Fast Docking Using the CHARMM Force Field with EADock DSS AURE´LIEN GROSDIDIER,1 VINCENT ZOETE,1 OLIVIER MICHIELIN1,2,3 1
Swiss Institute of Bioinformatics (SIB), Quartier Sorge, Baˆtiment Ge´nopode, CH-1015 Lausanne, Switzerland 2 Ludwig Institute for Cancer Research, Lausanne Branch, Switzerland 3 Pluridisciplinary Center for Clinical Oncology (CePO), Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland Received 13 October 2010; Revised 24 January 2011; Accepted 27 February 2011 DOI 10.1002/jcc.21797 Published online 3 May 2011 in Wiley Online Library (wileyonlinelibrary.com).
Abstract: The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. Nowadays, the universe of docking software ranges from fast and user friendly programs to algorithmically flexible and accurate approaches. EADock2 is an example of the latter. Its multiobjective scoring function was designed around the CHARMM22 force field and the FACTS solvation model. However, the major drawback of such a software design lies in its computational cost. EADock dihedral space sampling (DSS) is built on the most efficient features of EADock2, namely its hybrid sampling engine and multiobjective scoring function. Its performance is equivalent to that of EADock2 for drug-like ligands, while the CPU time required has been reduced by several orders of magnitude. This huge improvement was achieved through a combination of several innovative features including an automatic bias of the sampling toward putative binding sites, and a very efficient tree-based DSS algorithm. When the top-scoring prediction is considered, 57% of BMs of ˚ RMSD to the crystal structure. Up to 70% were reproduced a test set of 251 complexes were reproduced within 2 A when considering the five top scoring predictions. The success rate is lower in cross-docking assays but remains comparable with that of the latest version of AutoDock that accounts for the protein flexibility. q 2011 Wiley Periodicals, Inc.
J Comput Chem 32: 2149–2159, 2011
Key words: protein–ligand docking; CHARMM; drug design; structure-based drug design; EADock; EADock DSS; http://www.swissdock.ch
Introduction Docking and Drug Design
During the last 20 years, the prediction of the detailed molecular interaction between two compounds, like a protein of biological interest and a drug-like small organic molecule, has aroused a tremendous interest among members of the scientific community. Because of the regular increase in computing power available, the contribution of docking software to drug discovery pipelines has significantly evolved. Huge efforts have been invested in virtual high-throughput screening (VHTS), in which hundreds of thousands and even millions of automatic dockings are performed to prioritize the molecules to test experimentally against a target protein of therapeutical interest. However, the success of VHTS heavily depends on the reliability and accuracy of docking software, questioned by several recent studies.1,2 A critical need for improvement was pointed out concerning both
the sampling engine and the scoring function, which are central to all docking programs.1 As a consequence, it is no surprise that despite notable success,3–5 VHTS led to globally disappointing results.4,6 Interestingly, some authors recently stated that, to obtain the best results, VHTS should be considered from
Correspondence to: O. Michielin; e-mail:
[email protected] or V. Zoete; e-mail:
[email protected] Additional Supporting Information may be found in the online version of this article. Contract/grant sponsor: Swiss Institute of Bioinformatics, Swiss National Science Foundation; contract/grant numbers: SCORE 3232B0-103172, 3200B0-103173, 310030-130857 Contract/grant sponsor: Oncosuisse; contract/grant number: OCS 0138108-2003 Contract/grant sponsor: National Center of Competence in Research (NCCR) Molecular Oncology
q 2011 Wiley Periodicals, Inc.
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a ‘‘problem-centric rather than a method-centric perspective".6 Despite many attempts to design automated docking procedures, human expertise remains by far the most reliable element to achieve the best predictions.6 Indeed, expert knowledge and know-how are needed to adapt the different docking strategies, including the choice of the software itself, to the underlying biological specificities.6 For this reason, it may be considered unlikely that a single docking program will outperform all others in the near future. On the contrary, several docking approaches, each one of them having its own advantages, are very likely to remain the first choice in their specific scientific niches. EADock2 as a Prototyping Engine
EADock2 has been developed focusing on both accuracy and flexibility.7 Its accuracy was achieved by using a multiobjective evolutionary algorithm combining two physically sound scoring functions based on the CHARMM22 force field with an innovative sampling engine. The latter features several deterministic operators able to transiently modify the force field to cross energy barriers. The EADock2 flexibility was obtained by using CHARMM8 as a molecular mechanics (MM) engine, allowing the straightforward use of all features of this complete and very active molecular modeling package. The high performance of EADock2 on a validation set of 260 test complexes was published recently,7 but more importantly, thanks to its algorithmic flexibility, EADock2 opened the way to several methodological explorations9,10 and led to successful real world applications.11–14 However, EADock2, which relies on CHARMM for energy calculations and conformational exploration, remains computationally expensive. This pointed out the need for a much faster docking approach, which would keep the CHARMM22 force field for MM calculations and preserve the efficiency of EADock2 for most of the real world applications. EADock DSS as an Efficient Docking Software
EADock dihedral space sampling (DSS) aims at addressing these issues. Built on the key concepts unveiled by EADock2, EADock DSS (available online at http://www.swissdock.ch) is vastly improved by several innovative features that are presented in this article. A benchmark was carried out and led to a correct ˚ RMSD to prediction [i.e., a binding mode (BM) closer than 2 A the crystal structure] for 54.5% of the validation database of 251 protein–ligand test complexes. When considering the five most favorable predicted BMs, the success rate increases to 63.7%, and up to 70.0% when more thorough sampling parameters are chosen. Importantly, the performance on drug-like molecules (i.e., compounds with less than 15 free dihedral angles, which represent 99.3% of compounds found in the FDA-approved subset of the ZINC database as of October, 201015) is equivalent to that of EADock2, while the CPU time required has been reduced by several orders of magnitude. This makes EADock DSS a suitable back end for the SwissDock web portal (Grosdidier et al., in press; http://www.swissdock.ch), which aims at making the latest methodological development in docking software available to a wide community of researchers, including nonspecialists in molecular modeling.
Material and Methods Presentation of EADock DSS
To meet the speed requirements and the efficiency stated above, the algorithm of EADock DSS relies on four cornerstones. The first is the identification of putative binding pockets before the docking itself. The second is a new sampling engine called DSS, which is able to generate several tens of thousands of nonclashing BMs per second for a ligand of 10–15 internal degrees of freedom. The third is a new grid-based scoring function whose speed does not hinder the DSS engine. The last cornerstone is the incorporation of the generated BMs into the multiobjective scoring scheme developed previously for EADock2.7 These four aspects of EADock DSS are presented below, along with the methods used to analyze its performance. Cavity Identification
It has been recently suggested that a convenient way to reduce the complexity of the docking problem could be to identify putative binding sites where the sampling time should be invested.16 EADock DSS implements such a strategy through a variant of the grid-based LIGSITE algorithm.17 In brief, the protein is mapped onto a 3D grid. A grid node is considered part of the protein if it is within the van der Waals radius of any protein atom. Next, for each node in the solvent, the x, y, and z axes, and the four cubic diagonals are scanned for protein–solvent– protein events, as referred to in the original article. These events are sequences of grid nodes, which start and end with the protein and with at least one solvent node in between. Grid nodes that exhibit more than three and less than seven protein–solvent–protein events are considered as belonging to a protein cavity. Such points, referred to as ‘‘cavity points,’’ cluster into cavities and define their shape and volume. Depending on the ligand size, the cavities that are too small are excluded as follows: The size of each cavity (approximated by its number of cavity points) is compared with the size of the ligand (approximated by its number of atoms). If the number of cavity points is lower than half of the number of atoms, the cavity is considered too small for the ligand to fit into and is discarded. Generation of New BMs
Controlled Sampling Bias. The sampling bias toward cavities is controlled by a simple distance cutoff from reference positions (RPs) that are located in identified cavities. A maximum of 30 RPs are defined by a procedure iterating over the latter, as follows: For each cavity, the cavity point with the highest number of protein– solvent–protein events is promoted to RP if a minimal distance of ˚ to any of the previously selected RPs is respected and if the 1.6 A maximum number of RPs has not been reached. Once all identified cavities have been considered, the iterative procedure starts anew, considering cavity points with the second highest number of protein–solvent–protein events, and so forth. The sampling bias is implemented as follows: First, a RP is chosen. The ligand is randomly positioned within its allowed spherical volume (see results) and randomly rotated in space. A new nonclashing conformation is then generated by the new
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Figure 1. First step of the incremental reconstruction by our DSS algorithm, for a branch of the ligand of the HIV1 protease 5HVP. For more clarity, hydrogen atoms are neither shown nor taken into account in the following description. (A) The core atoms of the ligand are shown in red in the binding site. All other atom positions will be optimized by the DSS algorithm. Their initial positions are shown in translucent white, except those of the benzyl branch that will be optimized first, which are shown in yellow. Notice the initial clash between the phenyl ring and the protein surface (blue). According to our prioritization criterion, the dihedral labeled 1 is to be optimized first, because it is closest to the core atoms. The order of optimization of dihedrals is based on their distance (number of bonds) to core atoms. As a consequence, all dihedrals attached to the core atoms are refined first. However, for reasons of clarity, in this example the second dihedral (labeled 2) will be refined just after dihedral 1. (B) During the optimization of the first dihedral angle, a single atom (green sphere) will be effectively moved because the position of all other atoms in that branch also depends on the second dihedral angle. Several possible angle values can be assigned (green translucent cone). Five angles values are randomly picked with probabilities calculated according to the corresponding CHARMM22 energy, the ones leading to steric clashes with the protein or with the ligand itself are discarded, and the one leading to the best grid-based interaction energy between the green atom and the protein is retained. If all tested angle values lead to steric clashes, the parent dihedral angle, identified in the connectivity graph, is reoptimized. Otherwise, the dihedral angle value leading to the most favorable interaction energy is retained. (C) All the phenyl atoms shown in green are rotated during the optimization of the second dihedral angles and will have their energy taken into account for the dihedral angle value selection. (D) Final position of the benzyl group. Molecular graphics images were produced using the UCSF Chimera package23 from the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIH P41 RR001081).
DSS engine (see below). One out of 10 BMs is positioned without this bias, so that the rest of the search space is also sampled, though less frequently. Incremental Reconstruction by DSS. The sampling procedures of the most used docking programs18 follow two main different strategies: Some docking programs (AutoDock19 and GOLD20) concomitantly generate a ligand conformation and position it in the binding site, whereas other programs (FlexX21 and DOCK22) break the ligand into pieces that are docked first and subsequently used as anchors from which the whole ligand is reconstructed in place. In EADock DSS, the sampling of the dihedral angles space of the ligand has been substantially improved compared with
that of the previous version of EADock7 and is now performed by the DSS algorithm. This sequential dihedral space optimization algorithm (Fig. 1) stands in between these two main sampling strategies, albeit closer to the ligand in situ reconstruction, and can be described as follows. First, free dihedral angles are automatically detected by generating and parsing the connectivity graph, and those not forming cycles within this graph are optimized. By convention, the dihedral rotation is applied to the smallest set of atoms connected to the central bond of the dihedral angle that is being optimized. As a consequence, atoms that are on the biggest set of all dihedral angles will never move during such an optimization. These are called core atoms (see Fig. 1 for details). The optimization priority of a dihedral angle is inversely proportional to the number of bonds between this particular dihedral
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angle and the closest core atom. Dihedral angles that are directly connected to the core atoms are optimized first, so that the degrees of freedom that have the largest impact on the ligand’s global shape are addressed first in the optimization process. The remaining dihedral angles are subsequently refined according to their priority, thereby allowing the ligand to adapt optimally to its environment. For each dihedral angle, five angle values are randomly chosen, and the one leading to the most favorable grid-based interaction energy between the rotated atoms and the target protein is retained (see below). If all rotations result in steric clashes, the optimization sequence returns to the parent dihedral angle in the connectivity graph. Each time a BM is generated, the order of optimization of dihedral angles sharing the same priority is randomized. This innovative tree-based sampling algorithm is very efficient by design, because the sequential optimization allows for steric clashes to be detected and corrected before generating the whole conformation of the ligand. Moreover, the sampling of each dihedral angle typically requires the rotation of only one or two atoms. To insure optimal execution times, this very efficient DSS engine has been implemented in the Java code of EADock.
Table 1. Details of the different parameter values defined for each tested
Filtering Out Redundant BMs. Once a new BM is generated, its RMSD with each previously generated BM is calculated. If this ˚ , the BM with the less favorable grid-based RMSD is below 2 A energy is discarded (see below). This filtering limits the presence of potentially redundant BMs generated in the most accessible parts of the search space.
Sort by SimpleFitness. First, BMs that have been generated and filtered are sorted according to their SimpleFitness (CHARMM22 energy, as in EADock27), which is evaluated on a grid (see above).
Filtering Out Poorly Interacting BMs The random positioning based on the cutoff distance to one of the RPs might lead to BMs making very limited interactions with the protein surface even after the dihedral space optimization. Such BMs, identified ˚ to any of the tarby the fraction of their atoms closer than 2 A get protein atoms higher than 40%, are discarded. Grid-Based Scoring of New BMs
To maximize the execution speed of the DSS engine, we used a new grid-based scoring function that outperforms the traditional pairwise potential calculations7 by more than three orders of magnitude. This new scoring function evaluates the CHARMM22 energy with a soft core potential,24 using both van der Waals and electrostatic grids to calculate the interaction energy between the ligand and the target protein. For the sake of CPU and memory efficiency, grids are allocated and calculated on demand only for the regions in space that are effectively explored by the ligand. Such a tight coupling between the grid-based scoring and the DSS engine allows the sampling of several hundreds of thousands of BMs, and the easy identification of the most energetically favorable ones, which can then be refined further with the slow, yet more accurate CHARMM package. Incorporation of the Generated BM into the Multiobjective Scoring Scheme of EADock2
BMs generated by the DSS engine and scored on the grid have not been minimized. As the CHARMM22 energy is very sensi-
parameter preset, together with the average run time observed on the 251 test complexes on a single core of Xeon E5440 processors. Parameter
Short
Medium
Default
Long
Number of BMs generated by the DSS engine SD minimization steps ABNR minimization steps Number of BMs clustered and evaluated by the FullFitness Average time (min)
1000
3000
5000
30,000
20 50 50
50 100 150
100 250 250
100 250 5000
13 6 17
19 6 19
24 6 54
195 6 144
tive to small variations of coordinates, they have to be refined before the final ranking. This is carried out by applying the multiobjective scoring scheme of EADock2 to the BMs generated by the DSS engine, see below.
Minimization of Most Favorable BMs in the CHARMM Force Field. The most favorable BMs are relaxed using CHARMM by a steepest descent minimization followed by an adopted Newton–Raphson minimization, and their exact CHARMM22 energy is calculated. The number of minimization steps depends on the parameter scheme and is reported in Table 1. Evaluation of Clusters with FACTS. A critical issue revealed by our previous investigations was that despite the energy minimization, the CHARMM energy including an implicit solvent model remains very sensitive to small coordinate variations. This issue was addressed by considering clusters of BMs and to determine the average energies of their members.7 Here, the minimized BMs are clustered by RMSD with a distance cutoff ˚ , and the latter are evaluated by their FullFitness. As in of 2 A EADock2,7,25 the latter includes the FACTS9 solvation energy for the complex, the algorithm of which is able to automatically calculate possible missing parameters for small molecules and nucleic acids. Cluster Refinement. During their minimization, some closely related BMs, after having successfully passed the redundancy filtering described above by a whisker, are likely to converge to the same local minimum, as a consequence of which clusters generally contain no more than two or three BMs. As this is not enough to reliably evaluate the energy of the corresponding pose, the most promising clusters are expanded with the following procedure: One of the BMs of the cluster is randomly selected and subjected to a random combination of EADock ‘‘smart operators.’’26 This procedure is repeated until a given number of new BMs have been generated. Once all clusters have been expanded, newly generated BMs are reclustered for
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the sake of consistency, because a smart operator might allow transitions between clusters. Such a local and deterministic sampling has shown a good ability to generate clusters of BMs with an energy distribution tight enough to allow for an accurate ranking of the clusters.7 Finally, the prediction of EADock DSS consists in these new clusters of BMs, ranked according to their FullFitness. Importantly, the output of EADock DSS is fully compatible with that of EADock2, the latter may be used for post-processing if needed.
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Each preset consists of specific values for the number of BMs to sample, the number of BMs to minimize as well as the length of the minimization, and the number of BMs to cluster and evaluate with FACTS (Table 1). During the native docking assay, the performance of EADock DSS was evaluated for each preset. As this was a blind docking assay, similar or better performances can be expected in local docking, depending on the ligand and the target protein. Default parameters were used for the cross-docking assay (see Table 1).
Algorithm Validation
Such important algorithmic changes from the previous release of EADock require a careful validation, which was performed using a native docking assay based on the LPDB dataset,27 see below. Performance at Each Step. The evolutionary algorithm of EADock2 was replaced by a multistep linear procedure consisting in (i) the generation and rough scoring of BMs, (ii) their minimization, and (iii) their clustering and evaluation according to their FullFitness. The success rate (SR) of each of these three steps was evaluated by looking at the RMSD between the ligand pose observed in crystal structure and the BM with the most favorable score (SR0), the lowest RMSD among the five most favorable BMs (SR5), and finally the lowest RMSD whatever the rank of the corresponding BM (SRMax). In all cases, a prediction was consid˚. ered successful if the corresponding RMSD was lower than 2 A Database of Complexes Native Docking Assay
The 260 test complexes on which the performance of EADock2 was assessed were also used in this study.7 However, nine of them systematically led to CHARMM errors during FACTS calculations: alpha thrombin 1dwb, 1dwc, 1dwd, 1uvs, D-xylose isomerase 1die, hiv1 protease 5hvp, triosephosphate isomerase 2ypi, xylose isomerase 2xis, and glucoamylase 1ac0. They were removed, and a total of 251 complexes were considered for the validation of EADock DSS. Cross-Docking Assay
A cross-docking assay was performed with EADock DSS to evaluate its performance when the structure of the target protein does not present a perfect induced fit for the ligand. Following the procedure given in Ref. 28, this was performed using the 53 HIV1 protease complexes of the native docking database, for which all ligands were combined with all proteins, resulting in 2809 complexes. Docking Assay Parameter Presets
EADock DSS has several free parameters. Different presets were defined, corresponding to different types of applications and docking times: Short, medium, default, and long.
Analysis
Alongside raw success rates, a detailed analysis of the performance for the native docking assay was conducted, looking at the influence of the number of free dihedral angles of the ligand, and at the fraction of the surface of the ligand that is buried upon complexation (%BS): The higher this value, the more buried and the more constrained the BM. Both analyses were also performed on EADock2 runs to compare the performance and speed of EADock DSS and EADock2.
Results and Discussion Algorithmic Validation Cavity Identification
Evaluation of the Sampling Bias. Figure 2A shows a histogram of the distances between the geometrical center of the ligand in the crystal structure and the closest RP for the 251 test complexes. This is of major importance because RPs define the spherical volumes on which the subsequent sampling is focused, which should encompass the position of the ligand in the crystal structure. As can be seen from this figure, a distance lower than ˚ was obtained for 184 complexes (74 %), illustrating the 2 A good performance of the cavity identification procedure. The maximum length of the random translation applied to the ligand starting from one of the RPs is a trade-off between a vast majority of complexes for which the search space can be successfully narrowed (i.e., the position of the native BM can be obtained by the translation), and a few complexes for which the docking outcome will rely on the unbiased sampling occurring in one out of ten BM generations (see material and methods). The maximum translation, hence the radius of the spherical vol˚ (vertical line in Fig. 2A) to allow a direct ume, was set at 10 A access to the native BM for 243 complexes, while only eight rely on the unbiased sampling. The binding sites of seven complexes were too shallow to be identified (%BS ranging from 0.65 to 0.71): c-src tyrosine kinase 1a07, hemagglutinin 1hge, 1hgh, 1hgj, 4hmg, and ribonuclease 1afk, 1afl. The case of the last complex can be explained by another wide cavity, which captures most RPs (6-phosphogluconate dehydrogenase 1pgp). None of these complexes could be docked successfully. Selectivity of the Sampling Bias. The selectivity of search space filtering was assessed by calculating the fraction of RPs closer
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˚ from any atom of the ligand in the crystal RPs closer than 2 A structure is shown in Figure 2B. For 40% of the ligands, more ˚ to any of their atoms in than 50% of all RPs are closer than 2 A the BM observed in the crystal structure. This will lead to a very strong sampling bias toward this region of space, allowing much faster run times together with a better robustness of blind docking assays. For a small amount of complexes (15%), no ˚ to the ligand in the more than three RPs are closer than 2 A crystal structure. This applies to complexes in which the ligand is poorly bound on the surface of the target protein rather than being buried, and to complexes having several binding pockets into which the ligand could fit. Considering the enormous reduction of the search space offered by the cavity detection algorithm, the resulting sampling bias, albeit limited, is still likely to perform better than a naive uniform sampling. Interestingly, the other way around, the recognition of the volume of the cavity is successful because 85% of the ligands ˚ to any cavity have at least 80% of their atoms closer than 2 A grid point. This indicates that not only the cavity detection algorithm is able to identify the native binding pockets in most of the cases but also that the free space available for the ligand is correctly estimated. A schematic representation of the sampling bias toward cavities for the HIV1 protease complex 5HVP is shown in Figure 3. As can be seen from this figure, most of the protein surface is ignored, and a strong focus on the native binding site is observed. Performance at Each Step
Figure 2. (A) Histogram of the distances observed between the center of mass of the ligand and the closest RP in the crystal structures ˚ for 74% of of the 251 test complexes. This distance is below 2 A complexes and highlights the performance of our cavity identifica˚ indicates the maximum tion procedure. The vertical line at 10 A translation allowed for biasing the search space toward cavities (see text for details). (B) Histogram of the fraction of the RPs that are ˚ to any atom of the ligand in the crystal structure for closer than 2 A each of the 251 test complexes. This reflects the good selectivity of our sampling bias, because more than 50% of all RPs are closer ˚ in 40% of the cases. than 2 A
˚ from any atom of the ligand in the crystal structure. than 2 A The higher this fraction, the stronger is the sampling bias of the search space toward the true binding pocket. The fraction of
The DSS engine generates BMs quickly. Some of them have limited steric clash with the protein and are better ranked with a softcore scoring function. The combination of both is however likely to generate errors above the typical 5 kcal/mol energy difference between the BMs present in the crystal structure and docking decoys according to our SimpleFitness.7 Many fine ranking errors are likely to remain and are addressed by (i) BM minimization and (ii) clustering and scoring with FullFitness. The impact of these two steps on the success rate is laid out in Table 2. When all BMs generated and scored on the grid, whatever their ranks, are considered, the success rate is already high (62%). The table clearly shows that SR0 and SR5 resulting from the grid-based scoring function are dramatically improved by the minimization. This confirms the need for hybrid docking algorithms combining a stochastic search with a minimization procedure. On the contrary, the increase of SR5 is slightly above the fluctuation typically observed (data not shown), and this last step increases the success rate, as it has been pointed out in our previous work.7 Performance on Native Structures Blind Docking
Overall Success Rate. The success rate obtained with EADock DSS on the 251 test complexes is shown in Table 3. With default parameters, the predicted BM was correct for 54.5% of the test complexes. This success rate increased up to 63.7% when the five most favorable predicted BMs were considered. Performance varies depending on the set of parameters. It is
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Table 2. This table shows the success rates (see text for details) after
each step of the EADock DSS algorithm, considering the most favorable prediction (SR0), the lowest RMSD among the five most favorable predictions (SR5), and the lowest RMSD whatever the rank of the prediction (SRMax). The good initial performance of the DSS engine is highlighted by its promising SRMax of 62%. Step
SR0 (%)
SR5 (%)
SRMax (%)
DSS sampling Minimization Clustering1FullFitness
17 55 55
29 62 64
62 68 69
different niche, in which a greater algorithmic flexibility is requied (e.g., ad hoc operators design, flexible docking, or extra potential).7 Several synergies between the two approaches are currently being investigated, among which the two most obvious, that are (i) generating seeds for EADock2 using EADock DSS and (ii) integrating part of EADock DSS into EADock2 as an operator. These developments will be reported separately. We also noticed that the performance of EADock DSS on this set of 251 complexes is comparable with that of AutoDock (data not shown), with a speed similar to that of its latest and fastest derivative AutoDock Vina29 (data not shown). This is in agreement with the latest published benchmark of AutoDock4 which was able, considering the most favorable prediction, to reproduce the crystal ˚ for 85 out of 188 test complexes (45%).30 structure within 2.5 A This slightly lower success rate might be explained by the different protein setups and docking algorithms. Influence of the Number of Dihedral Angles of the Ligand. The performance of EADock DSS depends on the number of free dihedral angles of the ligand, as shown in Figure 4. As expected, the higher the flexibility of the ligand, the more difficult the prediction. The cumulative success rate shows that the performance
Table 3. This table presents the success rates (see text for details)
Figure 3. Schematic representation of the sampling bias toward cavities for the HIV1 protease complex 5HVP. The cavity grid points detected are shown as small dots, whereas RPs are depicted ˚ is centered as large dots. A transparent green sphere of radius 10 A on each RP to reflect the sampling density bias toward cavities. See material and methods for details. (A) Face view, (B) side view, and (C) top view.
noteworthy that even a very short docking assay managed to propose a correct BM for marginally less that 50% of the test complexes. The maximum performance is obtained for the long parameter preset, with a SR5 slightly above 70%. Comparison with Other Docking Software. EADock2 compares very favorably with EADock DSS when considering the success rates. However, EADock2 is 10–100 times slower. These differences lead to the obvious conclusion that EADock2 occupies a
considering the most favorable prediction (SR0) and the lowest RMSD among the five most favorable predictions (SR5) for EADock DSS and the different parameter sets tested, as well as for EADock2. The latter performs better, but the CPU time averaged on 251 test complexes reflects that it is ten to a hundred times slower, and targets different needs. The high standard deviations of the CPU time is due to the number of rotatable bonds of the ligands, as well as to the shapes of binding sites. As an example the docking of amprenavir (17 free dihedrals) into the HIV-1 protease (1HPV) with default parameters requires 24 minutes on a single core of a Intel(R) Xeon E5440 processor. Parameter
SR0 (%)
SR5 (%)
Short Medium Default Long EADock2
49 54 55 57 66
62 63 64 70 77
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Average CPU time (min) 13 19 24 195 1860
6 6 6 6 6
17 19 54 144 1132
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Figure 4. Success rate of EADock2 and EADock DSS with different parameter sets considering the most favorable prediction (A) or the five most favorable (B) on subsets of the complexes database defined by the number of the ligand dihedral angles. For each figure, the inset details the cumulative success rate. The performance of the much faster EADock DSS compared with that of EADock2 when dealing with ligands with less than 15 free dihedral angles makes it the tool of choice for such complexes. (C) Histogram showing the flexibility of the ligands of our validation set and for FDA approved drugs. See text for details. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
of EADock DSS on relatively rigid ligands with less than 15 free dihedral angles cannot be distinguished from that of EADock2, even when docking run times are reduced to a few
minutes (Figs. 4A and 4B). Importantly, such relatively small or rigid ligands are of major importance for drug design, because they represent 99.3% of the FDA approved subset of the ZINC
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Figure 5. Success rates of EADock2 and EADock DSS with different parameter sets considering the most favorable prediction (A) or the five most favorable (B), on subset of the complexes database defined by the fraction of the surface of the ligand which is buried upon complexation. The inset depicts the cumulative success rate. The performance of the much faster EADock DSS compared to that of EADock2 when dealing with buried ligands makes it the tool of choice for the corresponding complexes. See text for details. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
database as of October 2010,15 with a majority of ligands (52%) having 5 or less free dihedral angles. Our validation set contains ligands with 16 free dihedrals on average and is skewed toward flexible ligands by 31 of them having 15 or more free dihedrals (Fig. 4C). As such ligands are particularly difficult to dock successfully, the success rates reported on our validation set are likely to underestimate what can be expected for drug-like ligands. For drug-like ligands, EADock DSS is a very fast and efficient alternative to EADock2, even using the short parameter preset. For ligands with more than 15 free dihedral angles, better success rates were generally observed with the default and the long parameter presets. However, as the latter is eight times slower, we recommend it only for particular complexes for which the default preset has led to suspicious results.
The high SR5 obtained by the long parameter preset is due to the higher number of BMs that are minimized and scored with FACTS (5000 vs. 100–250 for the others presets). Indeed, the success rate observed before minimization increases from 56 to 66% when considering the most favorable 5000 among 30,000 BMs generated with the long parameter preset, instead of the most favorable 250 among 5000 BMs generated with the default parameter preset. On the contrary, the lower SR0 observed in the short protocol is to be related to its very short minimization scheme. A particularly interesting result is the flat curve obtained for EADock2, which hardly depends on the number of dihedral angles of the ligand. This highlights the strength of its seeding procedure and smart operators,26 and unveils the potential gain that could be achieved by a combination with EADock DSS.
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Grosdidier, Zoete, and Michielin • Vol. 32, No. 10 • Journal of Computational Chemistry
Influence of Cavity Definition. Besides the flexibility of the ligand, the impact of the ligand’s surface buried upon complexation on docking algorithms performance has not been well characterized. From Figure 5, one can see that the reliability of docking predictions is very high for complexes featuring a buried binding site (%BS [ 0.95): The SR5 ranges from 92 to 96%, whatever the parameter presets. However, when less buried binding sites are included (%BS \ 0.95), SR5 rapidly decreases. The figure unambiguously shows that the performance of EADock DSS on buried binding sites cannot be distinguished from that of EADock2. For such complexes, the faster EADock DSS is recommended. The fast, medium, and default parameter presets of EADock DSS perform equally well and can all be recommended. The high SR5 observed for EADock DSS with the long parameter presets, compared with other presets, is due to the higher number of BMs clustered and evaluated with the FullFit˚ RMSD ness (see Table 1). This allows BM that are within 2 A to the crystal structure but impaired by an unfavorable SimpleFitness to have their solvation energy taken into account and subsequently be assigned a much better rank. In real world applications, this parameter can be tuned to achieve the best performance on solvent exposed binding sites. It is worth noting that whatever the parameters chosen for EADock DSS, EADock2 performs better for complexes with open and shallow binding sites, because it does not make any assumption about the search space, in contrast to the former, which identifies cavities before docking and focuses the search around RPs. Cross Docking Assay
Although redocking assays are of major importance for the assessment of docking software, many real world drug design projects are carried out starting from target structures that have not been cocrystallized with a ligand of the family of compounds under investigation. For this reason, the reliability of cross-docking assays has to be considered. The performance observed with default parameters on a cross-docking assay of 2809 HIV1 protease complexes is shown in Table 4. Several RMSD thresholds are presented, because the native BM cannot always be exactly reproduced due to the specific induced-fit observed in crystallized complexes. Moreover, the pairwise RMSDs between the two proteins observed during the fit ˚ . Such an iniof the HIV1 proteases typically ranged from 1 to 1.5 A tial deviation must be taken into account during the assessment of docking software in cross-docking studies. This can be conven˚ iently achieved by increasing the widely accepted threshold of 2 A usually defining a successful prediction. Although the upper limit ˚ might be less relevant in some cases to verify whether the of 3.5 A atomic contacts between the ligand and the protein have been reproduced, it has nevertheless been used in a comparable study.28 As can be seen in Table 4, the success rates observed with a threshold ˚ are very low, but increase quickly as this threshold becomes of 2 A more permissive. The cross-docking matrix is shown in Supporting Information Figure 1. Large ligands are more difficult to dock, probably because they require a higher induced fit all along the binding groove. The poor performance of redocking experiments on 1hte is due to the fact that part of its binding site is sterically hindered in the crystal structure.
Table 4. This table presents the success rates (see text for details)
considering the most favorable prediction (SR0) and the lowest RMSD among the five most favorable predictions (SR5) for EADock DSS with default parameters on a cross docking assay of 2809 complexes generated from 53 experimental HIV1 protease complexes. Different success rate thresholds are presented to highlight the strong performance ˚ to 3.5 A ˚ . The higher threshold value was increase when going from 2 A chosen based on a recent publication assessing AutoDock in a crossdocking experiment and showing similar results (see text for details). ˚) RMSD threshold (A
SR0 (%)
SR5 (%)
2 2.5 3 3.5
27 32 36 41
39 48 55 63
The cross-docking success rate of 40% considering the most favorable predicted BMs is much lower than that obtained with native docking assays. A common way to address this issue is to add flexibility to the target protein. Unfortunately, when the implementation of such a flexibility is limited, the impact on docking predictions remains marginal: Despite a similarly permis˚ chosen by a recent and comparable study sive threshold of 3.5 A performed using AutoDock28 similar success rates were found, whether or not the protein flexibility was taken into account. The combination of the DSS engine with the grid-based scoring function used in EADock DSS could be applied to the protein to allow some degree of flexibility. The simplicity and efficiency of this approach would probably be very useful to tackle the additional degrees of freedom, which make the docking problem much harder to solve. Such an improvement is currently being implemented.
Conclusions Docking programs are widely used in most drug design pipelines for tasks ranging from high-throughput virtual screening to highaccuracy docking for structure-based drug design. While EADock2 is a very convenient tool for the latter and already led to several successful applications11–14 and methodological developments,9,10 its docking speed remains limiting for large-scale approaches. EADock DSS, presented in this article, addresses this problem. It is significantly faster, remains however accurate enough to be useful, because its predictions are correct for 70% of 251 test complexes, when considering the five most probable BMs. When looking at test ligands with less than 15 free dihedral angles and/or test complexes with well-defined binding pockets, its success rate reaches up to 96% and cannot be distinguished from that of EADock2. Moreover, the better overall performance of the latter indicates that the combination of both approaches, which is currently under development, is promising. A web-based interface to EADock DSS, called SwissDock, is available free of charge for the scientific community at the URL http://www.swissdock.ch.
Acknowledgments The authors thank the VITAL-IT project of the Swiss Institute of Bioinformatics (Lausanne, Switzerland) for providing the computational resources.
Journal of Computational Chemistry
DOI 10.1002/jcc
Fast Docking with EADock DSS
References 1. Leach, A.; Shoichet, B.; Peishoff, C. J Med Chem 2006, 49, 5851. 2. Tirado-Rives, J.; Jorgensen, W. L. J Med Chem 2006, 49, 5880. 3. Zoete, V.; Grosdidier, A.; Michielin, O. J Cell Mol Med 2009, 13, 238. 4. Kolb, P.; Irwin, J. J. Curr Top Med Chem 2009, 9, 755. 5. Huang, D.; Caflisch, A. J Mol Recognit 2010, 23, 183. 6. Schneider, G. Nat Rev Drug Discov 2010, 9, 273. 7. Grosdidier, A.; Zoete, V.; Michielin, O. J Comput Chem 2009, 30, 2021. 8. Brooks; B. R.; Brooks, C. L., III; Mackerell, A. D.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G; Bartels, C; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A. R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.; Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Karplus, M. J Comput Chem 2009, 30, 1545. 9. Haberthu¨r, U.; Caflisch A. J Comput Chem 2008, 29, 701. 10. Ro¨hrig, U. F.; Grosdidier, A.; Zoete, V.; Michielin, O. J Comput Chem 2009, 30, 2305. 11. Li, C.; Grosdidier, A.; Crambert, G.; Horisberger, J.-D.; Michielin, O.; Geering, K. J Biol Chem 2004, 279, 38895. 12. Michalik, L.; Zoete, V.; Krey, G.; Grosdidier, A.; Gelman, L.; Chodanowski, P.; Feige, J. N.; Desvergne, B.; Wahli, W.; Michielin, O. J Biol Chem 2007, 282, 9666. 13. Rochat, B.; Zoete, V.; Grosdidier, A.; von Gru¨nigen, S.; Marull, M.; Michielin, O Biopharm Drug Dispos 2008, 29, 103.
2159
14. Ro¨hrig, U. F.; Awad, L.; Grosdidier, A.; Larrieu, P.; Stroobant, V.; Colau, D.; Cerundolo, V.; Simpson, A. J. G.; Vogel, P.; den Eynde, B. J. V.; Zoete, V.; Michielin, O. J Med Chem 2010, 53, 1172. 15. Irwin, J. J.; Shoichet, B. K. J Chem Inf Model 2005, 45, 177. 16. Ghersi, D.; Sanchez, R. Proteins 2009, 74, 417. 17. Hendlich, M.; Rippmann, F.; Barnickel, G. J Mol Graph Model 1997, 15, 359. 18. Sousa, S. F.; Fernandes, P. A.; Ramos, M. J Proteins 2006, 65, 15. 19. Morris, G. M.; Goodsell, D. S.; Halliday, R. S.; Huey, R.; Hart, W. E.; Belew, R. K.; Olson, A. J. J Comput Chem 1998, 19, 1639. 20. Jones, G.; Willett, P.; Glen, R.; Leach, A.; Taylor, R. J Mol Biol 1997, 267, 727. 21. Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. J Mol Biol 1996, 261, 470. 22. Ewing, T. J.; Makino, S.; Skillman, A. G.; Kuntz, I. J Comput Aided Mol Des 2001, 15, 411. 23. Pettersen, E. F.; Goddard, T. D.; Huang, C. C.; Couch, G. S.; Greenblatt, D. M.; Meng, E. C.; Ferrin, T. E. J Comput Chem 2004, 13, 1605. 24. Min, D.; Li, H.; Li, G.; Bitetti-Putzer, R.; Yang, W. J Chem Phys 2007, 126, 144109. 25. Zoete, V.; Grosdidier, A.; Cuendet, M.; Michielin, O. J Mol Recognit 2010, 23, 457. 26. Grosdidier, A.; Zoete, V.; Michielin, O. Proteins 2007, 67, 1010. 27. Roche, O.; Kiyama, R.; Brooks, C. J Med Chem 2001, 44, 3592. 28. Morris, G. M.; Huey, R.; Lindstrom, W.; Sanner, M. F.; Belew, R. K.; Goodsell, D. S.; Olson, A. J. J Comput Chem 2009, 30, 2785. 29. Trott, O.; Olson, A. J. J Comput Chem 2009, 31, 455. 30. Huey, R.; Morris, G. M.; Olson, A. J.; Goodsell, D. S. J Comput Chem 2007, 28, 1145.
Journal of Computational Chemistry
DOI 10.1002/jcc
