Ligand docking software

In standard virtual docking studies, ligands are freely docked into a rigid receptor. However, it has become increasingly clear that side chain flexibility plays a crucial role in ligand—protein complexes.

These changes allow the receptor to alter its binding site according to the orientation of the ligand. Four different strategies are currently in use for docking flexible ligands, namely: a Monte Carlo or molecular-dynamics docking of complete molecules; b in-site combinatorial search, c ligand buildup; and d site mapping and fragment assembly. Monte Carlo methods accept or reject the random changes of the thermodynamic accessible states by using Metropolis criteria Metropolis and Ulam The configurations with increase in temperature T will be accepted by slow cooling through so-called simulated annealing Kirkpatrick et al.

The changes in conformations are quite large, allowing the ligand to cross the energy barriers on the potential energy surface. This technique of conformational searches combined with the potentials of molecular affinity gives an efficient method of substrate docking with known structures Goodsell and Olson Along with affinity potentials, distance constraints were added as soft potentials in simulated annealing Yue AutoDock 2.

ICM software generates the ligand in 3D grid space by Monte Carlo movements and minimization of interaction potentials. The results obtained were highly similar to FNR:Fd complexes of Anabaena and maize, showing a good correlation computationally. QXP is a multistep docking program using a local Monte Carlo search with a restricted rotational angle Pellegrini and Doniach Recently, a newly designed and implemented version of the AutoDock program called AutoDock Vina has been released.

This version abandoned the former empirical scoring function and GA-based optimizer, but adopted a new knowledge-based scoring function with a Monte Carlo sampling technique and the Broyden—Fletcher—Goldfarb—Shanno BFGS method for local optimization. Their simulation results showed a significant improvement in both prediction accuracy and docking time. In recent years, swarm intelligence algorithms have emerged as a fast and reasonably accurate technique in solving complex search problems in computer science.

Three of the programs were modifications of the popular open-source docking program AutoDock, albeit different versions, and all of them showed better predictive performance when compared to the original AutoDock implementation. Furthermore, a novel search method called QPSO-ls quantum-behaved particle swarm optimization was introduced for solving a highly flexible docking problem, which is a hybrid of quantum-behaved particle swarm optimization QPSO and a local search method of Solis and Wets Fu et al.

In another program called GalaxyDock, the receptor side chains were preselected and globally optimized using an AutoDock-based algorithm for flexible side-chain docking Shin and Seok GOLD explores the flexibility of the ligand through the process of evolution by using a genetic algorithm and displaces loosely bound water on ligand binding Jones et al.

The various degrees of conformational flexibility of DNA were sampled by the semi-flexibility of sugar-phosphate backbone and DNA base pairs for further docking calculations. Further rotamer libraries can be used to reduce the side chain placement problem to a combinatorial optimization problem with the minimum energy, i.

One of these methods is based on the dead-end elimination DEE theorem of Desmet et al. Later, GMEC was investigated as the convex hull of all feasible solutions with some classes of facet-defining inequalities in a branch-and-cut algorithm. The side chain conformations generated by these techniques are then subjected to a geometry optimization with a molecular mechanics force field. Finally, the binding free energy of the optimized structure is estimated Jackson and Sternberg Further, the algorithms were developed to build ligands directly in the binding site in flexible-docking and design strategy.

One of these was the de novo design of peptide inhibitors using a library of low-energy conformations of isolated amino acid residues as building blocks Moon and Howe Goodford introduced the idea of using functional groups water, methyl group, amine nitrogen, carboxy oxygen, and hydroxyl as molecular probes to map the binding site of a macromolecule Goodford Thus, the energy contour surfaces for the various probes differentiate regions of attraction between the probe and protein.

The procedure is well suited to multiple-copy techniques Miranker and Karplus The goal of fragment-assembly approaches, pioneered by Lewis and Dean a , b , is to connect the individual molecular fragments into a single viable molecule. The CLIX program attempts to make a pair of favorable interactions in the binding site of the protein with a pair of chemical substitutions Lawrence and Davis LUDI places molecular fragments to form hydrogen bonds with the enzyme so that the hydrophobic pockets are filled.

The linked-fragment approach of Verlinde and coworkers are based on shape descriptors Verlinde et al. Caflisch and coworkers used MCSS maximal common substructure search against HIV protease to map a binding site and constructed peptide inhibitors by building bonds to connect the various minima they found Caflisch et al. FlexX uses a tree-search technique for placing the ligand into the active site, incrementally starting with the base fragment Rarey et al.

Unlike other docking programs, Glide performs a complete systematic search of the conformational, orientational, and positional space of the docked ligand with the OPLS-AA force field Optimized Potentials for Liquid Simulations. The best possible conformation is further refined using Monte Carlo sampling Friesner et al. Further, a surface-based molecular similarity method was implemented in Surflex Jain to rapidly generate suitable putative poses for molecular fragments using the Hammerhead docking system Jain In addition, a multi-objective docking strategy, MoDock, has been proposed to further improve the pose prediction with the available scoring functions divided into the following three types: force field-based, empirical-based, and knowledge-based.

The results obtained indicate that the multi-objective strategy can enhance the pose prediction power of docking with the available scoring functions Gu et al. Surprisingly, both Dock as well as FlexX were not able to produce a reasonable solution for at least three TK ligands IdU 5-iododeoxyuridine , hmtt 6-[6-hydroxymethymethyl-2,4-dioxo-hexahydro-pyrimidinyl-methyl]methyl-1H-pyrimidin-2,4-dione , and mct North -methanocarba-thymidine for Dock; hmtt, ganciclovir, and penciclovir for FlexX.

On the other hand, DOCK failed completely to predict a reliable pose for raloxifene. However, no relationship was found between the docking accuracy and ranking score with these programs Bissantz et al.

In , five docking programs, DOCK 4. The results revealed that ICM provided the highest docking accuracy against these receptors, with a value of 0. All these docking programs performed well with small hydrophobic ligands, while the performance of GOLD and Surflex remained roughly unchanged.

In the same year of , three highly regarded docking programs, namely, Glide, GOLD, and ICM, were evaluated on a vertex dataset of diverse protein—ligand complexes to predict their ability to reproduce crystallographic binding orientations.

In regards to ligand complexity, all these docking programs performed well, with the ligands having ten or fewer rotatable bonds. Evaluation of known crystal structures of 40 zinc-dependent metalloproteinase ligand complexes showed the lowest energy conformations by GOLD and DrugScore with a proper ZBG zinc binding group binding.

If the RMSD limit is increased to 2. At the RMSD cutoff of 2. Later, ten docking programs and 37 scoring functions were analyzed against seven protein types to predict the binding mode, lead identification using virtual screening, and lead optimization. Studies also showed that GOLD performed well with hydrophilic targets where there is some lipophilic character in the active site i. Furthermore, seven commonly used programs were evaluated on the PDBbind database with protein complexes Plewczynski et al.

The level of correlation for hydrophobic molecules is 0. In , four popular docking programs were evaluated, Glide version 4. Out of these four docking programs, GOLD and Surflex processed well with the dataset, while Glide and LigandFit failed to process 25 and 8 complexes, respectively. Recently, ten docking programs were evaluated. On the basis of the results for the top scored poses, the performance of the academic programs conform to the following order: LeDock The averaged success rates of the commercial docking programs in predicting the top scored poses and best poses are This shows that all these docking algorithms were able to explore the conformational space to generate correctly docked poses in the binding pockets sufficiently well on a diverse set of protein—ligand complexes.

In general. Glide performs well with diversified binding sites and flexibility of the ligand, while ICM and GOLD perform significantly poorer when binding sites are mainly influenced by hydrophobic contacts. These results also show that the difference between the commercial and academic programs was not obvious, even though the capability of predicting the ligand binding poses by the commercial programs is slightly better than that of the academic programs from a global perspective.

Structure-based drug design is a powerful technique for the rapid identification of small molecules against the 3D structure of the macromolecular targets available by either X-ray, NMR, or homology models. Because of abundant information regarding the sequences and structures of the proteins, the structural information of individual proteins and their interactions became very important for further drug therapy. Although many docking programs exist for conformational searching and binding pose prediction, the scoring functions are not accurate and need to be improved further.

Nevertheless, despite the drawbacks of each docking strategy, active research is taking place to address all the issues regarding scoring, explicit protein flexibility, explicit water, etc. Even in the absence of knowledge regarding the binding site and limited backbone movements, a variety of search algorithms have been developed for protein—protein docking over the past two decades.

As rigid body docking can systematically explore the shape complementarity between proteins, this may not work well for docking the proteins that are crystallized separately. Thus, a high-resolution protocol is very much needed to understand the basic principles to detect the underlying mechanism of protein—protein interactions and actual binding with other proteins.

Rescoring using empirical potentials may not even eliminate all the false-positives. Even fine tuning of individual protein—protein interactions by redesigning the protein interface depends on the accurate structure of the protein complex generated by high-resolution docking protocols. Although, ZDOCK, rDOCK, and HEX provided the results with high docking accuracy, the provided complexes are not highly useful to design the inhibitors for the protein interfaces due to constraints in rigid body docking.

Due to this, flexible approaches were developed that generally examine very limited conformations compared to the rigid body methods.

These docking methods predict binding poses most likely to occur on the broad surface regions and then define the sites into high-affinity complex structures. The best example is the HADDOCK software, which has been quite successful in resolving a large number of accurate models for protein—protein complexes. One good example is the study of the complex formed between plectasin, a member of the innate immune system, and the bacterial wall precursor lipid II.

The study has clearly identified the residues involved at the binding site between the two proteins, providing valuable information for the design of novel antibiotics. However, the absolute energies associated with the intermolecular interaction are not estimated with satisfactory accuracy by the current algorithms.

The major issues of solvent effects, entropic effects, and receptor flexibility still need to be handled with special attention. If you like, we can also look at some of the poor scoring models to see exactly what went wrong. To find the top 20 worse models by interface score:.

When we open it up in Pymol, we can see that the ligand binding direction is different from the native position. This can happen when there is an extended binding pocket but in this case, the Rosetta score was able to discern the difference between these models. Q: My protein has a quite large cavity and a small ligand not bigger than a Leucine. They're also the sd of a gaussian, so you're not necessarily limited to the given amount in any given step.

That said, if you're increasing the size of the pocket, it might make sense to bump the move size up in proportion. By default Transform will always start with the input position. X option, then it will first randomize the starting location of the ligand within an X. X Angstrom sphere from the starting position, as well as randomizing the orientation. Also, if you're increasing the size of the pocket, you're likely going to need to increase the size of the Grid such that it will cover the maximal extent of ligand travel.

If it doesn't, Transform will reject any ligand which accidentally falls outside the grid. Congratulations, you have performed RosettaLigand docking study! Now use your docked models to generate hypotheses and test them in the wet lab! Ligand Docking Search. Prepare a human dopamine 3 receptor structure.

We will do this by obtaining the crystal structure 3PBL and removing the excess information. The 'A' option tells the script to obtain chain A only. The full crystal structure consists of two monomers as a crystallization artifact. Normally, we would truncate this lysozyme segment and perform loop modeling as discussed in the comparative modeling tutorial to regenerate the intracellular loop. However in the interest of time, we will use the lysozyme containing structure as the eticlopride binding site is far from the intracellular domain.

In the directory, you will find a pair of already prepared files: eticlopride. The BCL is a suite of tools for protein modeling, small molecule calculations, and machine learning. The generated libraries will differ depending on the chosen method.

This is okay as Rosetta is merely telling you that the ligand has more atoms than an amino acid. A receptor coordinate file with all hydrogen atoms is required.

If you are using experimental structures for instance, from the PDB , use a text editor to remove water, ligands, cofactors, ions, etc. ADT will read coordinates, add charges, merge non-polar hydrogens, and assign appropriate atom types. Once receptor and ligand coordinates are formatted, the AutoDock suite provides a number of methods for docking simulation.

This protocol includes six methods, ranging from a simple docking to advanced methods, as described in the following table. To do this, restart ADT and set the default working directory see Step 1. For this protocol, we are assuming that we have restarted ADT and need to read from coordinate files. There may also be a warning window if there are slight irregularities in charges.

Other options are available, or the values may be changed manually with the thumbwheels. Run AutoDock Vina. The imatinib ligand used in this protocol is challenging, and Vina will occasionally not find the correct pose with the default parameters. The default exhaustiveness value is 8; increasing this to about 24 will give a more consistent docking result. Programs in the AutoDock suite may also be run at the command line. To do this, instead open a terminal window and change to the directory that contains the coordinate files and configuration file.

Then issue the command listed above. This command assumes that the AutoDock Vina executable vina is also located in the same directory. With exhaustiveness set to 24, Vina will most often give a single docked pose with this energy.

With the lower default exhaustiveness, several poses flipped end-to-end, with less favorable energy, may be reported. This will show coordinates for each docked result. Use the arrow keys on the keyboard to scroll through the poses.

The X-ray crystallographic ligand position is in silver. Note that Vina does not retain hydrogen atom positions during docking, so the threonine hydroxyl hydrogen is placed in a random position in the docked coordinate set. Generate a grid parameter file for AutoDock that specifies the PDBQT files for the receptor, and parameters for generating the atomic affinity maps. Start ADT and set the default working directory see Step 1.

After you specify the grid parameter file, ADT will suggest a name for the log file. Alternatively, at the command line, open a terminal window and change to the directory that contains the coordinate files and grid parameter file. Then issue the command:. This command assumes that the AutoGrid executable autogrid4 is also located in the same directory.

Shift-click in the histogram to calculate an isocontour; you can then drag the bar in the histogram right and left to change the contour level. Generate the docking parameter file that specifies the PDBQT file for the ligand and parameters for the docking simulation.

After you specify the docking parameter file, ADT will suggest a name for the log file. Alternatively, at the command line, open a terminal window and change to the directory that contains the coordinate files and docking parameter file. This command assumes that the AutoDock executable autodock4 is also located in the same directory.

Visualize AutoDock results Figure 3a. Tabs at the top choose each of the steps for setting up, running and analyzing a virtual screen. Start Raccoon2 and configure the server. Raccoon is designed to run virtual screening on a large computational resource, such as a Linux cluster.

When you add a new server, you must configure the connection and install one or more docking services. Launch Raccoon2 Figure 3 and create a new server connection by clicking the three-gear icon to open the Connection Manager. Several options need to be set: server name a name to identify the resource ; address the host name or IP address of the server ; and a username and password.

Install a docking service by selecting the server in the Connection menu. Click Save, and close the Service Manager.

Set up the Ligand Library. This protocol describes virtual screening of C-Abl with a small library of compounds that includes the known inhibitor imatinib. Ligand libraries are stored on the server and made available for docking jobs, to reduce redundancy and allow tracing and reproducing experiments. Right click to select the desired ligand library.

Set up the receptor coordinates. Close the report window. Configure AutoDock Vina docking parameters. Modify docking parameters if desired or load the config. Peform the virtual screening calculation. Filter and analyze the results.

To assist with filtering and selection, Raccoon calculates docking pose properties such as interaction, score, and ligand efficiency. Choose the receptor file to be used for processing results and type a descriptive log file name or select the config.

Two interaction filters are applied here. This will select the small number of molecules that show both of these interactions. The THR position of c-Abl is known to mutate to obtain resistance. Export results. Once a set of ligands is filtered, select interesting ones by clicking on the button in the Results panel. Generate receptor coordinate files. The receptor coordinates are split into two PDBQT files, one for the rigid portion and one for the flexible side chains.

As with the rigid docking protocols, the method requires a receptor coordinate file that includes all hydrogen atoms.

This protocol describes the cross-docking of imatinib to c-Abl in PDB entry 1fpu, treating Thr as flexible. Several selection methods are available, including direct selection and using the Select menu. ADT will highlight the selected residue with small yellow crosses. Generate parameter files for AutoDock Vina. For this protocol, enter them manually. The first step is to calculate the energy maps.

By default, ADT will name the map files based on the name of this file. AutoLigand may be run in two modes. This protocol will generate ligand envelopes individually, manually picking a seed point and a volume in ADT. This mode is appropriate if you know the location of the active site of your target. AutoLigand may also be run at the command line to scan the entire surface of the protein, predicting the location of optimal binding sites.

This mode is described in more detail on the AutoDock website. Use the default of for the volume of the envelope. The envelope will be displayed in the viewer Figure 5 , and a PDB file will be written with coordinates of the envelope. This method adds dummy atoms to the ligand that correspond to all possible sites of hydration.

A modified AutoGrid map is then used during docking, giving a favorable score when the water is well placed and omitting the water if it overlaps with the receptor. A final script analyzes the docked results, retaining only those waters in appropriate positions.

This protocol assumes that the ligand and receptor have been prepared for a standard AutoDock docking. Two coordinate files, ligand. Calculate the default atomic grid maps. If standard filenames are used for the maps, only the receptor name must be specified for the script that generates the map for the water energy evaluation:.

Create a modified docking parameter file. Both of these changes are highlighted in Box 3. The indicated timing of each step is a rough estimate--the actual times will depend on the complexity of the system being docked, and the equipment being used for the computation. AutoDock Vina Step 5A will provide coordinates for one or more optimized poses for the ligand Figure 3a. In our tests of the docking of imatinib with c-Abl, the default docking parameters are sufficient to give a consistent solution in most cases.

The conformational flexibility of this system is at the limit of the default docking protocol, which may be indicated by a docking result with multiple less favorable poses. For challenging systems with high degrees of conformational flexibility, the exhaustiveness parameter can be used to perform additional docking simulations, often giving more consistent results. This is described in Step 5 A iii. When analyzing results from AutoDock Vina, note that it uses the input hydrogen positions to assign hydrogen-bonding types to heteroatoms, but does not optimize them during docking simulation, so the hydrogen positions in the output pose are in random conformations.

For larger ligands, Goldscore gives superior results. Docking with the Chemscore function is up to three times faster than docking with the Goldscore function.