Maximum common binding modes (MCBM): consensus docking scoring using multiple ligand information and interaction fingerprints

Improving the scoring functions for small molecule-protein docking is a highly challenging task in current computational drug design. Here we present a novel consensus scoring concept for the prediction of binding modes for multiple known active ligands. Similar ligands are generally believed to bind to their receptor in a similar fashion. The presumption of our approach was that the true binding modes of similar ligands should be more similar to each other compared to false positive binding modes. The number of conserved (consensus) interactions between similar ligands was used as a docking score. Patterns of interactions were modeled using ligand receptor interaction fingerprints. Our approach was evaluated for four different data sets of known cocrystal structures (CDK-2, dihydrofolate reductase, HIV-1 protease, and thrombin). Docking poses were generated with FlexX and rescored by our approach. For comparison the CScore scoring functions from Sybyl were used, and consensus scores were calculated thereof. Our approach performed better than individual scoring functions and was comparable to consensus scoring. Analysis of the distribution of docking poses by self-organizing maps (SOM) and interaction fingerprints confirmed that clusters of docking poses composed of multiple ligands were preferentially observed near the native binding mode. Being conceptually unrelated to commonly used docking scoring functions our approach provides a powerful method to complement and improve computational docking experiments.     

Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening

Poor performance of scoring functions is a well-known bottleneck in structure-based virtual screening (VS), which is most frequently manifested in the scoring functions' inability to discriminate between true ligands vs known nonbinders (therefore designated as binding decoys). This deficiency leads to a large number of false positive hits resulting from VS. We have hypothesized that filtering out or penalizing docking poses recognized as non-native (i.e., pose decoys) should improve the performance of VS in terms of improved identification of true binders. Using several concepts from the field of cheminformatics, we have developed a novel approach to identifying pose decoys from an ensemble of poses generated by computational docking procedures. We demonstrate that the use of target-specific pose (scoring) filter in combination with a physical force field-based scoring function (MedusaScore) leads to significant improvement of hit rates in VS studies for 12 of the 13 benchmark sets from the clustered version of the Database of Useful Decoys (DUD). This new hybrid scoring function outperforms several conventional structure-based scoring functions, including XSCORE::HMSCORE, ChemScore, PLP, and Chemgauss3, in 6 out of 13 data sets at early stage of VS (up 1% decoys of the screening database). We compare our hybrid method with several novel VS methods that were recently reported to have good performances on the same DUD data sets. We find that the retrieved ligands using our method are chemically more diverse in comparison with two ligand-based methods (FieldScreen and FLAP::LBX). We also compare our method with FLAP::RBLB, a high-performance VS method that also utilizes both the receptor and the cognate ligand structures. Interestingly, we find that the top ligands retrieved using our method are highly complementary to those retrieved using FLAP::RBLB, hinting effective directions for best VS applications. We suggest that this integrative VS approach combining cheminformatics and molecular mechanics methodologies may be applied to a broad variety of protein targets to improve the outcome of structure-based drug discovery studies.     

Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge

We continued prospective assessments of the Wilma–solvated interaction energy (SIE) platform for pose prediction, binding affinity prediction, and virtual screening on the challenging SAMPL4 data sets including the HIV-integrase inhibitor and two host–guest systems. New features of the docking algorithm and scoring function are tested here prospectively for the first time. Wilma–SIE provides good correlations with actual binding affinities over a wide range of binding affinities that includes strong binders as in the case of SAMPL4 host–guest systems. Absolute binding affinities are also reproduced with appropriate training of the scoring function on available data sets or from comparative estimation of the change in target’s vibrational entropy. Even when binding modes are known, SIE predictions lack correlation with experimental affinities within dynamic ranges below 2 kcal/mol as in the case of HIV-integrase ligands, but they correctly signaled the narrowness of the dynamic range. Using a common protein structure for all ligands can reduce the noise, while incorporating a more sophisticated solvation treatment improves absolute predictions. The HIV-integrase virtual screening data set consists of promiscuous weak binders with relatively high flexibility and thus it falls outside of the applicability domain of the Wilma–SIE docking platform. Despite these difficulties, unbiased docking around three known binding sites of the enzyme resulted in over a third of ligands being docked within 2 Å from their actual poses and over half of the ligands docked in the correct site, leading to better-than-random virtual screening results.     

SKATE: A docking program that decouples systematic sampling from scoring

SKATE is a docking prototype that decouples systematic sampling from scoring. This novel approach removes any interdependence between sampling and scoring functions to achieve better sampling and, thus, improves docking accuracy. SKATE systematically samples a ligand's conformational, rotational and translational degrees of freedom, as constrained by a receptor pocket, to find sterically allowed poses. Efficient systematic sampling is achieved by pruning the combinatorial tree using aggregate assembly, discriminant analysis, adaptive sampling, radial sampling, and clustering. Because systematic sampling is decoupled from scoring, the poses generated by SKATE can be ranked by any published, or in‐house, scoring function. To test the performance of SKATE, ligands from the Asetex/CDCC set, the Surflex set, and the Vertex set, a total of 266 complexes, were redocked to their respective receptors. The results show that SKATE was able to sample poses within 2 Å RMSD of the native structure for 98, 95, and 98% of the cases in the Astex/CDCC, Surflex, and Vertex sets, respectively. Cross‐docking accuracy of SKATE was also assessed by docking 10 ligands to thymidine kinase and 73 ligands to cyclin‐dependent kinase. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

GalaxyDock BP2 score: a hybrid scoring function for accurate protein–ligand docking

Protein–ligand docking is a useful tool for providing atomic-level understanding of protein functions in nature and design principles for artificial ligands or proteins with desired properties. The ability to identify the true binding pose of a ligand to a target protein among numerous possible candidate poses is an essential requirement for successful protein–ligand docking. Many previously developed docking scoring functions were trained to reproduce experimental binding affinities and were also used for scoring binding poses. However, in this study, we developed a new docking scoring function, called GalaxyDock BP2 Score, by directly training the scoring power of binding poses. This function is a hybrid of physics-based, empirical, and knowledge-based score terms that are balanced to strengthen the advantages of each component. The performance of the new scoring function exhibits significant improvement over existing scoring functions in decoy pose discrimination tests. In addition, when the score is used with the GalaxyDock2 protein–ligand docking program, it outperformed other state-of-the-art docking programs in docking tests on the Astex diverse set, the Cross2009 benchmark set, and the Astex non-native set. GalaxyDock BP2 Score and GalaxyDock2 with this score are freely available at http://galaxy.seoklab.org/softwares/galaxydock.html.     

Optimizing the signal-to-noise ratio of scoring functions for protein--ligand docking

Empirical scoring functions provide estimates of the free energy of protein-ligand binding in situations when atomic-scale simulations are intractable, for example, in virtual high-throughput screening. Currently, such scoring functions are often inaccurate, and further improvements are complicated by the lack of reliable training data, the complex interplay between scoring functions and docking algorithms, and an inconsistent statistical treatment of positive and negative training data. In comparison to various other performance measures of scoring functions, "analysis of variance" provides a well-behaved objective function for optimization, which focuses on the signal-to-noise ratio of ligand-decoy discrimination. In combination with a large database of ligands and decoys, an in situ optimization of scoring function parameters was able to generate improved, target-specific scoring functions for three different proteins of pharmaceutical interest: cyclin-dependent kinase 2, the estrogen receptor, and cyclooxygenase-2. Statistical analysis of the improvements observed in "receiver-operating characteristic" curves showed that the optimized scoring functions achieved a significantly (between p < 0.0001 and p < 0.05) higher enrichment of true ligands. A scaffold dependence of the resulting binding modes was observed, which is discussed in conjunction with the rigid receptor hypothesis commonly made in protein-ligand docking. In summary, the approach described here represents a well-adapted statistical method for setting up scoring functions.     

Machine learning optimization of cross docking accuracy

Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening capabilities of the docking program, respectively. Benchmarks show that different docking programs can excel in individual benchmarks which suggests that the scoring function employed by the programs can be optimized for a particular task. Here the scoring function of Smina is re-optimized towards enhancing the docking power using a supervised machine learning approach and a manually curated database of ligands and cross docking receptor pairs. The optimization method does not need associated binding data for the receptor-ligand examples used in the data set and works with small train sets. The re-optimization of the weights for the scoring function results in a similar docking performance with regard to docking power towards a cross docking test set. A ligand decoy based benchmark indicates a better discrimination between poses with high and low RMSD. The reported parameters for Smina are compatible with Autodock Vina and represent ready-to-use alternative parameters for researchers who aim at pose prediction rather than affinity prediction.     

Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015

Here is reported the development of a novel scoring function that performs remarkably well at identifying the native binding pose of a subset of HSP90 inhibitors containing aminopyrimidine or resorcinol based scaffolds. This scoring function is called PocketScore, and consists of the interaction energy between a ligand and three residues in the binding pocket: Asp93, Thr184 and a water molecule. We integrated PocketScore into a molecular docking workflow, and used it to participate in the Drug Design Data Resource (D3R) Grand Challenge 2015 (GC2015). PocketScore was able to rank 180 molecules of the GC2015 according to their binding affinity with satisfactory performance. These results indicate that the specific residues considered by PocketScore are determinant to properly model the interaction between HSP90 and its subset of inhibitors containing aminopyrimidine or resorcinol based scaffolds. Moreover, the development of PocketScore aimed at improving docking power while neglecting the prediction of binding affinities, suggesting that accurate identification of native binding poses is a determinant factor for the performance of virtual screens.     

Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015

High affinity ligands for a given target tend to share key molecular interactions with important anchoring amino acids and therefore often present quite conserved interaction patterns. This simple concept was formalized in a topological knowledge-based scoring function (GRIM) for selecting the most appropriate docking poses from previously X-rayed interaction patterns. GRIM first converts protein–ligand atomic coordinates (docking poses) into a simple 3D graph describing the corresponding interaction pattern. In a second step, proposed graphs are compared to that found from template structures in the Protein Data Bank. Last, all docking poses are rescored according to an empirical score (GRIMscore) accounting for overlap of maximum common subgraphs. Taking the opportunity of the public D3R Grand Challenge 2015, GRIM was used to rescore docking poses for 36 ligands (6 HSP90α inhibitors, 30 MAP4K4 inhibitors) prior to the release of the corresponding protein–ligand X-ray structures. When applied to the HSP90α dataset, for which many protein–ligand X-ray structures are already available, GRIM provided very high quality solutions (mean rmsd = 1.06 Å, n = 6) as top-ranked poses, and significantly outperformed a state-of-the-art scoring function. In the case of MAP4K4 inhibitors, for which preexisting 3D knowledge is scarce and chemical diversity is much larger, the accuracy of GRIM poses decays (mean rmsd = 3.18 Å, n = 30) although GRIM still outperforms an energy-based scoring function. GRIM rescoring appears to be quite robust with comparison to the other approaches competing for the same challenge (42 submissions for the HSP90 dataset, 27 for the MAP4K4 dataset) as it ranked 3rd and 2nd respectively, for the two investigated datasets. The rescoring method is quite simple to implement, independent on a docking engine, and applicable to any target for which at least one holo X-ray structure is available.     

Fingerprint directed scaffold hopping for identification of CCR2 antagonists

Chemokine receptors have evolved as attractive targets for disease conditions which arise due to immunomodulation involving host-defense mechanisms. CCR2, a chemokine receptor, is targeted for diseases like arthritis, multiple sclerosis, vascular disease, obesity, and type 2 diabetes. This study provides a new strategy of a ligand based technique which exploits fingerprint led fragment features in conjunction with structure-guided design for identifying new scaffolds for CCR2. A fragment based mining (FBM) technique was employed on a chemical database to identify novel scaffold hops. The hits were subjected to 3-point pharmacophore fingerprint procedures with Tanimoto similarity metric to compare pharmacophoric fingerprints. The final 66 hits generated by these exercises were predicted by the validated HQSAR model, and the top predicted were suggested as probable scaffolds for CCR2 antagonism. The identified scaffolds were validated through molecular docking studies. The ligands were docked by providing receptor flexibility in the extra cellular domain (1 and 3), N terminal domain, and in the transmembrane (TM1 & TM7) helix region with IFD approach. Some of the scaffolds showed H-bonding potential which was not explored by the data set molecules. All identified scaffolds highlighted a key hydrogen bonding interaction with Thr292 as supported by mutational studies. The observed pi stacking interaction with Tyr188 in data set molecules was also produced by the new scaffolds. Taking the advantage of receptor flexibility the scaffolds explored the hydrophobic binding cleft between helix 1 and 7 occupied by residues Leu44, Leu45, Leu48 and Ile300, Ile303, Ile304, respectively. Two of the identified molecules have promising outcomes and can be considered as novel scaffolds for CCR2 binding.     

Comparison of two implementations of the incremental construction algorithm in flexible docking of thrombin inhibitors

A set of 32 known thrombin inhibitors representing different chemical classes has been used to evaluate the performance of two implementations of incremental construction algorithms for flexible molecular docking: DOCK 4.0 and FlexX 1.5. Both docking tools are able to dock 10–35% of our test set within 2 Å of their known, bound conformations using default sampling and scoring parameters. Although flexible docking with DOCK or FlexX is not able to reconstruct all native complexes, it does offer a significant improvement over rigid body docking of single, rule-based conformations, which is still often used for docking of large databases. Docking of sets of multiple conformers of each inhibitor, obtained with a novel protocol for diverse conformer generation and selection, yielded results comparable to those obtained by flexible docking. Chemical scoring, which is an empirically modified force field scoring method implemented in DOCK 4.0, outperforms both interaction energy scoring by DOCK and the Böhm scoring function used by FlexX in rigid and flexible docking of thrombin inhibitors. Our results indicate that for reliable docking of flexible ligands the selection of anchor fragments, conformational sampling and currently available scoring methods still require improvement.     

Knowledge-based interaction fingerprint scoring: a simple method for improving the effectiveness of fast scoring functions

A new method for the postprocessing of docking outputs has been developed, based on encoding putative 3D binding modes (docking solutions) as ligand-protein interactions into simple bit strings, a method analogous to the structural interaction fingerprint. Instead of employing traditional scoring functions, the method uses a series of new, knowledge-based scores derived from the similarity of the bit strings for each docking solution to that of a known reference binding mode. A GOLD docking study was carried out using the Bissantz estrogen receptor antagonist set along with the new scoring method. Superior recovery rates, with up to 2-fold enrichments, were observed when the new knowledge-based scoring was compared to the GOLD fitness score. In addition, top ranking sets of molecules (actives and potential actives or decoys) were structurally diverse with low molecular weights and structural complexities. Principal component analysis and clustering of the fingerprints permits the easy separation of active from inactive binding modes and the visualization of diverse binding modes.     

Docking and scoring with ICM: the benchmarking results and strategies for improvement

Flexible docking and scoring using the internal coordinate mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2?? RMSD found in 91 and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC?=?82.2 and ROC((2%))?=?45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target.     

Binding energy landscape analysis helps to discriminate true hits from high-scoring decoys in virtual screening

Although virtual screening through molecular docking has been widely applied in lead discovery, it is still challenging to distinguish true hits from high-scoring decoys because of the difficulty in accurately predicting protein-ligand binding affinities. Following the successful application of energy landscape analysis to both protein folding and biomolecular binding studies, we attempted to use protein-ligand binding energy landscape analysis to recognize true binders from high-scoring decoys. Two parameters describing the binding energy landscape were used for this purpose. The energy gap, defined as the difference between the binding energy of the native binding mode and the average binding energy of other binding modes in the "denatured binding phase", was used to describe the thermodynamic stability of binding, and the number of local binding wells in the landscapes was used to account for the kinetic accessibility. These parameters, together with the docking score, were combined using logistic regression to investigate their capability to discriminate true ligands from high-scoring decoys. Inhibitors and the noninhibitors of two enzyme systems, neuraminidase and cyclooxygenase-2, were used to test their discrimination capability. Using a five-fold cross-validation, the areas under the receiver operator characteristic curves (AUCs) from the best linear combinations of parameters reached 0.878 for neuraminidase and 0.776 for cyclooxygenase-2. To make a more independent test, inhibitors and high-scoring decoys in a directory of useful decoys (DUD), the largest and most comprehensive public data set for benchmarking virtual screen programs by far, were used as independent test sets to test the discrimination capability of these parameters. The AUCs of the best linear combinations of parameters for the independent test sets were 0.750 for neuraminidase and 0.855 for cyclooxygenase-2. Furthermore, combining these two parameters with the docking scoring function improved the enrichment ratio to 200-300% compared to that using the scoring function alone. This study suggests that incorporating information from binding energy landscape analysis can significantly increase the success rate of virtual screening.     

Validation studies of the site-directed docking program LibDock

The performance of the site-features docking algorithm LibDock has been evaluated across eight GlaxoSmithKline targets as a follow-up to a broad validation study of docking and scoring software (Warren, G. L.; Andrews, W. C.; Capelli, A.; Clarke, B.; Lalonde, J.; Lambert, M. H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Walls, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. J. Med. Chem. 2006, 49, 5912-5931). Docking experiments were performed to assess both the accuracy in reproducing the binding mode of the ligand and the retrieval of active compounds in a virtual screening protocol using both the DJD (Diller, D. J.; Merz, K. M., Jr. Proteins 2001, 43, 113-124) and LigScore2 (Krammer, A. K.; Kirchoff, P. D.; Jiang, X.; Venkatachalam, C. M.; Waldman, M. J. Mol. Graphics Modell. 2005, 23, 395-407) scoring functions. This study was conducted using DJD scoring, and poses were rescored using all available scoring functions in the Accelrys LigandFit module, including LigScore2. For six out of eight targets at least 30% of the ligands were docked within a root-mean-square difference (RMSD) of 2.0 A for the crystallographic poses when the LigScore2 scoring function was used. LibDock retrieved at least 20% of active compounds in the top 10% of screened ligands for four of the eight targets in the virtual screening protocol. In both studies the LigScore2 scoring function enhanced the retrieval of crystallographic poses or active compounds in comparison with the results obtained using the DJD scoring function. The results for LibDock accuracy and ligand retrieval in virtual screening are compared to 10 other docking and scoring programs. These studies demonstrate the utility of the LigScore2 scoring function and that LibDock as a feature directed docking method performs as well as docking programs that use genetic/growing and Monte Carlo driven algorithms.     

BALLDock/SLICK: a new method for protein-carbohydrate docking

Protein-ligand docking is an essential technique in computer-aided drug design. While generally available docking programs work well for most drug classes, carbohydrates and carbohydrate-like compounds are often problematic for docking. We present a new docking method specifically designed to handle docking of carbohydrate-like compounds. BALLDock/SLICK combines an evolutionary docking algorithm for flexible ligands and flexible receptor side chains with carbohydrate-specific scoring and energy functions. The scoring function has been designed to identify accurate ligand poses, while the energy function yields accurate estimates of the binding free energies of these poses. On a test set of known protein-sugar complexes we demonstrate the ability of the approach to generate correct poses for almost all of the structures and achieve very low mean errors for the predicted binding free energies.     

Identifying the binding mode of a molecular scaffold

We describe a method for docking of a scaffold-based series and present its advantages over docking of individual ligands, for determining the binding mode of a molecular scaffold in a binding site. The method has been applied to eight different scaffolds of protein kinase inhibitors (PKI). A single analog of each of these eight scaffolds was previously crystallized with different protein kinases. We have used FlexX to dock a set of molecules that share the same scaffold, rather than docking a single molecule. The main mode of binding is determined by the mode of binding of the largest cluster among the docked molecules that share a scaffold. Clustering is based on our 'nearest single neighbor' method [J. Chem. Inf. Comput. Sci., 43 (2003) 208-217]. Additional criteria are applied in those cases in which more than one significant binding mode is found. Using the proposed method, most of the crystallographic binding modes of these scaffolds were reconstructed. Alternative modes, that have not been detected yet by experiments, could also be identified. The method was applied to predict the binding mode of an additional molecular scaffold that was not yet reported and the predicted binding mode has been found to be very similar to experimental results for a closely related scaffold. We suggest that this approach be used as a virtual screening tool for scaffold-based design processes.     

Are predefined decoy sets of ligand poses able to quantify scoring function accuracy?

Due to the large number of different docking programs and scoring functions available, researchers are faced with the problem of selecting the most suitable one when starting a structure-based drug discovery project. To guide the decision process, several studies comparing different docking and scoring approaches have been published. In the context of comparing scoring function performance, it is common practice to use a predefined, computer-generated set of ligand poses (decoys) and to reevaluate their score using the set of scoring functions to be compared. But are predefined decoy sets able to unambiguously evaluate and rank different scoring functions with respect to pose prediction performance? This question arose when the pose prediction performance of our piecewise linear potential derived scoring functions (Korb et al. in J Chem Inf Model 49:84–96, 2009) was assessed on a standard decoy set (Cheng et al. in J Chem Inf Model 49:1079–1093, 2009). While they showed excellent pose identification performance when they were used for rescoring of the predefined decoy conformations, a pronounced degradation in performance could be observed when they were directly applied in docking calculations using the same test set. This implies that on a discrete set of ligand poses only the rescoring performance can be evaluated. For comparing the pose prediction performance in a more rigorous manner, the search space of each scoring function has to be sampled extensively as done in the docking calculations performed here. We were able to identify relative strengths and weaknesses of three scoring functions (ChemPLP, GoldScore, and Astex Statistical Potential) by analyzing the performance for subsets of the complexes grouped by different properties of the active site. However, reasons for the overall poor performance of all three functions on this test set compared to other test sets of similar size could not be identified.     

Significant enhancement of docking sensitivity using implicit ligand sampling

The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the 'ligand-model' concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.