Protein flexibility in ligand docking and virtual screening to protein kinases

The main complicating factor in structure-based drug design is receptor rearrangement upon ligand binding (induced fit). It is the induced fit that complicates cross-docking of ligands from different ligand-receptor complexes. Previous studies have shown the necessity to include protein flexibility in ligand docking and virtual screening. Very few docking methods have been developed to predict the induced fit reliably and, at the same time, to improve on discriminating between binders and non-binders in the virtual screening process.We present an algorithm called the ICM-flexible receptor docking algorithm (IFREDA) to account for protein flexibility in virtual screening. By docking flexible ligands to a flexible receptor, IFREDA generates a discrete set of receptor conformations, which are then used to perform flexible ligand-rigid receptor docking and scoring. This is followed by a merging and shrinking step, where the results of the multiple virtual screenings are condensed to improve the enrichment factor. In the IFREDA approach, both side-chain rearrangements and essential backbone movements are taken into consideration, thus sampling adequately the conformational space of the receptor, even in cases of large loop movements.As a preliminary step, to show the importance of incorporating protein flexibility in ligand docking and virtual screening, and to validate the merging and shrinking procedure, we compiled an extensive small-scale virtual screening benchmark of 33 crystal structures of four different protein kinases sub-families (cAPK, CDK-2, P38 and LCK), where we obtained an enrichment factor fold-increase of 1.85±0.65 using two or three multiple experimental conformations. IFREDA was used in eight protein kinase complexes and was able to find the correct ligand conformation and discriminate the correct conformations from the “misdocked” conformations solely on the basis of energy calculation. Five of the generated structures were used in the small-scale virtual screening stage and, by merging and shrinking the results with those of the original structure, we show an enrichment factor fold increase of 1.89±0.60, comparable to that obtained using multiple experimental conformations.Our cross-docking tests on the protein kinase benchmark underscore the necessity of incorporating protein flexibility in both ligand docking and virtual screening. The methodology presented here will be extremely useful in cases where few or no experimental structures of complexes are available, while some binders are known.     

Flexible ligand docking to multiple receptor conformations: a practical alternative

State of the art docking algorithms predict an incorrect binding pose for about 50-70% of all ligands when only a single fixed receptor conformation is considered. In many more cases, lack of receptor flexibility results in meaningless ligand binding scores, even when the correct pose is obtained. Incorporating conformational rearrangements of the receptor binding pocket into predictions of both ligand binding pose and binding score is crucial for improving structure-based drug design and virtual ligand screening methodologies. However, direct modeling of protein binding site flexibility remains challenging because of the large conformational space that must be sampled, and difficulties remain in constructing a suitably accurate energy function. Here we show that using multiple fixed receptor conformations, either experimentally determined by crystallography or NMR, or computationally generated, is a practical shortcut that may improve docking calculations. In several cases, such an approach has led to experimentally validated predictions.     

Structural adaptation of extreme halophilic proteins through decrease of conserved hydrophobic contact surface

Background

Disrupting protein-protein interactions by small organic molecules is nowadays a promising strategy employed to block protein targets involved in different pathologies. However, structural changes occurring at the binding interfaces make difficult drug discovery processes using structure-based drug design/virtual screening approaches. Here we focused on two homologous calcium binding proteins, calmodulin and human centrin 2, involved in different cellular functions via protein-protein interactions, and known to undergo important conformational changes upon ligand binding.

Results

In order to find suitable protein conformations of calmodulin and centrin for further structure-based drug design/virtual screening, we performed in silico structural/energetic analysis and molecular docking of terphenyl (a mimicking alpha-helical molecule known to inhibit protein-protein interactions of calmodulin) into X-ray and NMR ensembles of calmodulin and centrin. We employed several scoring methods in order to find the best protein conformations. Our results show that docking on NMR structures of calmodulin and centrin can be very helpful to take into account conformational changes occurring at protein-protein interfaces.

Conclusions

NMR structures of protein-protein complexes nowadays available could efficiently be exploited for further structure-based drug design/virtual screening processes employed to design small molecule inhibitors of protein-protein interactions.     

Ligand-induced conformational changes: improved predictions of ligand binding conformations and affinities

A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein tyrosine phosphatase 1B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal structure cocrystallized with a hexapeptide. The estimated binding energies for various docking modes as well as the RMS differences between the docked compounds and the crystallographic structure were calculated. In this scenario the estimated binding energies were not predictive inasmuch as docking modes with low estimated binding energies corresponded to relatively large RMS differences when aligned with the corresponding crystal structure. Secondly, the inhibitors were docked to their parent protein structures in which they were cocrystallized. In this case, there was a good correlation between low predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side chains exposed to the active site were considered in their allowed rotamer conformations and protein models containing all possible combinations of side-chain rotamers were generated. To evaluate which of these modeled active sites is the most likely binding site conformation for a certain inhibitor, the inhibitors were docked against all active site models. The receptor rotamer model corresponding to the lowest estimated binding energy is taken as the top candidate. Using this protocol, correct inhibitor binding modes could successfully be discriminated from proposed incorrect binding modes. Moreover, the ranking of the estimated ligand binding energies was in good agreement with experimentally observed binding affinities.     

Docking multiple conformations of a flexible ligand into a protein binding site using NMR restraints

A method is described for docking a large, flexible ligand using intra-ligand conformational restraints from exchange-transferred NOE (etNOE) data. Numerous conformations of the ligand are generated in isolation, and a subset of representative conformations is selected. A crude model of the protein-ligand complex is used as a template for overlaying the selected ligand structures, and each complex is conformationally relaxed by molecular mechanics to optimize the interaction. Finally, the complexes were assessed for structural quality. Alternative approaches are described for the three steps of the method: generation of the initial docking template; selection of a subset of ligand conformations; and conformational sampling of the complex. The template is generated either by manual docking using interactive graphics or by a computational grid-based search of the binding site. A subset of conformations from the total number of peptides calculated in isolation is selected based on either low energy and satisfaction of the etNOE restraints, or a cluster analysis of the full set. To optimize the interactions in the complex, either a restrained Monte Carlo-energy minimization (MCM) protocol or a restrained simulated annealing (SA) protocol were used. This work produced 53 initial complexes of which 8 were assessed in detail. With the etNOE conformational restraints, all of the approaches provide reasonable models. The grid-based approach to generate an initial docking template allows a large volume to be sampled, and as a result, two distinct binding modes were identified for a fifteen-residue peptide binding to an enzyme active site.     

Identification of selective MMP-9 inhibitors through multiple e-pharmacophore,ligand-based pharmacophore,molecular docking,and density functional theory approaches

Matrix metalloproteinase-9 (MMP-9) is a significant target for the development of drugs for the treatment of arthritis, CNS disorders, and cancer metastasis. The structure-based and ligand-based methods were used for the virtual screening (VS) of database compounds to obtain potent and selective MMP-9 inhibitors. Experimentally known MMP-9 inhibitors were used to grow up ligand-based three pharmacophore models utilizing Schrodinger suite. The X-ray crystallographic structures of MMP-9 with different inhibitors were used to develop five energy-optimized structure-based (e-pharmacophore) models. All developed pharmacophores were validated and applied to screen the Zinc database. Pharmacophore matched compounds were subjected to molecular docking to retrieve hits with novel scaffolds. The molecules with diverse structures, high docking scores and low binding energies for various crystal structures of MMP-9, were selected as final hits. The Induced fit docking (IFD) analysis provided significant information about the driving of inhibitor to approve a suitable bioactive conformational position in the active site of protein. Since charge transfer reaction occurs during receptor–ligand interaction, therefore, electronic features of hits (ligands) are interesting parameters to explain the binding interactions. Density functional theory (DFT) at B3LYP/6-31G* level was utilized to explore electronic features of hits. The docking study of hits using AutoDock was helpful to establish the binding interactions. The study illustrates that the combined pharmacophore approach is advantageous to identify diverse hits which have better binding affinity to the active site of the enzyme for all possible bioactive conformations. The approach used in the study is worthy to design drugs for other targets.     

Accounting for global protein deformability during protein-protein and protein-ligand docking

Computational docking methods are valuable tools aimed to simplify the costly process of drug development and improvement. Most current approaches assume a rigid receptor structure to allow virtual screening of large numbers of possible ligands and putative binding sites on a receptor molecule. However, inclusion of receptor flexibility can be of critical importance since binding of a ligand can lead to changes in the receptor protein conformation that are sterically necessary to accommodate a ligand. Recent approaches to efficiently account for receptor flexibility during docking simulations are reviewed. In particular, accounting efficiently for global conformational changes of the protein backbone during docking is a still challenging unsolved problem. An approximate method has recently been suggested that is based on relaxing the receptor conformation during docking in pre-calculated soft collective degrees of freedom (M. Zacharias, Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: binding of FK506 to FKBP, Proteins: Struct., Funct., Genet. 54 (2004) 759-767). Test applications on protein-protein docking and on docking the inhibitor staurosporine to the apo-form of cAMP-dependent protein kinase A catalytic domain indicate significant improvement of docking results compared to rigid docking at a very modest computational demand. Accounting for receptor conformational changes in pre-calculated global degrees of freedom might offer a promising route to improve systematic docking screening simulations.     

Testing a flexible-receptor docking algorithm in a model binding site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.     

Protein‐based virtual screening of chemical databases. II. Are homology models of g‐protein coupled receptors suitable targets?

The aim of the current study is to investigate whether homology models of G-Protein-Coupled Receptors (GPCRs) that are based on bovine rhodopsin are reliable enough to be used for virtual screening of chemical databases. Starting from the recently described 2.8 A-resolution X-ray structure of bovine rhodopsin, homology models of an "antagonist-bound" form of three human GPCRs (dopamine D3 receptor, muscarinic M1 receptor, vasopressin V1a receptor) were constructed. The homology models were used to screen three-dimensional databases using three different docking programs (Dock, FlexX, Gold) in combination with seven scoring functions (ChemScore, Dock, FlexX, Fresno, Gold, Pmf, Score). Rhodopsin-based homology models turned out to be suitable, indeed, for virtual screening since known antagonists seeded in the test databases could be distinguished from randomly chosen molecules. However, such models are not accurate enough for retrieving known agonists. To generate receptor models better suited for agonist screening, we developed a new knowledge- and pharmacophore-based modeling procedure that might partly simulate the conformational changes occurring in the active site during receptor activation. Receptor coordinates generated by this new procedure are now suitable for agonist screening. We thus propose two alternative strategies for the virtual screening of GPCR ligands, relying on a different set of receptor coordinates (antagonist-bound and agonist-bound states).     

A flexible docking scheme to explore the binding selectivity of PDZ domains

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTA LIGAND , we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density‐95/Dlg/ZO‐1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.     

Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock

The formation of specific protein-protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein-docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross-docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound-bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials ), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross-docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.     

Conformational transition in the substrate binding domain of β-secretase exploited by NMA and its implication in inhibitor recognition: BACE1-myricetin a case study

BACE1 is a key protease involved in the proteolysis of amyloid precursor protein (APP) that generates a toxic peptide amyloid beta (Aβ), a pathological feature of Alzheimer's disease (AD). The enzyme is believed to possess an open and a closed conformation that corresponds to its free and inhibitor-bound form respectively. Here, we study the dynamic transition of BACE1 employing normal mode analysis (NMA) using a simplified elastic network model (ENM). Estimation of the catalytic cavity volume on the structures of BACE1 encoded by the lowest frequency normal mode reveals the dynamical transition of the enzyme from the open to the closed conformer. Detailed analysis reveals that concerted movement of different loop segments in the active site of the protein, namely flap regions, 10s loop, A loop and F loop, squeeze the catalytic cavity between the N-terminal and C-terminal lobe of the substrate binding domain of BACE1. We also propose that the NMA encoded multiple receptor conformations (MRC) of BACE1 elucidate the pharmacophoric feature necessary to inhibit the enzyme by a polyphenol, myricetin. van der Waals interaction is found to be the main driving force that guides the ligand induced conformational switching to the closed conformer. We suggest that NMA derived MRC of BACE1 is an efficient way to treat the receptor flexibility in docking and thus can be further applied in virtual screening and structure based drug design.     

Small molecule induction of MSH2-dependent cell death suggests a vital role of mismatch repair proteins in cell death

Avoidance of apoptosis is one of the hallmarks of cancer development and progression. Chemotherapeutic agents aim to initiate an apoptotic response, but often fail due to dysregulation. MSH proteins are capable of recognizing cisplatin damage in DNA and participate in the initiation of cell death. We have exploited this recognition and computationally simulated a MutS homolog (MSH) "death conformation". Screening and docking experiments based on this model determined that the MSH2-dependent cell-death pathway can be induced by a small molecule without DNA damage, reserpine. Reserpine was identified via virtual screening on structures obtained from molecular dynamics as a small molecule that selectively binds a protein "death" conformation. The virtual screening predicts that this small molecule binds in the absence of DNA. Cell biology confirmed that reserpine triggers the MSH2-dependent cell-death pathway. This result supports the hypothesis that the MSH2-dependent pathway is initiated by specific protein conformational changes triggered by binding to either DNA damage or small compound molecules. These findings have multiple implications for drug discovery and cell biology. Computational modeling may be used to identify and eventually design small molecules that selectively activate particular pathways through conformational control. Molecular dynamics simulations can be used to model the biologically relevant conformations and virtual screening can then be used to select for small molecules that bind specific conformations. The ability of a small molecule to induce the cell-death pathway suggests a broader role for MMR proteins in cellular events, such as cell-death pathways, than previously suspected.     

Molecular Dynamics Simulations of Sonic Hedgehog-Receptor and Inhibitor Complexes and Their Applications for Potential Anticancer Agent Discovery

The sonic hedgehog (Shh) signaling pathway is necessary for a variety of development and differentiation during embryogenesis as well as maintenance and renascence of diverse adult tissues. However, an abnormal activation of the signaling pathway is related to various cancers. In this pathway, the Shh signaling transduction is facilitated by binding of Shh to its receptor protein, Ptch. In this study, we modeled the 3D structure of functionally important key loop peptides of Ptch based on homologous proteins. Using this loop model, the molecular interactions between the structural components present in the pseudo-active site of Shh and key residues of Ptch was investigated in atomic level through molecular dynamics (MD) simulations. For the purpose of developing inhibitor candidates of the Shh signaling pathway, the Shh pseudo-active site of this interface region was selected as a target to block the direct binding between Shh and Ptch. Two different structure-based pharmacophore models were generated considering the key loop of Ptch and known inhibitor-induced conformational changes of the Shh through MD simulations. Finally two hit compounds were retrieved through a series of virtual screening combined with molecular docking simulations and we propose two hit compounds as potential inhibitory lead candidates to block the Shh signaling pathway based on their strong interactions to receptor or inhibitor induced conformations of the Shh.     

Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.     

Efficacy and selectivity in flexible database docking

Flexible database docking with DOCK 4.0 has been evaluated for its ability to retrieve biologically active molecules from a database of approximately 1,000 compounds with known activities against thrombin and the progesterone receptor. The retrieval of known actives and chemically similar but inactive molecules was monitored as a function of conformational and orientational sampling. The largest enrichment of actives among the 10% highest ranking molecules is obtained when only five conformations are used to seed the next round of ligand reconstruction and limited sampling is applied to place the base fragment in the binding site. The performance of energy and chemical scoring, as implemented in DOCK 4.0, was found to depend on the protein used for docking. For the progesterone receptor, energy scoring yields the largest enrichments (64%) in terms of actives retrieved among the 10% top scoring molecules, while chemical scoring performs best for thrombin (94%). With the exception of the application of energy scoring to the progesterone receptor, both energy-based scoring schemes applied in this study do not discriminate well between true actives and chemically similar but inactive compounds. In conclusion, flexible docking is able to effectively prioritize high-throughput screening databases, using less conformational sampling than normally required for appropriate reconstruction of protein-ligand complexes. The more subtle discrimination between chemically similar classes of active and inactive compounds remains, however, problematic.     

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.     

ReFlexIn: A Flexible Receptor Protein-Ligand Docking Scheme Evaluated on HIV-1 Protease

For many targets of pharmaceutical importance conformational changes of the receptor protein are relevant during the ligand binding process. A new docking approach, ReFlexIn (Receptor Flexibility by Interpolation), that combines receptor flexibility with the computationally efficient potential grid representation of receptor molecules has been evaluated on the retroviral HIV-1 (Human Immunodeficiency Virus 1) protease system. An approximate inclusion of receptor flexibility is achieved by using interpolation between grid representations of individual receptor conformations. For the retroviral protease the method was tested on an ensemble of protease structures crystallized in the presence of different ligands and on a set of structures obtained from morphing between the unbound and a ligand-bound protease structure. Docking was performed on ligands known to bind to the protease and several non-binders. For the binders the ReFlexIn method yielded in almost all cases ligand placements in similar or closer agreement with experiment than docking to any of the ensemble members without degrading the discrimination with respect to non-binders. The improved docking performance compared to docking to rigid receptors allows for systematic virtual screening applications at very small additional computational cost.     

Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking

One approach to incorporate protein flexibility in molecular docking is the use of an ensemble consisting of multiple protein structures. Sequentially docking each ligand into a large number of protein structures is computationally too expensive to allow large-scale database screening. It is challenging to achieve a good balance between docking accuracy and computational efficiency. In this work, we have developed a fast, novel docking algorithm utilizing multiple protein structures, referred to as ensemble docking, to account for protein structural variations. The algorithm can simultaneously dock a ligand into an ensemble of protein structures and automatically select an optimal protein structure that best fits the ligand by optimizing both ligand coordinates and the conformational variable m, where m represents the m-th structure in the protein ensemble. The docking algorithm was validated on 10 protein ensembles containing 105 crystal structures and 87 ligands in terms of binding mode and energy score predictions. A success rate of 93% was obtained with the criterion of root-mean-square deviation <2.5 A if the top five orientations for each ligand were considered, comparable to that of sequential docking in which scores for individual docking are merged into one list by re-ranking, and significantly better than that of single rigid-receptor docking (75% on average). Similar trends were also observed in binding score predictions and enrichment tests of virtual database screening. The ensemble docking algorithm is computationally efficient, with a computational time comparable to that for docking a ligand into a single protein structure. In contrast, the computational time for the sequential docking method increases linearly with the number of protein structures in the ensemble. The algorithm was further evaluated using a more realistic ensemble in which the corresponding bound protein structures of inhibitors were excluded. The results show that ensemble docking successfully predicts the binding modes of the inhibitors, and discriminates the inhibitors from a set of noninhibitors with similar chemical properties. Although multiple experimental structures were used in the present work, our algorithm can be easily applied to multiple protein conformations generated by computational methods, and helps improve the efficiency of other existing multiple protein structure(MPS)-based methods to accommodate protein flexibility.