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.     

How to choose relevant multiple receptor conformations for virtual screening: a test case of Cdk2 and normal mode analysis

Better treatment of protein flexibility is essential in structure-based drug design projects such as virtual screening and protein-ligand docking. Diversity in ligand-binding mechanisms and receptor conformational changes makes it difficult to treat dynamic features of the receptor during the docking simulation. Thus, the use of pregenerated multiple receptor conformations is applied today in virtual screening studies. However, generation of a small relevant set of receptor conformations remains challenging. To address this problem, we propose a new protocol for the generation of multiple receptor conformations via normal mode analysis and for the selection of several receptor conformations suitable for docking/virtual screening. We validated this protocol on cyclin-dependent kinase 2, which possesses a binding site located at the interface between two subdomains and is known to undergo significant conformational changes in the active site region upon ligand binding. We believe that the suggested rules for the choice of suitable receptor conformations can be applied to other targets when dealing with in silico screening on flexible receptors.     

MS-DOCK: Accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening

Background  

The number of protein targets with a known or predicted tri-dimensional structure and of drug-like chemical compounds is growing rapidly and so is the need for new therapeutic compounds or chemical probes. Performing flexible structure-based virtual screening computations on thousands of targets with millions of molecules is intractable to most laboratories nor indeed desirable. Since shape complementarity is of primary importance for most protein-ligand interactions, we have developed a tool/protocol based on rigid-body docking to select compounds that fit well into binding sites.     

Scoring ligand similarity in structure‐based virtual screening

Scoring to identify high‐affinity compounds remains a challenge in virtual screening. On one hand, protein–ligand scoring focuses on weighting favorable and unfavorable interactions between the two molecules. Ligand‐based scoring, on the other hand, focuses on how well the shape and chemistry of each ligand candidate overlay on a three‐dimensional reference ligand. Our hypothesis is that a hybrid approach, using ligand‐based scoring to rank dockings selected by protein–ligand scoring, can ensure that high‐ranking molecules mimic the shape and chemistry of a known ligand while also complementing the binding site. Results from applying this approach to screen nearly 70 000 National Cancer Institute (NCI) compounds for thrombin inhibitors tend to support the hypothesis. EON ligand‐based ranking of docked molecules yielded the majority (4/5) of newly discovered, low to mid‐micromolar inhibitors from a panel of 27 assayed compounds, whereas ranking docked compounds by protein–ligand scoring alone resulted in one new inhibitor. Since the results depend on the choice of scoring function, an analysis of properties was performed on the top‐scoring docked compounds according to five different protein–ligand scoring functions, plus EON scoring using three different reference compounds. The results indicate that the choice of scoring function, even among scoring functions measuring the same types of interactions, can have an unexpectedly large effect on which compounds are chosen from screening. Furthermore, there was almost no overlap between the top‐scoring compounds from protein–ligand versus ligand‐based scoring, indicating the two approaches provide complementary information. Matchprint analysis, a new addition to the SLIDE (Screening Ligands by Induced‐fit Docking, Efficiently) screening toolset, facilitated comparison of docked molecules' interactions with those of known inhibitors. The majority of interactions conserved among top‐scoring compounds for a given scoring function, and from the different scoring functions, proved to be conserved interactions in known inhibitors. This was particularly true in the S1 pocket, which was occupied by all the docked compounds. Copyright © 2009 John Wiley & Sons, Ltd.     

New molecular scaffolds for the design of Mycobacterium tuberculosis type II dehydroquinase inhibitors identified using ligand and receptor based virtual screening

Using ligand and receptor based virtual screening approaches we have identified potential virtual screening hits targeting type II dehydroquinase from Mycobacterium tuberculosis, an effective and validated anti-mycobacterial target. Initially, we applied a virtual screening workflow based on a combination of 2D structural fingerprints, 3D pharmacophore and molecular docking to identify compounds that rigidly match specific aspects of ligand bioactive conformation. Subsequently, the resulting compounds were ranked and prioritized using receptor interaction fingerprint based scoring and quantitative structure activity relationship model developed using already known actives. The virtual screening hits prioritized belong to several classes of molecular scaffolds with several available substitution positions that could allow chemical modification to enhance binding affinity. Finally, identified hits may be useful to a medicinal chemist or combinatorial chemist to pick up the new molecular starting points for medicinal chemistry optimization for the design of novel type II dehydroquinase inhibitors.     

Mechanism for multiple ligand recognition by the human transferrin receptor

Transferrin receptor 1 (TfR) plays a critical role in cellular iron import for most higher organisms. Cell surface TfR binds to circulating iron-loaded transferrin (Fe-Tf) and transports it to acidic endosomes, where low pH promotes iron to dissociate from transferrin (Tf) in a TfR-assisted process. The iron-free form of Tf (apo-Tf) remains bound to TfR and is recycled to the cell surface, where the complex dissociates upon exposure to the slightly basic pH of the blood. Fe-Tf competes for binding to TfR with HFE, the protein mutated in the iron-overload disease hereditary hemochromatosis. We used a quantitative surface plasmon resonance assay to determine the binding affinities of an extensive set of site-directed TfR mutants to HFE and Fe-Tf at pH 7.4 and to apo-Tf at pH 6.3. These results confirm the previous finding that Fe-Tf and HFE compete for the receptor by binding to an overlapping site on the TfR helical domain. Spatially distant mutations in the TfR protease-like domain affect binding of Fe-Tf, but not iron-loaded Tf C-lobe, apo-Tf, or HFE, and mutations at the edge of the TfR helical domain affect binding of apo-Tf, but not Fe-Tf or HFE. The binding data presented here reveal the binding footprints on TfR for Fe-Tf and apo-Tf. These data support a model in which the Tf C-lobe contacts the TfR helical domain and the Tf N-lobe contacts the base of the TfR protease-like domain. The differential effects of some TfR mutations on binding to Fe-Tf and apo-Tf suggest differences in the contact points between TfR and the two forms of Tf that could be caused by pH-dependent conformational changes in Tf, TfR, or both. From these data, we propose a structure-based model for the mechanism of TfR-assisted iron release from Fe-Tf.     

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.     

Discovery of cannabinoid-1 receptor antagonists by virtual screening

In this work, we tried to find a new scaffold for a CB1 receptor antagonist using virtual screening. We first analyzed structural features for the known cannabinoid-1 receptor antagonists and, then, we built pharmacophore models using the HipHop concept and carried out a docking study based on our homology CB1 receptor 3D structure. The most active compound, including thiazole-4-one moiety, showed an activity value of 125 nM IC₅₀, with a good PK profile.     

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.     

Structure-based virtual screening for insect ecdysone receptor ligands using MM/PBSA

The ecdysone receptor (EcR) is an insect nuclear receptor that is activated by the molting hormone, 20-hydroxyecdysone. Because synthetic EcR ligands disrupt the normal growth of insects, they are attractive candidates for new insecticides. In this study, the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method was used to predict the binding activity of EcR ligands. Validity analyses using 40 known EcR ligands showed that the binding activity was satisfactorily predicted when the ligand conformational free energy term was introduced. Subsequently, this MM/PBSA method was applied to structure-based hierarchical virtual screening, and 12 candidate compounds were selected from a database of 3.8 million compounds. Five of these compounds were active in a cell-based competitive binding assay. The most potent compound is a simple proline derivative with low micromolar binding activity, representing a valuable lead compound for further structural optimization.     

Preferred conformations of endogenous cannabinoid ligand anandamide

Anandamide (arachidonyl-ethanolamide, AEA) is an important endogenous cannabinoid ligand isolated from porcine brain. AEA has a flexible molecular structure with a series of four non-conjugated double bonds, a hydrophobic alkyl chain, and a carboxyamide head group. It is known that AEA binds to cannabinoid receptor and induces cannabimimetic activity. However, questions still remain about the three-dimensional arrangement of the pharmacophoric groups of AEA that facilitate its interaction with cannabinoid receptor, a member of transmembrane G-protein coupled receptors (GPCRs). Such information is of critical importance for the design of novel analogs of potential therapeutic values. In the present studies, we developed a combined approach of 2D high-resolution NMR and computer modeling to investigate conformational features of AEA in solution. The developed method and experimental data is then applied to study the structural properties of AEA in a membrane-like environment that will be reported elsewhere. In addition to the measured NOEs, the dihedral angle constraints were for the first time being used as experimentally-determined structural constraints for performing molecular dynamics simulations to refine the NMR-determined AEA conformations. Our results showed that AEA prefers an extended pseudo-helical conformation in solution with two oxygen atoms pointing towards the same side and a straight pentyl chain, which was an averaged conformation observed on the basis of NMR time scale. The results were correlated to the computer predicted AEA models reported by others. The established NMR-based computational approach provides an alternative way to explore further the detailed conformational properties of AEA that encodes important pharmacophoric and conformational information regarding the activation of cannabinoid receptors.     

Discovery of natural estrogen receptor modulators with structure-based virtual screening

Eleven compounds were identified as estrogen receptor modulators from an in-house natural product database (NPD) by structure-based virtual screening for ERα and ERβ. Among them, 3 compounds were confirmed as ER agonists and 8 compounds were confirmed as ER antagonists by yeast two-hybrid (Y2H) assay, with EC₅₀ values ranging from several micromolar to 100 micromolar. In this study, a novel series of cycloartane triterpenoids isolated from Schisandra glaucescens Diels was found to have ER antagonistic effect, the most potent antagonist of which exhibited activity with EC₅₀ value of 2.55 and 4.68 μM for ERα and ERβ, respectively. Moreover, the types of modulation and subtype selectivity were also investigated through molecular docking simulation.     

Structure-based virtual ligand screening with LigandFit: pose prediction and enrichment of compound collections

Virtual ligand screening methods based on the structure of the receptor are extensively used to facilitate the discovery of lead compounds. In the present study, we investigated the LigandFit package on four different proteins (coagulation factor VIIa, estrogen receptor, thymidine kinase, and neuraminidase), a relatively large compound collection of 65,560 unique "drug-like" molecules and four focused libraries (1950 molecules each). We performed virtual screening experiments with the large database and evaluated six scoring functions available in the package (DockScore, LigScore1, LigScore2, PLP1, PLP2, and PMF). The results showed that LigandFit is an efficient program, especially when used with LigScore1. Similar computations were carried out using focused libraries. In this situation the LigScore1 scoring function outperformed the other ones on three out of the four proteins tested. Even for the difficult neuraminidase case, the LigandFit/LigScore1 combination was still reasonably successful. Assessment of docking accuracy was also performed and again, we found that LigandFit (with DockScore and the CFF parameters) was performing well. On the basis of these results and observed increased enrichments after LigandFit/Ligscore1 screening on focused libraries, we suggest that using this program as a final step of a hierarchical protocol can be very beneficial to assist lead finding.     

Structure-based virtual screening and identification of a novel androgen receptor antagonist

Hormonal therapies, mainly combinations of anti-androgens and androgen deprivation, have been the mainstay treatment for advanced prostate cancer because the androgen-androgen receptor (AR) system plays a pivotal role in the development and progression of prostate cancers. However, the emergence of androgen resistance, largely due to inefficient anti-hormone action, limits the therapeutic usefulness of these therapies. Here, we report that 6-(3,4-dihydro-1H-isoquinolin-2-yl)-N-(6-methylpyridin-2-yl)nicotinamide (DIMN) acts as a novel anti-androgenic compound that may be effective in the treatment of both androgen-dependent and androgen-independent prostate cancers. Through AR structure-based virtual screening using the FlexX docking model, fifty-four compounds were selected and further screened for AR antagonism via cell-based tests. One compound, DIMN, showed an antagonistic effect specific to AR with comparable potency to that of the classical AR antagonists, hydroxyflutamide and bicalutamide. Consistent with their anti-androgenic activity, DIMN inhibited the growth of androgen-dependent LNCaP prostate cancer cells. Interestingly, the compound also suppressed the growth of androgen-independent C4-2 and CWR22rv prostate cancer cells, which express a functional AR, but did not suppress the growth of the AR-negative prostate cancer cells PPC-1, DU145, and R3327-AT3.1. Taken together, the results suggest that the synthetic compound DIMN is a novel anti-androgen and strong candidate for useful therapeutic agent against early stage to advanced prostate cancer.     

Identification of free fatty acid receptor 2 agonists using virtual screening

Structure- and ligand-based virtual-screening methods (docking, 2D- and 3D-similarity searching) were analyzed for their effectiveness in virtual screening against FFAR2. To evaluate the performance of these methods, retrospective virtual screening was performed. Statistical quality of the methods was evaluated by BEDROC and RIE. The results revealed that electrostatic similarity search protocol using EON (ET combo) outperformed all other protocols with outstanding enrichment of >95% in top 1% and 2% of the dataset with an AUC of 0.958. Interestingly, the hit lists that are obtained from different virtual-screening methods are generally highly complementary to hits found from electrostatic similarity searching. These results suggest that considering electrostatic similarity searching first increases the chance of identifying more (and more diverse) active compounds from a virtual-screening campaign. Accordingly, prospective virtual screening using electrostatic similarity searching was used to identify novel FFAR2 ligands. The discovered compounds provide new chemical matter starting points for the initiation of a medicinal chemistry campaign.     

Comparative docking of dual conformations in human fatty acid synthase thioesterase domain reveals potential binding cavity for virtual screening of ligands

Human fatty acid synthase (hFASN), a homo dimeric lipogenic enzyme with seven catalytic domains, is an important clinical target in cancer, metabolic syndrome and infections. Here, molecular modelling and docking methods were implemented to examine the inter-molecular interactions of thioesterase (TE) domain in hFASN with its physiological substrate, and to identify potential chemical inhibitors. TE catalyses the hydrolysis of thioester bond between palmitate and the 4’ phosphopantetheine of acyl carrier protein, releasing 16-carbon palmitate. The crystal structure of hFASN TE in two inhibitory conformations (A and B) were geometry-optimized and used for molecular docking with palmitate, orlistat (a known FASN inhibitor) and virtual screening against compounds from National Cancer Institute (NCI) database. Relatively, low binding affinity was observed during the complex formation of palmitate with A (?.164 kcal/mol) and B (?.332 kcal/mol) forms of TE, when compared with orlistat-docked TE (A form: ?5.872 kcal/mol and B form: ?5.484 kcal/mol), clearly indicating that the native inhibited conformation (crystal structure) was unfavourable for substrate binding. We used these orlistat dual binding modes as positive controls for prioritizing the ligands during virtual screening. From 2, 31,617 molecules in the NCI database, 916 high-scoring compounds (hit ligands) were obtained for A-form and 4582 for B-form of the TE-domain, which were then ranked according to glide docking score, XP H bond score, absorption, distribution, metabolism and excretion and binding free energy (Prime/MM-GBSA). Consequently, two top scoring ligands (NSC: 319661 and NSC: 153166) emerged as promising drug candidates that may be tested in FASN-over-expressing diseases.     

Structure-based virtual screening for glycosyltransferase51

Glycosyltransferase is an essential and easily accessible drug target for antibiotic-resistance. The crystal structures of glycosyltransferase (GT₅₁) provide us with the chance to develop new antibiotics that interrupt a yet unexplored molecular target. Based on the crystal structure of GT₅₁, we have carried out computational screening of GT₅₁ in order to look for novel GT₅₁ inhibitors. The present study was accomplished by using advance docking and scoring methodology. It is the first example of virtual screening of GT₅₁ inhibitors. Two docking procedures (Surflex-Dock and FlexX-Pharm dockings) were applied and nine novel potential leads are proposed after thorough examination by a combination of methods.     

Insights from ligand and structure based methods in virtual screening of selective Ni-peptide deformylase inhibitors

In recent years, there has been a growing interest in developing bacterial peptide deformylase (PDF) inhibitors as novel antibiotics. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse PDF inhibitors which is useful for designing of potential antibiotics. Twenty one structurally diverse compounds were considered for the generation of quantitative pharmacophore model using HypoGen of Catalyst, further model was validated using 78 compounds. Pharmacophore model demonstrated the importance of two acceptors, one donor and one hydrophobic feature toward the biological activity. The inhibitors were also docked into the binding site of PDF to comprehend the structural insights of the active site. Combination of ligand and structure based methods were used to find the potential antibiotics.     

Ligand-based pharmacophore mapping and virtual screening for identification of potential discoidin domain receptor 1 inhibitors

Abbreviations ADME absorption, distribution, metabolism, and excretion

MMGB/SA molecular mechanics generalized born surface area

IFD induced fit docking

RTK receptor tyrosine kinase

NSCLC non-small-cell lung cancer

ATP adenosine triphosphate

OPLS optimized potential for liquid stimulation

RMSD root mean square deviation

HTVS high-throughput virtual screening

SP standard precision

XP extra precision

OPLS-AA optimized potential for liquid stimulation-all atom

MD molecular simulation

MME molecular mechanics energies

SGB surface generalized born

POPC membrane 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine membrane

PDB Protein Data Bank

DDR1 discoidin domain receptor 1

DDR2 discoidin domain receptor 2

DDRs discoidin domain receptors

ECM extracellular matrix

TIP4P transferable intermolecular potential 4 point

NPT constant particle number, pressure and temperature

RMSF root mean square fluctuation

Communicated by Ramaswamy H. Sarma