Computational study of the heterodimerization between μ and δ receptors

A growing body of evidence indicated that the G protein coupled receptors exist as homo- or hetero-dimers in the living cell. The heterodimerization between μ and δ opioid receptors has attracted researchers’ particular interests, it is reported to display novel pharmacological and signalling regulation properties. In this study, we construct the full-length 3D-model of μ and δ opioid receptors using the homology modelling method. Threading program was used to predict the possible templates for the N- and C-terminus domains. Then, a 30 ns molecular dynamics simulations was performed with each receptor embedded in an explicit membrane-water environment to refine and explore the conformational space. Based on the structures extracted from the molecular dynamics, the likely interface of μ–δ heterodimer was investigated through the analysis of protein–protein docking, cluster, shape complementary and interaction energy. The computational modelling works revealed that the most likely interface of heterodimer was formed between the transmembrane1,7 (TM1,7) domains of μ receptor and the TM(4,5) domains of δ receptor, with emphasis on μ-TM1 and δ-TM4, the next likely interface was μ(TM6,7)-δ(TM4,5), with emphasis on μ-TM6 and δ-TM4. Our results were consistent with previous reports. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Flexible CDOCKER: Development and application of a pseudo‐explicit structure‐based docking method within CHARMM

Protein‐ligand docking is a commonly used method for lead identification and refinement. While traditional structure‐based docking methods represent the receptor as a rigid body, recent developments have been moving toward the inclusion of protein flexibility. Proteins exist in an interconverting ensemble of conformational states, but effectively and efficiently searching the conformational space available to both the receptor and ligand remains a well‐appreciated computational challenge. To this end, we have developed the Flexible CDOCKER method as an extension of the family of complete docking solutions available within CHARMM. This method integrates atomically detailed side chain flexibility with grid‐based docking methods, maintaining efficiency while allowing the protein and ligand configurations to explore their conformational space simultaneously. This is in contrast to existing approaches that use induced‐fit like sampling, such as Glide or Autodock, where the protein or the ligand space is sampled independently in an iterative fashion. Presented here are developments to the CHARMM docking methodology to incorporate receptor flexibility and improvements to the sampling protocol as demonstrated with re‐docking trials on a subset of the CCDC/Astex set. These developments within CDOCKER achieve docking accuracy competitive with or exceeding the performance of other widely utilized docking programs. © 2015 Wiley Periodicals, Inc.     

Identifying conformational changes of the β2 adrenoceptor that enable accurate prediction of ligand/receptor interactions and screening for GPCR modulators

The new β₂ Adrenoceptor (β₂AR) crystal structures provide a high-resolution snapshot of receptor interactions with two particular partial inverse agonists, (−)-carazolol and timolol. However, both experimental and computational studies of GPCR structure are significantly complicated by the existence of multiple conformational states coupled to ligand type and receptor activity. Agonists and antagonists induce or stabilize distinct changes in receptor structure that mediate a range of pharmacological activities. In this work, we (1) established that the existing β₂AR crystallographic conformers can be extended to describe ligand/receptor interactions for additional antagonist types, (2) generated agonist-bound receptor conformations, and (3) validated these models for agonist and antagonist virtual ligand screening (VLS). Using a ligand directed refinement protocol, we derived a single agonist-bound receptor conformation that selectively retrieved a diverse set of full and partial β₂AR agonists in VLS trials. Additionally, the impact of extracellular loop two conformation on VLS was assessed by docking studies with rhodopsin-based β₂AR homology models, and loop-deleted receptor models. A general strategy for constructing and selecting agonist-bound receptor pocket conformations is presented, which may prove broadly useful in creating agonist and antagonist bound models for other GPCRs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Molecular dynamics simulation study of PTP1B with allosteric inhibitor and its application in receptor based pharmacophore modeling

Allosteric inhibition of protein tyrosine phosphatase 1B (PTP1B), has paved a new path to design specific inhibitors for PTP1B, which is an important drug target for the treatment of type II diabetes and obesity. The PTP1B_1–282-allosteric inhibitor complex crystal structure lacks α7 (287–298) and moreover there is no available 3D structure of PTP1B_1–298 in open form. As the interaction between α7 and α6–α3 helices plays a crucial role in allosteric inhibition, α7 was modeled to the PTP1B_1–282 in open form complexed with an allosteric inhibitor (compound-2) and a 5 ns MD simulation was performed to investigate the relative orientation of the α7–α6–α3 helices. The simulation conformational space was statistically sampled by clustering analyses. This approach was helpful to reveal certain clues on PTP1B allosteric inhibition. The simulation was also utilized in the generation of receptor based pharmacophore models to include the conformational flexibility of the protein-inhibitor complex. Three cluster representative structures of the highly populated clusters were selected for pharmacophore model generation. The three pharmacophore models were subsequently utilized for screening databases to retrieve molecules containing the features that complement the allosteric site. The retrieved hits were filtered based on certain drug-like properties and molecular docking simulations were performed in two different conformations of protein. Thus, performing MD simulation with α7 to investigate the changes at the allosteric site, then developing receptor based pharmacophore models and finally docking the retrieved hits into two distinct conformations will be a reliable methodology in identifying PTP1B allosteric inhibitors. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Robust optimization of scoring functions for a target class

Target-specific optimization of scoring functions for protein–ligand docking is an effective method for significantly improving the discrimination of active and inactive molecules in virtual screening applications. Its applicability, however, is limited due to the narrow focus on, e.g., single protein structures. Using an ensemble of protein kinase structures, the publically available directory of useful decoys ligand dataset, and a novel multi-factorial optimization procedure, it is shown here that scoring functions can be tuned to multiple targets of a target class simultaneously. This leads to an improved robustness of the resulting scoring function parameters. Extensive validation experiments clearly demonstrate that (1) virtual screening performance for kinases improves significantly; (2) variations in database content affect this kind of machine-learning strategy to a lesser extent than binary QSAR models, and (3) the reweighting of interaction types is of particular importance for improved screening performance. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Surflex-Dock 2.1: Robust performance from ligand energetic modeling,ring flexibility,and knowledge-based search

The Surflex flexible molecular docking method has been generalized and extended in two primary areas related to the search component of docking. First, incorporation of a small-molecule force-field extends the search into Cartesian coordinates constrained by internal ligand energetics. Whereas previous versions searched only the alignment and acyclic torsional space of the ligand, the new approach supports dynamic ring flexibility and all-atom optimization of docked ligand poses. Second, knowledge of well established molecular interactions between ligand fragments and a target protein can be directly exploited to guide the search process. This offers advantages in some cases over the search strategy where ligand alignment is guided solely by a “protomol” (a pre-computed molecular representation of an idealized ligand). Results are presented on both docking accuracy and screening utility using multiple publicly available benchmark data sets that place Surflex’s performance in the context of other molecular docking methods. In terms of docking accuracy, Surflex-Dock 2.1 performs as well as the best available methods. In the area of screening utility, Surflex’s performance is extremely robust, and it is clearly superior to other methods within the set of cases for which comparative data are available, with roughly double the screening enrichment performance.     

Role of protein flexibility in the design of Bcl-XL targeting agents: insight from molecular dynamics

Detailed understanding of protein–ligand interactions is crucial to the design of more effective drugs. This is particularly true when targets are protein interfaces which have flexible, shallow binding sites that exhibit substantial structural rearrangement upon ligand binding. In this study, we use molecular dynamics simulations and free energy calculations to explore the role of ligand-induced conformational changes in modulating the activity of three generations of Bcl-X_L inhibitors. We show that the improvement in the binding affinity of each successive ligand design is directly related to a unique and measurable reduction in local flexibility of specific regions of the binding groove, accompanied by the corresponding changes in the secondary structure of the protein. Dynamic analysis of ligand–protein interactions reveals that the latter evolve with each new design consistent with the observed increase in protein stability, and correlate well with the measured binding affinities. Moreover, our free energy calculations predict binding affinities which are in qualitative agreement with experiment, and indicate that hydrogen bonding to Asn100 could play a prominent role in stabilizing the bound conformations of latter generation ligands, which has not been recognized previously. Overall our results suggest that molecular dynamics simulations provide important information on the dynamics of ligand–protein interactions that can be useful in guiding the design of small-molecule inhibitors of protein interfaces. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Multiple protein structures and multiple ligands: effects on the apparent goodness of virtual screening results

As an extension to a previous published study (McGaughey et al., J Chem Inf Model 47:1504–1519, 2007) comparing 2D and 3D similarity methods to docking, we apply a subset of those virtual screening methods (TOPOSIM, SQW, ROCS-color, and Glide) to a set of protein/ligand pairs where the protein is the target for docking and the cocrystallized ligand is the target for the similarity methods. Each protein is represented by a maximum of five crystal structures. We search a diverse subset of the MDDR as well as a diverse small subset of the MCIDB, Merck’s proprietary database. It is seen that the relative effectiveness of virtual screening methods, as measured by the enrichment factor, is highly dependent on the particular crystal structure or ligand, and on the database being searched. 2D similarity methods appear very good for the MDDR, but poor for the MCIDB. However, ROCS-color (a 3D similarity method) does well for both databases. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Scoring confidence index: statistical evaluation of ligand binding mode predictions

Protein-ligand docking programs can generate a large number of possible binding orientations for each ligand candidate. The challenge is to identify the orientations closest to the native binding mode using a scoring method. Many different scoring functions have been developed for protein-ligand scoring, but their performance on binding mode prediction is often target-dependent. In this study, a statistical approach was employed to provide a confidence measure of scoring performance in finding close to the correct docked ligand orientations. It exploits the fact that the scores provided by an adequately performing scoring function generally improve as the ligand binding modes get closer to the correct native orientation. For such cases, the correlation coefficient of scores versus distances is expected to be highest when the most native-like orientation is used as a reference. This correlation coefficient, called the correlation-based score (CBScore), was used as an indicator of how far the docked pose was from the native orientation. The correlation between the original scores and CBScores as well as the range of CBScores were found to be good measures of scoring performance. They were combined into a single quantity, called the scoring confidence index. High values of the scoring confidence index were indicative of pronounced and relatively smooth binding energy landscapes with easily discernable global minima, resulting in reliable binding mode predictions. Low values of this index reflected rugged energy landscapes making the prediction of the correct binding mode very difficult and often unreliable. The diagnostic ability of the scoring confidence index was tested on a non-redundant set of 50 protein-ligand complexes scored with three commonly employed scoring functions: AffiScore, DrugScore and X-Score. Binding mode predictions were found to be three times more reliable for complexes with scoring confidence indices in the upper half than for cases with values in the lower half of the resulting range of 0–1.6. This new confidence measure of scoring performance is expected to be a valuable tool for virtual screening applications. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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.     

Identification of novel target sites and an inhibitor of the dengue virus E protein

Dengue and related flaviviruses represent a significant global health threat. The envelope glycoprotein E mediates virus attachment to a host cell and the subsequent fusion of viral and host cell membranes. The fusion process is driven by conformational changes in the E protein and is an essential step in the virus life cycle. In this study, we analyzed the pre-fusion and post-fusion structures of the dengue virus E protein to identify potential novel sites that could bind small molecules, which could interfere with the conformational transitions that mediate the fusion process. We used an in silico virtual screening approach combining three different docking algorithms (DOCK, GOLD and FlexX) to identify compounds that are likely to bind to these sites. Seven structurally diverse molecules were selected to test experimentally for inhibition of dengue virus propagation. The best compound showed an IC₅₀ in the micromolar range against dengue virus type 2. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

LigDockCSA: protein-ligand docking using conformational space annealing

Protein–ligand docking techniques are one of the essential tools for structure‐based drug design. Two major components of a successful docking program are an efficient search method and an accurate scoring function. In this work, a new docking method called LigDockCSA is developed by using a powerful global optimization technique, conformational space annealing (CSA), and a scoring function that combines the AutoDock energy and the piecewise linear potential (PLP) torsion energy. It is shown that the CSA search method can find lower energy binding poses than the Lamarckian genetic algorithm of AutoDock. However, lower‐energy solutions CSA produced with the AutoDock energy were often less native‐like. The loophole in the AutoDock energy was fixed by adding a torsional energy term, and the CSA search on the refined energy function is shown to improve the docking performance. The performance of LigDockCSA was tested on the Astex diverse set which consists of 85 protein–ligand complexes. LigDockCSA finds the best scoring poses within 2 Å root‐mean‐square deviation (RMSD) from the native structures for 84.7% of the test cases, compared to 81.7% for AutoDock and 80.5% for GOLD. The results improve further to 89.4% by incorporating the conformational entropy. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

Virtual fragment screening: an exploration of various docking and scoring protocols for fragments using Glide

Fragment-based drug discovery approaches allow for a greater coverage of chemical space and generally produce high efficiency ligands. As such, virtual and experimental fragment screening are increasingly being coupled in an effort to identify new leads for specific therapeutic targets. Fragment docking is employed to create target-focussed subset of compounds for testing along side generic fragment libraries. The utility of the program Glide with various scoring schemes for fragment docking is discussed. Fragment docking results for two test cases, prostaglandin D2 synthase and DNA ligase, are presented and compared to experimental screening data. Self-docking, cross-docking, and enrichment studies are performed. For the enrichment runs, experimental data exists indicating that the docking decoys in fact do not inhibit the corresponding enzyme being examined. Results indicate that even for difficult test cases fragment docking can yield enrichments significantly better than random. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Fully automated flexible docking of ligands into flexible synthetic receptors using forward and inverse docking strategies

The prediction of the structure of host-guest complexes is one of the most challenging problems in supramolecular chemistry. Usual procedures for docking of ligands into receptors do not take full conformational freedom of the host molecule into account. We describe and apply a new docking approach which performs a conformational sampling of the host and then sequentially docks the ligand into all receptor conformers using the incremental construction technique of the FlexX software platform. The applicability of this approach is validated on a set of host-guest complexes with known crystal structure. Moreover, we demonstrate that due to the interchangeability of the roles of host and guest, the docking process can be inverted. In this inverse docking mode, the receptor molecule is docked around its ligand. For all investigated test cases, the predicted structures are in good agreement with the experiment for both normal (forward) and inverse docking. Since the ligand is often smaller than the receptor and, thus, its conformational space is more restricted, the inverse docking approach leads in most cases to considerable speed-up. By having the choice between two alternative docking directions, the application range of the method is significantly extended. Finally, an important result of this study is the suitability of the simple energy function used here for structure prediction of complexes in organic media.     

Flexible docking simulations: Scaled collective variable Monte Carlo minimization approach using Bezier splines,and comparison with a standard Monte Carlo algorithm

An algorithm for docking a flexible ligand onto a flexible or rigid receptor, using the scaled‐collective‐variables Monte Carlo with energy minimization approach, is presented. Energy minimization is shown to be one of the best techniques for distinguishing between native‐ and nonnative‐generated conformations. Incorporation of this technique into a Monte Carlo procedure enables one to distinguish the native conformation directly during the conformational search. It avoids the generation of a large number of ligand conformers for which more sophisticated energy evaluation tools would have had to be applied to identify the nativelike conformations. The efficiency of the Monte Carlo minimization was greatly improved by incorporating a new grid‐based energy evaluation technique using Bezier splines for which the energy function, as well as all of its derivatives, can be deduced from the values at the grid points. Comparison between our ECEPP/3‐based algorithm and the Monte Carlo algorithm presented elsewhere (Hart, T. N.; Read, R. J. Prot Struct Funct Genet 1992, 13, 206–222) has been made for docking NH₂ D Phe Pro Arg COOH, the noncovalent analog of NH₂ D Phe Pro Arg chloromethylketone (PPACK), onto the active site of human α‐thrombin. ©1999 John Wiley & Sons, Inc. J Comput Chem 20: 244–252, 1999     

GalaxyDock3: Protein–ligand docking that considers the full ligand conformational flexibility

Predicting conformational changes of both the protein and the ligand is a major challenge when a protein–ligand complex structure is predicted from the unbound protein and ligand structures. Herein, we introduce a new protein–ligand docking program called GalaxyDock3 that considers the full ligand conformational flexibility by explicitly sampling the ligand ring conformation and allowing the relaxation of the full ligand degrees of freedom, including bond angles and lengths. This method is based on the previous version (GalaxyDock2) which performs the global optimization of a designed score function. Ligand ring conformation is sampled from a ring conformation library constructed from structure databases. The GalaxyDock3 score function was trained with an additional bonded energy term for the ligand on a large set of complex structures. The performance of GalaxyDock3 was improved compared to GalaxyDock2 when predicted ligand conformation was used as the input for docking, especially when the input ligand conformation differs significantly from the crystal conformation. GalaxyDock3 also compared favorably with other available docking programs on two benchmark tests that contained diverse ligand rings. The program is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2019 Wiley Periodicals, Inc.     

Modeling of peptides containing D-amino acids: implications on cyclization

Cyclic peptides are therapeutically attractive due to their high bioavailability, potential selectivity, and scaffold novelty. Furthermore, the presence of D-residues induces conformational preferences not followed by peptides consisting of naturally abundant L-residues. Therefore, comprehending how amino acids induce turns in peptides, subsequently facilitating cyclization, is significant in peptide design. Here, we performed 20-ns explicit-solvent molecular dynamics simulations for three diastereomeric peptides with stereochemistries: LLLLL, LLLDL, and LDLDL. Experimentally LLLLL and LDLDL readily cyclize, whereas LLLDL cyclizes in low yield. Simulations at 310 K produced conformations with inter-terminal hydrogen bonds that correlated qualitatively with the experimental cyclization trend. Energies obtained for representative structures from quantum chemical (B3LYP/PCM/cc-pVTZ//HF/6-31G*) calculations predicted pseudo-cyclic and extended conformations as the most stable for LLLLL and LLLDL, respectively, in agreement with the experimental data. In contrast, the most stable conformer predicted for peptide LDLDL was not a pseudo-cyclic structure. Moreover, D-residues preferred the experimentally less populated α_L rotamers even when simulations were performed at a higher temperature and with strategically selected starting conformations. Energies calculated with molecular mechanics were consistent only with peptide LLLLL. Thus, the conformational preferences obtained for the all L-amino acid peptide were in agreement with the experimental observations. Moreover, refinement of the force field is expected to provide far-reaching conformational sampling of peptides containing D-residues to further develop force field-based conformational-searching methods. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Closing the side-chain gap in protein loop modeling

The success of structure-based drug design relies on accurate protein modeling where one of the key issues is the modeling and refinement of loops. This study takes a critical look at modeled loops, determining the effect of re-sampling side-chains after the loop conformation has been generated. The results are evaluated in terms of backbone and side-chain conformations with respect to the native loop. While models can contain loops with high quality backbone conformations, the side-chain orientations could be poor, and therefore unsuitable for ligand docking and structure-based design. In this study, we report on the ability to model loop side-chains accurately using a variety of commercially available algorithms that include rotamer libraries, systematic torsion scans and knowledge-based methods. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

GalaxyDock2: Protein–ligand docking using beta‐complex and global optimization

In this article, an enhanced version of GalaxyDock protein–ligand docking program is introduced. GalaxyDock performs conformational space annealing (CSA) global optimization to find the optimal binding pose of a ligand both in the rigid‐receptor mode and the flexible‐receptor mode. Binding pose prediction has been improved compared to the earlier version by the efficient generation of high‐quality initial conformations for CSA using a predocking method based on a beta‐complex derived from the Voronoi diagram of receptor atoms. Binding affinity prediction has also been enhanced by using the optimal combination of energy components, while taking into consideration the energy of the unbound ligand state. The new version has been tested in terms of binding mode prediction, binding affinity prediction, and virtual screening on several benchmark sets, showing improved performance over the previous version and AutoDock, on which the GalaxyDock energy function is based. GalaxyDock2 also performs better than or comparable to other state‐of‐the‐art docking programs. GalaxyDock2 is freely available at http://galaxy.seoklab.org/softwares/galaxydock.html . © 2013 Wiley Periodicals, Inc.