ClusPro: performance in CAPRI rounds 6-11 and the new server

ClusPro is the first fully automated, web-based program for docking protein structures. Users may upload the coordinate files of two protein structures through ClusPro's web interface, or enter the PDB codes of the respective structures. The server performs rigid body docking, energy screening, and clustering to produce models. The program output is a short list of putative complexes ranked according to their clustering properties. ClusPro has been participating in CAPRI since January 2003, submitting predictions within 24 h after a target becomes available. In Rounds 6-11, ClusPro generated acceptable submissions for Targets 22, 25, and 27. In general, acceptable models were obtained for the relatively easy targets without substantial conformational changes upon binding. We also describe the new version of ClusPro that incorporates our recently developed docking program PIPER. PIPER is based on the fast Fourier transform correlation approach, but the method is extended to use pairwise interaction potentials, thereby increasing the number of near-native docked structures.     

Implicit flexibility in protein docking: cross-docking and local refinement

In previous CAPRI rounds (3-5) we showed that using MD-generated ensembles, as inputs for a rigid-body docking algorithm, increased our success rate, especially for targets exhibiting substantial amounts of induced fit. In recent rounds (6-11), our cross-docking was followed by a short MD-based local refinement for the subset of solutions with the lowest interaction energies after minimization. The above approach showed promising results for target 20, where we were able to recover 30% of native contacts for one of our submitted models. Further tests, performed a posteriori, revealed that cross-docking approach produces more near-native (NN) solutions but only for targets with large conformational changes upon binding. However, at the time of the blind docking experiment, these improved solutions were not chosen for the subsequent refinement, as their interaction energies after minimization ranked poorly compared with other solutions. This indicates deficiencies in the present scoring schemes that are based on interaction energies of minimized structures. Refinement MD simulations substantially increase the fraction of native contacts for NN docked solutions, but generally worsen interface and ligand RMSD. Further analysis shows that although MD simulations are able to improve sidechain packing across the interface, which results in an increased fraction of native contacts, they are not capable of improving interface and ligand backbone RMSD for NN structures beyond 1.5 and 3.5 A, respectively, even if explicit solvent is used.     

Flexible relaxation of rigid-body docking solutions

Molecular Dynamics (MD) simulations have been performed on a set of rigid-body docking poses, carried out over 25 protein-protein complexes. The results show that fully flexible relaxation increases the fraction of native contacts (NC) by up to 70% for certain docking poses. The largest increase in the fraction of NC is observed for docking poses where anchor residues are able to sample their bound conformation. For each MD simulation, structural snap-shots were clustered and the centre of each cluster used as the MD-relaxed docking pose. A comparison between two energy-based scoring schemes, the first calculated for the MD-relaxed poses, the second for energy minimized poses, shows that the former are better in ranking complexes with large hydrophobic interfaces. Furthermore, complexes with large interfaces are generally ranked well, regardless of the type of relaxation method chosen, whereas complexes with small hydrophobic interfaces remain difficult to rank. In general, the results indicate that current force-fields are able to correctly describe direct intermolecular interactions between receptor and ligand molecules. However, these force-fields still fail in cases where protein-protein complexes are stabilized by subtle energy contributions.     

Re-Docking Scheme for Generating Near-Native Protein Complexes by Assembling Residue Interaction Fingerprints

Interaction profile method is a useful method for processing rigid-body docking. After the docking process, the resulting set of docking poses could be classified by calculating similarities among them using these interaction profiles to search for near-native poses. However, there are some cases where the near-native poses are not included in this set of docking poses even when the bound-state structures are used. Therefore, we have developed a method for generating near-native docking poses by introducing a re-docking process. We devised a method for calculating the profile of interaction fingerprints by assembling protein complexes after determining certain core-protein complexes. For our analysis, we used 44 bound-state protein complexes selected from the ZDOCK benchmark dataset ver. 2.0, including some protein pairs none of which generated near-native poses in the docking process. Consequently, after the re-docking process we obtained profiles of interaction fingerprints, some of which yielded near-native poses. The re-docking process involved searching for possible docking poses in a restricted area using the profile of interaction fingerprints. If the profile includes interactions identical to those in the native complex, we obtained near-native docking poses. Accordingly, near-native poses were obtained for all bound-state protein complexes examined here. Application of interaction fingerprints to the re-docking process yielded structures with more native interactions, even when a docking pose, obtained following the initial docking process, contained only a small number of native amino acid interactions. Thus, utilization of the profile of interaction fingerprints in the re-docking process yielded more near-native poses.     

Computer modeling of human angiogenin-dinucleotide substrate interaction

Structures of substrate bound human angiogenin complexes have been obtained for the first time by computer modeling. The dinucleotides CpA and UpA have been docked onto human angiogenin using a systematic grid search procedure in torsion and Eulerian angle space. The docking was guided throughout by the similarity of angiogenin-substrate interactions with interactions of RNase A and its substrate. The models were subjected to 1 nanosecond of molecular dynamics to access their stability. Structures extracted from MD simulations were refined by simulated annealing. Stable hydrogen bonds that bridged protein and ligand residues during the MD simulations were taken as restraints for simulated annealing. Our analysis on the MD structures and annealed models explains the substrate specificity of human angiogenin and is in agreement with experimental results. This study also predicts the B2 binding site residues of angiogenin, for which no experimental information is available so far. In the case of one of the substrates, CpA, we have also identified the presence of a water molecule that invariantly bridges the B2 base with the protein. We have compared our results to the RNase A-substrate complex and highlight the similarities and differences.     

Engineering of Candida antarctica lipase B for hydrolysis of bulky carboxylic acid esters

Candida antarctica lipase B (CALB) is a widely used biocatalyst with high activity and specificity for a wide range of primary and secondary alcohols. However, the range of converted carboxylic acids is more narrow and mainly limited to unbranched fatty acids. To further broaden the biotechnological applications of CALB it is of interest to expand the range of converted carboxylic acid and extend it to carboxylic acids that are branched or substituted in close proximity of the carboxyl group. An in silico library of 2400 CALB variants was built and screened in silico by substrate-imprinted docking, a four step docking procedure. First, reaction intermediates of putative substrates are covalently docked into enzyme active sites. Second, the geometry of the resulting enzyme-substrate complex is optimized. Third, the substrate is removed from the complex and then docked again into the optimized structure. Fourth, the resulting substrate poses are rated by geometric filter criteria as productive or non-productive poses. Eleven enzyme variants resulting from the in silico screening were expressed in Escherichia coli BL21 and measured in the hydrolysis of two branched fatty acid esters, isononanoic acid ethyl ester and 2-ethyl hexanoic acid ethyl esters. Five variants showed an initial increase in activity. The variant with the highest wet mass activity (T138S) was purified and further characterized. It showed a 5-fold increase in hydrolysis of isononanoic acid ethyl ester, but not toward sterically more demanding 2-ethyl hexanoic acid ethyl ester.     

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.     

BiGGER: a new (soft) docking algorithm for predicting protein interactions

A new computationally efficient and automated "soft docking" algorithm is described to assist the prediction of the mode of binding between two proteins, using the three-dimensional structures of the unbound molecules. The method is implemented in a software package called BiGGER (Bimolecular Complex Generation with Global Evaluation and Ranking) and works in two sequential steps: first, the complete 6-dimensional binding spaces of both molecules is systematically searched. A population of candidate protein-protein docked geometries is thus generated and selected on the basis of the geometric complementarity and amino acid pairwise affinities between the two molecular surfaces. Most of the conformational changes observed during protein association are treated in an implicit way and test results are equally satisfactory, regardless of starting from the bound or the unbound forms of known structures of the interacting proteins. In contrast to other methods, the entire molecular surfaces are searched during the simulation, using absolutely no additional information regarding the binding sites. In a second step, an interaction scoring function is used to rank the putative docked structures. The function incorporates interaction terms that are thought to be relevant to the stabilization of protein complexes. These include: geometric complementarity of the surfaces, explicit electrostatic interactions, desolvation energy, and pairwise propensities of the amino acid side chains to contact across the molecular interface. The relative functional contribution of each of these interaction terms to the global scoring function has been empirically adjusted through a neural network optimizer using a learning set of 25 protein-protein complexes of known crystallographic structures. In 22 out of 25 protein-protein complexes tested, near-native docked geometries were found with C(alpha) RMS deviations < or =4.0 A from the experimental structures, of which 14 were found within the 20 top ranking solutions. The program works on widely available personal computers and takes 2 to 8 hours of CPU time to run any of the docking tests herein presented. Finally, the value and limitations of the method for the study of macromolecular interactions, not yet revealed by experimental techniques, are discussed.     

Three-dimensional models of alpha(2A)-adrenergic receptor complexes provide a structural explanation for ligand binding.

We have compared bacteriorhodopsin-based (alpha(2A)-AR(BR)) and rhodopsin-based (alpha(2A)-AR(R)) models of the human alpha(2A)-adrenengic receptor (alpha(2A)-AR) using both docking simulations and experimental receptor alkylation studies with chloroethylclonidine and 2-aminoethyl methanethiosulfonate hydrobromide. The results indicate that the alpha(2A)-AR(R) model provides a better explanation for ligand binding than does our alpha(2A)-AR(BR) model. Thus, we have made an extensive analysis of ligand binding to alpha(2A)-AR(R) and engineered mutant receptors using clonidine, para-aminoclonidine, oxymetazoline, 5-bromo-N-(4, 5-dihydro-1H-imidazol-2-yl)-6-quinoxalinamine (UK14,304), and norepinephrine as ligands. The representative docked ligand conformation was chosen using extensive docking simulations coupled with the identification of favorable interaction sites for chemical groups in the receptor. These ligand-protein complex studies provide a rational explanation at the atomic level for the experimentally observed binding affinities of each of these ligands to the alpha(2A)-adrenergic receptor.     

Molecular insight into pseudolysin inhibition using the MM-PBSA and LIE methods

Pseudolysin, the extracellullar elastase of Pseudomonas aeruginosa (EC: 3.4.24.26) plays an important role in the pathogenesis of P. aeruginosa infections. In the present study, molecular dynamics simulations and theoretical affinity predictions were used to gain molecular insight into pseudolysin inhibition. Four low molecular weight inhibitors were docked at their putative binding sites and molecular dynamics (MD) simulations were performed for 5.0 ns, and the free energy of binding was calculated by the linear interaction energy method. The number and the contact surface area of stabilizing hydrophobic, aromatic, and hydrogen bonding interactions appears to reflect the affinity differences between the inhibitors. The proteinaceous inhibitor, Streptomyces metalloproteinase inhibitor (SMPI) was docked in three different binding positions and MD simulations were performed for 3.0 ns. The MD trajectories were used for molecular mechanics-Poisson-Boltzmann surface area analysis of the three binding positions. Computational alanine scanning of the average pseudolysin-SMPI complexes after MD revealed residues at the pseudolysin-SMPI interface giving the main contribution to the free energy of binding. The calculations indicated that SMPI interacts with pseudolysin via the rigid active site loop, but that also contact sites outside this loop contribute significantly to the free energy of association.     

Binding Mode Analyses and Pharmacophore Model Development for Stilbene Derivatives as a Novel and Competitive Class of α-Glucosidase Inhibitors

Stilbene urea derivatives as a novel and competitive class of non-glycosidic α-glucosidase inhibitors are effective for the treatment of type II diabetes and obesity. The main purposes of our molecular modeling study are to explore the most suitable binding poses of stilbene derivatives with analyzing the binding affinity differences and finally to develop a pharmacophore model which would represents critical features responsible for α-glucosidase inhibitory activity. Three-dimensional structure of S. cerevisiae α-glucosidase was built by homology modeling method and the structure was used for the molecular docking study to find out the initial binding mode of compound 12, which is the most highly active one. The initial structure was subjected to molecular dynamics (MD) simulations for protein structure adjustment at compound 12-bound state. Based on the adjusted conformation, the more reasonable binding modes of the stilbene urea derivatives were obtained from molecular docking and MD simulations. The binding mode of the derivatives was validated by correlation analysis between experimental K_i value and interaction energy. Our results revealed that the binding modes of the potent inhibitors were engaged with important hydrogen bond, hydrophobic, and π-interactions. With the validated compound 12-bound structure obtained from combining approach of docking and MD simulation, a proper four featured pharmacophore model was generated. It was also validated by comparison of fit values with the K_i values. Thus, these results will be helpful for understanding the relationship between binding mode and bioactivity and for designing better inhibitors from stilbene derivatives.     

Flexible protein docking refinement using pose-dependent normal mode analysis

Modeling conformational changes in protein docking calculations is challenging. To make the calculations tractable, most current docking algorithms typically treat proteins as rigid bodies and use soft scoring functions that implicitly accommodate some degree of flexibility. Alternatively, ensembles of structures generated from molecular dynamics (MD) may be cross-docked. However, such combinatorial approaches can produce many thousands or even millions of docking poses, and require fast and sensitive scoring functions to distinguish them. Here, we present a novel approach called "EigenHex," which is based on normal mode analyses (NMAs) of a simple elastic network model of protein flexibility. We initially assume that the proteins to be docked are rigid, and we begin by performing conventional soft docking using the Hex polar Fourier correlation algorithm. We then apply a pose-dependent NMA to each of the top 1000 rigid body docking solutions, and we sample and re-score multiple perturbed docking conformations generated from linear combinations of up to 20 eigenvectors using a multi-threaded particle swarm optimization algorithm. When applied to the 63 "rigid body" targets of the Protein Docking Benchmark version 2.0, our results show that sampling and re-scoring from just one to three eigenvectors gives a modest but consistent improvement for these targets. Thus, pose-dependent NMA avoids the need to sample multiple eigenvectors and it offers a promising alternative to combinatorial cross-docking.     

Theoretical studies on the interaction of biphenyl inhibitors with Mycobacterium tuberculosis protein tyrosine phosphatase MptpB

MptpB is an essential secreted virulence factor for M. tuberculosis. Inhibition of MptpB impairs mycobacterial survival in host macrophages and thus helps reduce tuberculosis infections. However, the binding mode of the biphenyl inhibitors, which are known as some of the most potent MptpB inhibitors, remains unclear. In this study, to understand the interactions between biphenyl inhibitors and MptpB, docking and molecular dynamics simulations were carried out using AutoDock and GROMACS softwares. Calculation results show that all the biphenyl inhibitors can be docked to the binding site of MptpB, with the acid warheads forming a hydrogen bond network at the active site. But the binding modes of other terminals of these inhibitors are different. The cyclohexyl and trifluoromethyl substituents at R1 and R2 sites are necessary for the inhibitors to adopt their double-site binding mechanism. The estimated binding affinities are basically consistent with the experimental results. MD simulations show that these binding complexes display different stability.     

Molecular simulation of N-acetylneuraminic acid analogs and molecular dynamics studies of cholera toxin-Neu5Gc complex

Cholera toxin (CT) is an AB₅ protein complex secreted by the pathogen Vibrio cholera, which is responsible for cholera infection. N-acetylneuraminic acid (NeuNAc) is a derivative of neuraminic acid with nine-carbon backbone. NeuNAc is distributed on the cell surface mainly located in the terminal components of glycoconjugates, and also plays an important role in cell–cell interaction. In our current study, molecular docking and molecular dynamic (MD) simulations were implemented to identify the potent NeuNAc analogs with high-inhibitory activity against CT protein. Thirty-four NeuNAc analogs, modified in different positions C-1/C-2/C-4/C-5/C-7/C-8/C-9, were modeled and docked against the active site of CT protein. Among the 34 NeuNAc analogs, the analog Neu5Gc shows the least extra precision glide score of ?9.52 and glide energy of ?44.71?kcal/mol. NeuNAc analogs block the CT active site residues HIS:13, ASN:90, LYS:91, GLN:56, GLN:61, and TRP:88 through intermolecular hydrogen bonding. The MD simulation for CT-Neu5Gc docking complex was performed using Desmond. MD simulation of CT-Neu5Gc complex reveals the stable nature of docking interaction.     

Exploring dual inhibitory role of febrifugine analogues against Plasmodium utilizing structure-based virtual screening and molecular dynamic simulation

Malaria is an endemic disease caused by the protozoan parasite Plasomodium falciparum. Febrifugine analogues are natural compound obtained from the traditional Chinese herbs have shown significant antimalarial and anticancerous efficacy in experimental model. Development of resistance against the existing antimalarial drug has alarmed the scientific innovators to find a potential antimalarial molecule which can be further used by endemic countries for the elimination of this disease. In this study, structure-based virtual screening and molecular dynamics (MD) base approaches were used to generate potential antimalarial compound against plasmepsin II and prolyl-tRNA synthetase of Plasmodium. Here, we have docked series of febrifugine analogues (n = 11,395) against plasmepsin II in three different docking modes and then it was compared with previously reported target prolyl-tRNA synthetase. Extra precision docking resulted into 235 ligands having better docking score were subject for QikProp analysis. Better ligands (n = 39) obtained from QikProp analysis were subject for ADMET prediction and docking protocol validation through the estimation of receiver operator characteristics. In the later stage, 24 ligands obtained from ADMET study were subject for the estimation of binding energy through MM-GBSA and same were also docked against prolyl-tRNA synthetase to get compounds with dual inhibitor role. Finally, MD simulation and 2D fingerprint MACCS study of two best ligands have shown significant interaction with plasmepsin II and homology against known active ligand with noteworthy MACCS index, respectively. This study concludes that FA12 could be potential drug candidate to fight against Plasmodium falciparum parasites.     

Molecular Modeling-Based Evaluation of hTLR10 and Identification of Potential Ligands in Toll-Like Receptor Signaling

Toll-like receptors (TLRs) are pattern recognition receptors that recognize pathogens based on distinct molecular signatures. The human (h)TLR1, 2, 6 and 10 belong to the hTLR1 subfamilies, which are localized in the extracellular regions and activated in response to diverse ligand molecules. Due to the unavailability of the hTLR10 crystal structure, the understanding of its homo and heterodimerization with hTLR2 and hTLR1 and the ligand responsible for its activation is limited. To improve our understanding of the TLR10 receptor-ligand interaction, we used homology modeling to construct a three dimensional (3D) structure of hTLR10 and refined the model through molecular dynamics (MD) simulations. We utilized the optimized structures for the molecular docking in order to identify the potential site of interactions between the homo and heterodimer (hTLR10/2 and hTLR10/1). The docked complexes were then used for interaction with ligands (Pam₃CSK₄ and PamCysPamSK₄) using MOE-Dock and ASEDock. Our docking studies have shown the binding orientations of hTLR10 heterodimer to be similar with other TLR2 family members. However, the binding orientation of hTLR10 homodimer is different from the heterodimer due to the presence of negative charged surfaces at the LRR11-14, thereby providing a specific cavity for ligand binding. Moreover, the multiple protein-ligand docking approach revealed that Pam₃CSK₄ might be the ligand for the hTLR10/2 complex and PamCysPamSK_4, a di-acylated peptide, might activate hTLR10/1 hetero and hTLR10 homodimer. Therefore, the current modeled complexes can be a useful tool for further experimental studies on TLR biology.     

Docking and molecular dynamics studies at trypanothione reductase and glutathione reductase active sites

A theoretical docking study on the active sites of trypanothione reductase (TR) and glutathione reductase (GR) with the corresponding natural substrates, trypanothione disulfide (T[S]2) and glutathione disulfide (GSSG), is reported. Molecular dynamics simulations were carried out in order to check the robustness of the docking results. The energetic results are in agreement with previous experimental findings and show the crossed complexes have lower stabilization energies than the natural ones. To test DOCK3.5, four nitro furanic compounds, previously designed as potentially active anti-chagasic molecules, were docked at the GR and TR active sites with the DOCK3.5 procedure. A good correlation was found between differential inhibitory activity and relative interaction energy (affinity). The results provide a validation test for the use of DOCK3.5 in connection with the design of anti-chagasic drugs.     

Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4

In drug discovery process, improvement of ADME/Tox properties of lead compounds including metabolic stability is critically important. Cytochrome P450 (CYP) is one of the major metabolizing enzymes and the prediction of sites of metabolism (SOM) on the given lead compounds is key information to modify the compounds to be more stable against metabolism. There are two factors essentially important in SOM prediction. First is accessibility of each substrate atom to the oxygenated Fe atom of heme in a CYP protein, and the other is the oxidative reactivity of each substrate atom. To predict accessibility of substrate atoms to the heme iron, conventional protein-rigid docking simulations have been applied. However, the docking simulations without consideration of protein flexibility often lead to incorrect answers in the case of very flexible proteins such as CYP3A4. In this study, we demonstrated an approach utilizing molecular dynamics (MD) simulation for SOM prediction in which multiple MD runs were executed using different initial structures. We applied this strategy to CYP3A4 and carbamazepine (CBZ) complex. Through 10 ns MD simulations started from five different CYP3A4-CBZ complex models, our approach correctly predicted SOM observed in experiments. The experimentally known epoxidized sites of CBZ by CYP3A4 were successfully predicted as the most accessible sites to the heme iron that was judged from a numerical analysis of calculated ΔG(binding) and the frequency of appearance. In contrast, the predictions using protein-rigid docking methods hardly provided the correct SOM due to protein flexibility or inaccuracy of the scoring functions. Our strategy using MD simulation with multiple initial structures will be one of the reliable methods for SOM prediction.     

A fast protein-ligand docking algorithm based on hydrogen bond matching and surface shape complementarity

With the rapid development of structural determination of target proteins for human diseases, high throughout virtual screening based drug discovery is gaining popularity gradually. In this paper, a fast docking algorithm (H-DOCK) based on hydrogen bond matching and surface shape complementarity was developed. In H-DOCK, firstly a divide-and-conquer strategy based enumeration approach is applied to rank the intermolecular modes between protein and ligand by maximizing their hydrogen bonds matching, then each docked conformation of the ligand is calculated according to the matched hydrogen bonding geometry, finally a simple but effective scoring function reflecting mainly the van der Waals interaction is used to evaluate the docked conformations of the ligand. H-DOCK is tested for rigid ligand docking and flexible one, the latter is implemented by repeating rigid docking for multiple conformations of a small molecule and ranking all together. For rigid ligands, H-DOCK was tested on a set of 271 complexes where there is at least one intermolecular hydrogen bond, and H-DOCK achieved success rate (RMSD<2.0?Å) of 91.1%. For flexible ligands, H-DOCK was tested on another set of 93 complexes, where each case was a conformation ensemble containing native ligand conformation as well as 100 decoy ones generated by AutoDock [1], and the success rate reached 81.7%. The high success rate of H-DOCK indicates that the hydrogen bonding and steric hindrance can grasp the key interaction between protein and ligand. H-DOCK is quite efficient compared with the conventional docking algorithms, and it takes only about 0.14 seconds for a rigid ligand docking and about 8.25 seconds for a flexible one on average. According to the preliminary docking results, it implies that H-DOCK can be potentially used for large scale virtual screening as a pre-filter for a more accurate but less efficient docking algorithm.