Rescoring docking hit lists for model cavity sites: predictions and experimental testing

Molecular docking computationally screens thousands to millions of organic molecules against protein structures, looking for those with complementary fits. Many approximations are made, often resulting in low “hit rates.” A strategy to overcome these approximations is to rescore top-ranked docked molecules using a better but slower method. One such is afforded by molecular mechanics-generalized Born surface area (MM-GBSA) techniques. These more physically realistic methods have improved models for solvation and electrostatic interactions and conformational change compared to most docking programs. To investigate MM-GBSA rescoring, we re-ranked docking hit lists in three small buried sites: a hydrophobic cavity that binds apolar ligands, a slightly polar cavity that binds aryl and hydrogen-bonding ligands, and an anionic cavity that binds cationic ligands. These sites are simple; consequently, incorrect predictions can be attributed to particular errors in the method, and many likely ligands may actually be tested. In retrospective calculations, MM-GBSA techniques with binding-site minimization better distinguished the known ligands for each cavity from the known decoys compared to the docking calculation alone. This encouraged us to test rescoring prospectively on molecules that ranked poorly by docking but that ranked well when rescored by MM-GBSA. A total of 33 molecules highly ranked by MM-GBSA for the three cavities were tested experimentally. Of these, 23 were observed to bind—these are docking false negatives rescued by rescoring. The 10 remaining molecules are true negatives by docking and false positives by MM-GBSA. X-ray crystal structures were determined for 21 of these 23 molecules. In many cases, the geometry prediction by MM-GBSA improved the initial docking pose and more closely resembled the crystallographic result; yet in several cases, the rescored geometry failed to capture large conformational changes in the protein. Intriguingly, rescoring not only rescued docking false positives, but also introduced several new false positives into the top-ranking molecules. We consider the origins of the successes and failures in MM-GBSA rescoring in these model cavity sites and the prospects for rescoring in biologically relevant targets.     

Efficient molecular docking of NMR structures: application to HIV-1 protease

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.     

Improving accuracy and efficiency of blind protein-ligand docking by focusing on predicted binding sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein-ligand complexes and 19 ligand-free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand-binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure-based functional annotation of proteins.     

Dopamine transporter comparative molecular modeling and binding site prediction using the LeuT(Aa) leucine transporter as a template

Pharmacological and behavioral studies indicate that binding of cocaine and the amphetamines by the dopamine transporter (DAT) protein is principally responsible for initiating the euphoria and addiction associated with these drugs. The lack of an X-ray crystal structure for the DAT or any other member of the neurotransmitter:sodium symporter (NSS) family has hindered understanding of psychostimulant recognition at the atomic level; structural information has been obtained largely from mutagenesis and biophysical studies. The recent publication of a crystal structure for the bacterial leucine transporter LeuT(Aa), a distantly related NSS family homolog, provides for the first time a template for three-dimensional comparative modeling of NSS proteins. A novel computational modeling approach using the capabilities of the Molecular Operating Environment program MOE 2005.06 in conjunction with other comparative modeling servers generated the LeuT(Aa)-directed DAT model. Probable dopamine and amphetamine binding sites were identified within the DAT model using multiple docking approaches. Binding sites for the substrate ligands (dopamine and amphetamine) overlapped substantially with the analogous region of the LeuT(Aa) crystal structure for the substrate leucine. The docking predictions implicated DAT side chains known to be critical for high affinity ligand binding and suggest novel mutagenesis targets in elucidating discrete substrate and inhibitor binding sites. The DAT model may guide DAT ligand QSAR studies, and rational design of novel DAT-binding therapeutics.     

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.     

ROSETTALIGAND: protein-small molecule docking with full side-chain flexibility

Protein-small molecule docking algorithms provide a means to model the structure of protein-small molecule complexes in structural detail and play an important role in drug development. In recent years the necessity of simulating protein side-chain flexibility for an accurate prediction of the protein-small molecule interfaces has become apparent, and an increasing number of docking algorithms probe different approaches to include protein flexibility. Here we describe a new method for docking small molecules into protein binding sites employing a Monte Carlo minimization procedure in which the rigid body position and orientation of the small molecule and the protein side-chain conformations are optimized simultaneously. The energy function comprises van der Waals (VDW) interactions, an implicit solvation model, an explicit orientation hydrogen bonding potential, and an electrostatics model. In an evaluation of the scoring function the computed energy correlated with experimental small molecule binding energy with a correlation coefficient of 0.63 across a diverse set of 229 protein- small molecule complexes. The docking method produced lowest energy models with a root mean square deviation (RMSD) smaller than 2 A in 71 out of 100 protein-small molecule crystal structure complexes (self-docking). In cross-docking calculations in which both protein side-chain and small molecule internal degrees of freedom were varied the lowest energy predictions had RMSDs less than 2 A in 14 of 20 test cases.     

Structure based design of heat shock protein 90 inhibitors acting as anticancer agents

Structure based drug design (SBDD) was used to discover heat shock protein 90 (HSP90) inhibitors useful in the treatment of cancer. By using the crystal structure of HSP90-ligand complex (1uyi), a docking model was prepared and was validated by external dataset containing known HSP90 inhibitors. This validated model was then used to virtually screen commercial databases, selected hits of which were bought and sent for real biological evaluation. Further as an alternative method, pharmacophores were generated using crystal structure conformations of ligands in HSP90 complexes (1uyi and 2bz5) and where used for virtual screening. Both cases yielded several hits containing novel scaffolds, particularly compound KHSP8 showed an IC(50) value of 0.902 μM in case of colon cancer (HT29), which is comparable to doxorubicin (0.828 μM). These compounds were being now used as leads for constructing small molecular libraries to get compounds with favourable pharmacokinetics and drug like properties.     

Flexible ligand docking using conformational ensembles.

Molecular docking algorithms suggest possible structures for molecular complexes. They are used to model biological function and to discover potential ligands. A present challenge for docking algorithms is the treatment of molecular flexibility. Here, the rigid body program, DOCK, is modified to allow it to rapidly fit multiple conformations of ligands. Conformations of a given molecule are pre-calculated in the same frame of reference, so that each conformer shares a common rigid fragment with all other conformations. The ligand conformers are then docked together, as an ensemble, into a receptor binding site. This takes advantage of the redundancy present in differing conformers of the same molecule. The algorithm was tested using three organic ligand protein systems and two protein-protein systems. Both the bound and unbound conformations of the receptors were used. The ligand ensemble method found conformations that resembled those determined in X-ray crystal structures (RMS values typically less than 1.5 A). To test the method's usefulness for inhibitor discovery, multi-compound and multi-conformer databases were screened for compounds known to bind to dihydrofolate reductase and compounds known to bind to thymidylate synthase. In both cases, known inhibitors and substrates were identified in conformations resembling those observed experimentally. The ligand ensemble method was 100-fold faster than docking a single conformation at a time and was able to screen a database of over 34 million conformations from 117,000 molecules in one to four CPU days on a workstation.     

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes

We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.     

Flexible protein-flexible ligand docking with disrupted velocity simulated annealing

By docking flexible balanol to a rigid model of protein kinase A (PKA), we found that a new simulated annealing protocol termed disrupted velocity simulated annealing (DIVE-SA) outperformed the replica-exchange method and the traditional simulated annealing method in identifying the correct docking pose. In this protocol, the atomic velocities were reassigned periodically to encourage the system to sample a large conformational space. We also found that scaling potential energy surface to reduce structural transition barriers could further facilitate docking. The DIVE-SA method was then evaluated on its ability to perform flexible ligand-flexible protein docking of three ligands (balanol, a balanol analog, and ATP) to PKA. To reduce computational time and to avoid possible unphysical structural changes resulting from the use of nonoptimal force fields, a soft restrain was applied to keep the root-mean-square-deviation (RMSD) between instantaneous protein structures and a chosen reference structure small. Because the restrain was applied to the overall RMSD rather than to individual atoms, a protein could still experience relatively large conformational changes during docking. To examine the impact of applying such a restrain on docking, we constructed two semi-flexible protein models by choosing two different crystal structures as reference. Both the balanol analog and ATP were able to dock to either one of these semi-flexible protein models. On the other hand, balanol could only dock well to one of them. Further analysis indicated that the restrain on the glycine-rich loop was too strong, preventing it to adjust its structure to accommodate balanol in the binding pocket of PKA. Removing the restrain on the glycine-rich loop resulted in much better docking poses. This finding demonstrates the important role that the flexibility of the glycine-rich loop play in accepting different ligands and should profitably not be restrained in molecular docking so that more diverse ligands can be studied.     

Structure-based identification of binding sites,native ligands and potential inhibitors for G-protein coupled receptors

G-protein coupled receptors (GPCRs) are the largest family of cell-surface receptors involved in signal transmission. Drugs associated with GPCRs represent more than one fourth of the 100 top-selling drugs and are the targets of more than half of the current therapeutic agents on the market. Our methodology based on the internal coordinate mechanics (ICM) program can accurately identify the ligand-binding pocket in the currently available crystal structures of seven transmembrane (7TM) proteins [bacteriorhodopsin (BR) and bovine rhodopsin (bRho)]. The binding geometry of the ligand can be accurately predicted by ICM flexible docking with and without the loop regions, a useful finding for GPCR docking because the transmembrane regions are easier to model. We also demonstrate that the native ligand can be identified by flexible docking and scoring in 1.5% and 0.2% (for bRho and BR, respectively) of the best scoring compounds from two different types of compound database. The same procedure can be applied to the database of available chemicals to identify specific GPCR binders. Finally, we demonstrate that even if the sidechain positions in the bRho binding pocket are entirely wrong, their correct conformation can be fully restored with high accuracy (0.28 A) through the ICM global optimization with and without the ligand present. These binding site adjustments are critical for flexible docking of new ligands to known structures or for docking to GPCR homology models. The ICM docking method has the potential to be used to "de-orphanize" orphan GPCRs (oGPCRs) and to identify antagonists-agonists for GPCRs if an accurate model (experimentally and computationally validated) of the structure has been constructed or when future crystal structures are determined.     

Accounting for ligand-bound metal ions in docking small molecules on adenylyl cyclase toxins

The adenylyl cyclase toxins produced by bacteria (such as the edema factor (EF) of Bacillus anthracis and CyaA of Bordetella pertussis) are important virulence factors in anthrax and whooping cough. Co-crystal structures of these proteins differ in the number and positioning of metal ions in the active site. Metal ions bound only to the ligands in the crystal structures are not included during the docking. To determine what effect these "missing" metals have on docking results, the AutoDock, LigandFit/Cerius2, and FlexX programs were compared for their ability to correctly place substrate analogues and inhibitors into the active sites of the crystal structures of EF, CyaA, and mammalian adenylate cyclase. Protonating the phosphates of substrate analogues improved the accuracy of docking into the active site of CyaA, where the grid did not account for one of the three Mg2+ ions in the crystal structure. The AutoDock ranking (based on docking energies) of a test group of compounds was relatively unaffected by protonation of carboxyl groups. However, the ranking by FlexX-ChemScore varied significantly, especially for docking to CyaA, suggesting that alternate protonation states should be tested when screening compound libraries with this program. When the charges on the bound metal were set correctly, AutoDock was the most reliable program of the three tested with respect to positioning substrate analogues and ranking compounds according to their experimentally determined ability to inhibit EF.     

Homology modeling of dopamine d(2) and d(3) receptors: molecular dynamics refinement and docking evaluation

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D(3) (hD(3)) receptor has been recently solved. Based on the hD(3) receptor crystal structure we generated dopamine D(2) and D(3) receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD(3) and hD(2L) receptors was differentiated by means of MD simulations and D(3) selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental K(i) was obtained for hD(3) and hD(2L) receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands.     

Analysis of a three-dimensional structure of human acidic mammalian chitinase obtained by homology modeling and ligand binding studies

The three-dimensional (3D) model of the human acidic mammalian chitinase (hAMCase) was constructed based on the crystal structure of the human chitotriosidase (EC 3.2.1.44, PDB code 1HKK) by using InsightII/Homology module. With the aid of molecular mechanics and molecular dynamics methods, the last refined model was obtained and further assessed by Profile-3D and Procheck, which confirms that the refined model is reliable. Furthermore, the docking results of the ligands (allosamidin and NAG₂) into the active site of hAMCase indicate that allosamidin is a more preferred ligand than NAG₂, and that Glu119 forms hydrogen bond with allosamidin, which is in good agreement with the experimental results. From the docking studies, we also suggest that Trp10, Glu49, Asp192, and Glu276 in hAMCase are four important determinant residues in binding as they have strong van-der-Waals and electrostatic interactions with the ligand, respectively.     

FLIPDock: docking flexible ligands into flexible receptors

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.