    LigandBox. Download LigandBox from; fill the registration form and click “Send.” An e-mail will be sent reporting a link for downloading the software. Follow the installation instructions available from (see Note 6).

  6. 6.

    Open Babel (Version 2.4.1). Download Open Babel [22, 23] (stable release for Linux), and follow the installation instructions (, see Note 7).

  7. 7.

    UCSF Chimera . Download the latest Linux release of UCSF Chimera [12] (32 or 64 bit, according to the Linux system in use) from Click the relative “Instructions” link from the same web page for installation instructions (see Note 8).

  8. 8.

    Human Coagulation Factor Xa (FXa) PDB Files. 6 FXa co-crystal structures (PDB codes: 1EZQ, 1F0S, 1XKA, 2BOK, 2CJI, and 2FZZ) have been selected for this exercise (see Note 9). The complexes can be downloaded from the Protein Data Bank (PDB) ( [24]. A convenient way to retrieve the structures is through the Linux command line interface (terminal). Open the Linux terminal. Type into the terminal the code shown in Table 1 to create a parent directory (“FXA”) containing a child folder (“00_PDB”), and download the PDB files in there.

Table 1

Command line sequence 1: retrieve structures from PDB

3 Methods

The protocol is characterized by four steps (Fig. 1):
  1. 1.

    Set the working directories to store the input/output files.

  2. 2.

    Prepare the PDB structures and input files for molecular docking: PDB structures are “cleaned” from solvent molecules and non-interacting ions and then superimposed and protonated at physiological pH (using UCSF Chimera); unwanted lines from the PDB files are also removed (using Linux shell command lines). From each cleaned PDB complex, the relative protein (“lock”) and ligand (“key”) structures are extracted; then randomized keys’ conformations are derived (through Open Babel) to perform the subsequent “random conformation docking simulations” (see Note 10). The obtained PDBs are then converted into PDBQT format (as input files for AutoDock Vina) using Python scripts available from MGLTools. Afterward, the grid box center coordinates and dimensions are computed using LigandBox and placed into the AutoDock Vina configuration file (see Note 9).

  3. 3.

    Experimental/random conformation re-docking and cross-docking simulations (ECRD, RCRD, ECCD, and RCCD, respectively) through AutoDock Vina, followed by cluster analysis and DA calculation using Clusterizer-DockAccessor.

  4. 4.

    Analysis of the results.

    Before starting the protocol, it is advisable to test the system (see Notes 28).

Fig. 1

Top, required software; bottom, schematized procedure. The protocol is characterized by four main steps for setting the working directories (step 1); preparing the docking input files (step 2); running the experimental conformation re-docking (ECRD), random conformation re-docking (RCRD), experimental conformation cross-docking (ECCD), and random conformation cross-docking (RCCD) simulations (step 3); and assessing the docking performance (step 4). aExp Experimental, bRC randomized starting conformer

3.1 Setting the Working Directories

A series of directories will now be set to store input/output files during the protocol (run the code shown in Table 2).
Table 2

Command line sequence 2: set working directories

3.2 Preparing PDB Structures and Input Files

3.2.1 Preparing the PDB Complexes

To prepare the PDB complexes (see Note 11), launch UCSF Chimera from the “00_PDB” folder (Table 3).
Table 3

Command line sequence 3: launch UCSF Chimera from “00_PDB ”

All the downloaded complexes are now opened in UCSF Chimera and shown in the main graphic window (see Fig. 2, left). Click Favorites ➔ Command Line to start the command line tool; then click Favorites ➔ Model Panel to list all the models loaded in the Chimera session (see Fig. 2, right).
  1. (a)

    Removing Unnecessary Chains

Fig. 2

UCSF Chimera. Left, graphic window (molecular structures are shown and the command line is turned on); right, “Model Panel”

Unnecessary chains in the PDBs can be removed from the native structure (see Note 11). Write “split” into the Chimera command line to partition each model into separate sub-models; the Model Panel will show all the different models according to the chain (see Fig. 3, left). In this exercise, six models (see below) can be removed from the Chimera session since secondary chains are unnecessary for the docking simulation. Go to the Model Panel, hold Ctrl key and click “1EZQ.pdb B,” “1F0S.pdb B,” “1XKA.pdb L,” “2BOK.pdb L,” “2CJI.pdb B,” and “2FZZ.pdb L” to select the models to be removed, then release the Ctrl key (selected models are highlighted as shown in Fig. 3, left), and click the “close” button placed on the right side panel (the one enclosed in the red box in Fig. 3, left) to remove the selected models from the Chimera session. Now, only proteins’ chains comprising the active site are listed (see Fig. 3, right).
  1. (b)

    Removing Solvent and Non-interacting Ions

Fig. 3

UCSF Chimera: “Model Panel.” Left, selected models to be removed; right, models to maintain in the session

Solvent molecules and non-interacting ions will be now removed (see Note 11).

Click Select ➔ Residue to show standard amino acids, ligands (“4PP,” “5QC,” “784,” “GSK,” “PR2,” “RPR”), solvent molecules (“HOH”), and ions (“CA” and “NA”) codes characterizing the models (see Fig. 4, see Note 11).
Fig. 4

UCSF Chimera: “Residue” window

In the empty Chimera command line write “select #:HOH,CA,NA” and then press enter (or return); H2O molecules, Ca2+, and Na+ ions from all the loaded models are now selected (green highlighted from the graphic window). While the selection is active, write in the empty command line “delete selection,” and then press enter (or return). Water molecules, Ca2+, and Na+ ions are now removed from the session.
  1. (c)

    Complex Alignment


Complex structures can be superimposed upon each other using the structure with the highest resolution and no gaps as reference.

In Chimera, click Tools ➔ Structure Comparison ➔ MatchMaker to open the MatchMaker tool. As “Reference structure” (left panel of the dialog window), select “2CJI.pdb A” (characterized by the highest resolution, 2.1 Å, and without missing non-terminal residues), and as “Structure(s) to match” (right panel of the dialog window), select all the structures except “2CJI.pdb A” (as depicted in Fig. 5). Click OK to start MatchMaker.
Fig. 5

UCSF Chimera: “MatchMaker” dialog box

Complexes are now superimposed (Fig. 6).
  1. (d)

    Structure Protonation

Fig. 6

Superimposed complexes

Complexes can now be protonated, through Chimera, considering the physiological pH (pH 7.4, see Note 12). Click Tools ➔ Structure Editing ➔ AddH (check that “Consider each model in isolation from all others,” “also consider H-bonds,” and “Residue-name-based” options are selected) ➔ Click OK. Structures are now protonated (see Note 13).
  1. (e)

    Renaming and Saving the PDB Structures

Models need to be renamed: click Favorites ➔ Model Panel ➔ 1EZQ.pdb A, and then click rename …; a dialog window will open showing the actual model’s name (“1EZQ.pdb A”) which should be modified to “1EZQ.A.pdb”; then click OK. In a similar way, rename all the other complexes (see Note 11). A list of renamed models as shown in Fig. 7 (left) should be obtained and ready to be saved: click File ➔ Save PDB …; a new window will open; click “01_PDB_ALIGNED” folder, check that “Save relative to model” is set to “2CJI.A.pdb” and “Save multiple models in” is set to “multiple files [file name must contain $name or $number]”; then write “$name” in the “File name” field, and deselect “Add .pdb suffix if non given” (see Fig. 7, right).
Fig. 7

Left, renamed models in “Model Panel”; right, “Save PDB …” dialog box

Click Save. The new PDB files are now saved in the “01_PDB_ALIGNED” folder. Exit from Chimera by clicking File ➔ Quit.
  1. (f)

    Extracting Useful Data from PDB Files

Since unnecessary information is still present in the PDB files, additional cleaning is required (run the code shown in Table 4).
Table 4

Command line sequence 4: enter the “01_PDB_ALIGNED” folder and clean the PDB files

3.2.2 Extrapolating Locks and Keys

Now, proteins (“locks”) and ligands (“keys”) can be extracted from the cleaned complexes (run the code shown in Table 5).
Table 5

Command line sequence 5: save LOCK and KEY PDBs

In “03_LOCK_KEY” folder, a total of six lock and six key PDB files are now available.

3.2.3 Preparing Ligand Random Conformation

Open Babel will be employed to center the ligands (XYZ = 0,0,0) and perform conformational analysis to generate conformers to be used as input structures for the random conformation docking simulations (run the code shown in Table 6).
Table 6

Command line sequence 6: prepare ligands’ randomized conformations

Six new PDB files are now saved in “03_LOCK_KEY” folder.

3.2.4 Preparing PDBQT Input Files for AutoDock Vina

Next, two Python scripts (“” and “”) available from MGLTools will be used to format (PDBQT format) the ligand and receptor files for AutoDock Vina (run the code shown in Table 7, see Note 3).
Table 7

Command line sequence 7: prepare PDBQT files

3.2.5 Setting the Grid Box

LigandBox can be used to calculate the box center and size (run the code shown in Table 8). Since a unique grid box is required, a multi-PDBQT file needs to be created. The box center is computed by considering the center of mass of the ensemble of co-crystallized ligands, while each edge length is derived in order to enclose all the ligands with additional 10 Å from each XYZ dimension (see Notes 6 and 9).
Table 8

Command line sequence 8: enter the “04_PDBQTs” folder and launch LigandBox

A message with the grid box center XYZ coordinates, dimensions, and number of points (with a default grid points spacing of 0.375 Å) will be displayed (Fig. 8, see Note 9).
Fig. 8

Grid box parameters

The resulting grid box can be explored through AutoDockTools (ADT).

Launch ADT, and then open all the experimental PDBQT files (keys + locks): click File ➔ Read Molecule. A new window will open, double click the “04_PDBQTs” folder, select only the experimental PDBQT files (as shown in Fig. 9, top left), and then click Open. Now ligands and proteins are loaded into ADT (Fig. 9, top right).
Fig. 9

AutoDockTools (ADT). Top left, Read Molecule dialog window; top right, ADT GUI; bottom, ADT 3D-viewer and Grid Options panel

To facilitate the visualization, display the proteins as ribbon and the ligands as ball-and-stick using the dashboard widget panel on the left (Fig. 9, top right). Click Grid ➔ Grid Box ➔ Set Dimensions, and then insert the previously obtained box parameters (from LigandBox, see Note 9) in the “Grid Options” panel to show the resulting grid box (Fig. 9, bottom).

Click File ➔ Exit to close ADT.

3.2.6 Setting the AutoDock Vina Configuration File

The grid box parameters can now be included in the AutoDock Vina configuration file:

By using a text editor, create a configuration file as depicted in Fig. 10 (see Note 9), and save as “conf” in the “05_DOCKING” directory.
Fig. 10

AutoDock Vina configuration file

3.3 Docking Simulations and Assessment

Go to the parent directory “FXA” (see Subheading 2, item 8) to start the docking simulations and assessment (using AutoDock Vina and Clusterizer-DockAccessor, respectively).

To automatize the simulations, a series of list files must be created by typing the code shown in Table 9.
Table 9

Command line sequence 9: set list files for iterative ECRD and RCRD docking simulations

3.3.1 Re-docking Simulations and Assessment

Experimental conformation re-docking (ECRD) and cluster analysis can be launched by typing the code shown in Table 10.
Table 10

Command line sequence 10: ECRD (AutoDock Vina) and cluster analysis (Clusterizer)

Once calculations are finished, random conformation re-docking (RCRD) and relative cluster analysis can be started by typing the code shown in Table 11.
Table 11

Command line sequence 11: RCRD (AutoDock Vina) and cluster analysis (Clusterizer)

After completion of the calculations, it’s possible to compute the ECRD and RCRD docking accuracies (using DockAccessor) by typing in the terminal the code shown in Table 12.
Table 12

Command line sequence 12: ECRD/RCRD docking assessment (DockAccessor)

3.3.2 Cross-docking Simulations and Assessment

Experimental and random conformation cross-docking (ECCD and RCCD, respectively) can be performed by typing sequentially the codes from Tables 13, 14, and 15. Cross-docking demands higher computational time.
Table 13

Command line sequence 13: ECCD (AutoDock Vina) and cluster analysis (Clusterizer)

Table 14

Command line sequence 14: RCCD (AutoDock Vina) and cluster analysis (Clusterizer)

Table 15

Command line sequence 15: ECCD/RCCD docking assessment (DockAccessor)

3.4 Analysis of the Results

In the “VINA” folder, four log files can be found, reporting the docking accuracy values as well as the relevant RMSDs (see Notes 14 and 15). Let’s start considering the results from re-docking simulations:

Open with a text editor “ECRD.DOCKING.ACCURACY.HA.RMSDh.log” and “RCRD.DOCKING.ACCURACY.HA.RMSDh.log” files (Fig. 11), which report the docking accuracies from best docked (BD), best cluster (BC), and best fit (BF) poses (previously extrapolated through Clusterizer).
Fig. 11

Re-docking: docking accuracy values. ECRD results (top) and RCRD results (bottom)

The analysis of the results (Fig. 11) shows that:
  • BF DA value is 100% from ECRD and RCRD. This indicates that even starting from a randomized ligand conformer (RCRD simulation), the docking sampling algorithm is capable to explore efficiently the search space.

  • BD poses give the highest DA value (83.33%) compared to BC (66.67% and 50% from ECRD and RCRD, respectively).

  • Discrepancy between the BD (or BC) and the BF docking accuracy values reflects the limitation of the scoring function.

To analyze results from cross-docking simulations:

Open with a text editor “ECCD.DOCKING.ACCURACY.HA.RMSDh.log” and “RCCD.DOCKING.ACCURACY.HA.RMSDh.log” files (Fig. 12).
Fig. 12

Cross-docking: docking accuracy values. ECCD results (top) and RCCD results (bottom)

As expected, when performing cross-docking, the docking performance results get worse as the structures of the receptor with diverse ligands can be rather different. Indeed, this is demonstrated by the lower docking accuracy values from BF poses (since the increased difficulty to sample the experimental conformer of a ligand when considering non-cognate protein structures). Moreover, BD poses still outperform the BC ones in terms of docking accuracy values (Fig. 12), suggesting to consider (in this instance) the BD poses when docking ligands with no experimental pose information.

4 Notes

  1. 1.

    Command lines may be written line by line (press enter or return at the end of each line) as reported in the tables (without the line number). Since temporary environment variables are set during the computation, the same Linux terminal window must be used.

  2. 2.

    AutoDock Vina program files (“vina” and “vina_split”) must be executable throughout the whole system (e.g., copied or linked to /usr/local/bin). Test: open a terminal, write “vina” or “vina_split,” and then press enter (or return). Both programs should run.

  3. 3.

    Some Python scripts from the MGLTools “Utilities24” folder ( must be executable throughout the whole system (e.g., copied or linked to /usr/local/bin), in particular “,” “,” and “”. Test: open a terminal, write “” or “” or “,” and then press enter (or return). All the programs should run.

  4. 4.

    Clusterizer-DockAccessor programs must be executable throughout the whole system (e.g., copied or linked to /usr/local/bin). Test: open a terminal, write “” or “,” and then press enter (or return). All the programs should run.

  5. 5.

    After installing DOCK6.8, an environment variable called “DOCKPATH” (specifying the absolute path in which DOCK6 is installed) must be set: i.e., write “export DOCKPATH=/SOFTWARE/dock6” if DOCK6 is installed in /SOFTWARE/dock6. The DOCKPATH variable must be set before starting the protocol.

  6. 6.

    LigandBox program must be executable throughout the whole system (e.g., copied or linked to /usr/local/bin). Test: open a terminal, write “,” and then press enter (or return). The program should run.

  7. 7.

    Open Babel program must be executable throughout the whole system. Test: open a terminal and write “obabel”, and then press enter (or return). The program should run.

  8. 8.

    UCSF Chimera program may be executable throughout the whole system. Test: open a terminal and write “chimera”, and then press enter (or return). The program should run.

  9. 9.

    A reduced set of FXa co-crystal structures was selected for the purpose of this exercise. Since the crystal structures of PDB entries can be revised during the time, current atomic coordinates can differ from those actually used when preparing this chapter; as a consequence, the grid box center XYZ coordinates and size can differ from those herein reported.

  10. 10.

    The use of input ligand structures with randomized conformation is preferred, since it prevents biases toward the starting conformation in the sampling algorithm.

  11. 11.

    Because of the variability of the PDB files, preparation of other PDBs may differ from the one herein described. Thus, a preventive inspection of the considered PDB files is generally necessary.

  12. 12.

    UCSF Chimera assigns protonation states at physiological pH. However, a visual inspection of the protonated ligands and proteins is always recommended. If protonation at different pH is required, Open Babel is a valid alternative to be considered.

  13. 13.

    Energy minimization of 3D structures solved by X-ray crystallography is generally carried out (before docking simulation) to reduce nonphysical contacts or interactions and optimize molecular geometry. In the present exercise, energy minimization is not addressed since it is beyond the scope of this work.

  14. 14.

    Since AutoDock Vina uses a random seed for the search algorithm, a certain variability of the docking results is expected.

  15. 15.

    Extensive analyses (not discussed in this exercise) can be performed by considering the RMSD values from each docked ligand, from re-docking and cross-docking results. For example, it is possible to detect if: 1) the simulation fails when docking a certain ligand scaffold (i.e., when higher RMSD values are obtained by docking a congeneric series of compounds); 2) a representative structure from the protein ensemble can be used proficiently to dock new ligands (i.e., when from cross-docking simulations lower RMSDs are obtained by docking different ligands in a same protein conformer from the ensemble). Also, quantification of the DA results can help the user in tuning the docking parameters (e.g. AutoDock Vina’s exhaustiveness) to achieve optimal performance.




F.B. thanks Prof. Garland R. Marshall (Washington University School of Medicine in St. Louis, MO) for supporting and funding the design and development of the Clusterizer-DockAccessor protocol; Dr. Chris M. W. Ho (Drug Design Methodologies, LLC, St. Louis, MO) and Ms. Mariama Jaiteh (Uppsala University, Uppsala, Sweden) for providing insightful comments.


  1. 1.
    Persch E, Dumele O, Diederich F (2015) Molecular recognition in chemical and biological systems. Angew Chem Int Ed Eng 54(11):3290–3327. CrossRefGoogle Scholar
  2. 2.
    Yu W, MacKerell AD Jr (2017) Computer-aided drug design methods. Methods Mol Biol 1520:85–106. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Tang YT, Marshall GR (2011) Virtual screening for lead discovery. Methods Mol Biol 716:1–22. CrossRefPubMedGoogle Scholar
  4. 4.
    Ballante F, Ragno R (2012) 3-D QSAutogrid/R: an alternative procedure to build 3-D QSAR models. Methodologies and applications. J Chem Inf Model 52(6):1674–1685. CrossRefPubMedGoogle Scholar
  5. 5.
    Ballante F, Reddy DR, Zhou NJ et al (2017) Structural insights of SmKDAC8 inhibitors: targeting schistosoma epigenetics through a combined structure-based 3D QSAR, in vitro and synthesis strategy. Bioorg Med Chem 25(7):2105–2132. CrossRefPubMedGoogle Scholar
  6. 6.
    Kubinyi H (1993) 3D QSAR in drug design. Volume 1: theory methods and applications. Three-dimensional quantitative structure activity relationships, Vol. 1. Springer, BerlinGoogle Scholar
  7. 7.
    Oprea TI, Waller CL (1997) Theoretical and practical aspects of three-dimensional quantitative structure-activity relationships. In: Reviews in computational chemistry. Wiley, Hoboken, NJ, pp 127–182. CrossRefGoogle Scholar
  8. 8.
    Bursulaya BD, Totrov M, Abagyan R et al (2003) Comparative study of several algorithms for flexible ligand docking. J Comp Aided Molec Design 17(11):755–763. CrossRefGoogle Scholar
  9. 9.
    Stahl M (2000) Modifications of the scoring function in FlexX for virtual screening applications. Perspect Drug Discov Design 20(1):83–98. CrossRefGoogle Scholar
  10. 10.
    Wang R, Lu Y, Fang X et al (2004) An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J Chem Inf Comput Sci 44(6):2114–2125. CrossRefPubMedGoogle Scholar
  11. 11.
    Reddy DR, Ballante F, Zhou NJ et al (2017) Design and synthesis of benzodiazepine analogs as isoform-selective human lysine deacetylase inhibitors. Eur J Med Chem 127:531–553. CrossRefPubMedGoogle Scholar
  12. 12.
    Pettersen EF, Goddard TD, Huang CC et al (2004) UCSF Chimera—a visualization system for exploratory research and analysis. J Comput Chem 25(13):1605–1612. CrossRefPubMedGoogle Scholar
  13. 13.
    Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Maignan S, Guilloteau JP, Pouzieux S et al (2000) Crystal structures of human factor Xa complexed with potent inhibitors. J Med Chem 43(17):3226–3232CrossRefPubMedGoogle Scholar
  16. 16.
    Kamata K, Kawamoto H, Honma T et al (1998) Structural basis for chemical inhibition of human blood coagulation factor Xa. Proc Natl Acad Sci U S A 95(12):6630–6635CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Scharer K, Morgenthaler M, Paulini R et al (2005) Quantification of cation-pi interactions in protein-ligand complexes: crystal-structure analysis of Factor Xa bound to a quaternary ammonium ion ligand. Angew Chem Int Ed Eng 44(28):4400–4404. CrossRefGoogle Scholar
  18. 18.
    Watson NS, Brown D, Campbell M et al (2006) Design and synthesis of orally active pyrrolidin-2-one-based factor Xa inhibitors. Bioorg Med Chem Lett 16(14):3784–3788. CrossRefPubMedGoogle Scholar
  19. 19.
    Pinto DJ, Orwat MJ, Quan ML et al (2006) 1-[3-Aminobenzisoxazol-5’-yl]-3-trifluoromethyl-6-[2’-(3-(R)-hydroxy-N-pyrrolidin yl)methyl-[1,1’]-biphen-4-yl]-1,4,5,6-tetrahydropyrazolo-[3,4-c]-pyridin-7-one (BMS-740808) a highly potent, selective, efficacious, and orally bioavailable inhibitor of blood coagulation factor Xa. Bioorg Med Chem Lett 16(15):4141–4147. CrossRefPubMedGoogle Scholar
  20. 20.
    Ballante F, Marshall GR (2016) An automated strategy for binding-pose selection and docking assessment in structure-based drug design. J Chem Inf Model 56(1):54–72. CrossRefPubMedGoogle Scholar
  21. 21.
    Allen WJ, Balius TE, Mukherjee S et al (2015) DOCK 6: Impact of new features and current docking performance. J Comput Chem 36(15):1132–1156. CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    O’Boyle NM, Banck M, James CA et al (2011) Open Babel: an open chemical toolbox. Aust J Chem 3:33. CrossRefGoogle Scholar
  23. 23.
    The Open Babel Package. 2.4.1 Accessed June 2017. edn.
  24. 24.
    Berman HM, Westbrook J, Feng Z et al (2000) The protein data bank. Nucleic Acids Res 28(1):235–242CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Biochemistry and Molecular BiophysicsWashington University School of MedicineSaint LouisUSA
  2. 2.Department of Cell and Molecular BiologyUppsala Biomedicinska Centrum BMC, Uppsala UniversityUppsalaSweden

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