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Results for “Molecular Docking Simulation”

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PubMed1999

Integrating computation and visualization for biomolecular analysis: an example using python and AVS.

One of the challenges in biocomputing is to enable the efficient use of a wide variety of fast-evolving computational methods to simulate, analyze, and understand the complex properties and interactions of molecular systems. Our laboratory investigates several areas including molecular visualization, protein-ligand docking, protein-protein docking, molecular surfaces, and the derivation of phenomenological potentials. In this paper we present an approach based on the Python programming language to achieve a high level of integration between these different computational methods and our primary visualization system AVS. This approach removes many limitations of AVS while increasing dramatically the inter-operability of our computational tools. Several examples are shown to illustrate how this approach enables a high level of integration and inter-operability between different tools, while retaining modularity and avoiding the creation of a large monolithic package that is difficult to extend and maintain.

Capsid↗

PubMed2005

Comparative molecular dynamics simulations of HIV-1 integrase and the T66I/M154I mutant: binding modes and drug resistance to a diketo acid inhibitor.

HIV-1 IN is an essential enzyme for viral replication and an interesting target for the design of new pharmaceuticals for use in multidrug therapy of AIDS. L-731,988 is one of the most active molecules of the class of beta-diketo acids. Individual and combined mutations of HIV-1 IN at residues T66, S153, and M154 confer important degrees of resistance to one or more inhibitors belonging to this class. In an effort to understand the molecular mechanism of the resistance of T66I/M154I IN to the inhibitor L-731,988 and its specific binding modes, we have carried out docking studies, explicit solvent MD simulations, and binding free energy calculations. The inhibitor was docked against different protein conformations chosen from prior MD trajectories, resulting in 2 major orientations within the active site. MD simulations have been carried out for the T66I/M154I DM IN, DM IN in complex with L-731,988 in 2 different orientations, and 1QS4 IN in complex with L-731,988. The results of these simulations show a similar dynamical behavior between T66I/M154I IN alone and in complex with L-731,988, while significant differences are observed in the mobility of the IN catalytic loop (residues 138-149). Water molecules bridging the inhibitor to residues from the active site have been identified, and residue Gln62 has been found to play an important role in the interactions between the inhibitor and the protein. This work provides information about the binding modes of L-731,988, as well as insight into the mechanism of inhibitor-resistance in HIV-1 integrase.

Amino Acid Substitution↗

PubMed2001

A new molecular simulation software package--Peking University Drug Design System (PKUDDS) for structure-based drug design.

We present a comprehensive molecular simulation program package, the Peking University Drug Design System (PKUDDS), which runs on personal computers. PKUDDS has been developed mainly for computer-aided drug design using the methods of two-dimensional quantitative structure-activity relationships, three-dimensional quantitative structure-activity relationships, molecular docking, and database screening. This study presents an overview of its functionality, especially of methods developed in our group. PKUDDS uses genetic algorithms in molecular docking, conformational analysis, and quantitative structure-activity relationships as the most useful optimization technique. A user-friendly graphical interface provides easy access to many functions of PKUDDS. We report some examples of our considerable research using PKUDDS.

Antineoplastic Agents↗

PubMed2004

[A molecular basis for odorant recognition: olfactory receptor pharmacology].

Olfactory receptors (ORs) comprise the largest super-family of rhodopsin-like G-protein coupled receptors (GPCR) that involve the recognition and discrimination of thousands of odorants. We recently succeeded in functional reconstitution of mouse ORs in mammalian cell lines and provided molecular evidence that structurally-related ORs recognized overlapping sets of odorants with distinct ligand specificities. Here we show that mOR-EG, a mouse olfactory receptor that was isolated from a eugenol-responsive cell, recognizes 22 different odorants with EC50 values ranging from a few microM to several hundred microM. We constructed a molecular model of mOR-EG using the recent atomic-level structure of bovine rhodopsin. Site-directed mutations were introduced in a potential ligand-binding pocket based on computational ligand-docking simulation. Mutations of some amino acid residues in TM3, TM5, and TM6 dramatically affected the EC50 value of eugenol in Ca2+ imaging. Finally, we succeeded in rational receptor design with predicted ligand specificity by introducing point mutations in the binding site, confirming the accuracy of the binding site mapping. The current studies also help understand mechanisms underlying molecular recognition by GPCRs, with implications for therapeutic application.

Animals↗

PubMed2004

Topology scanning and putative three-dimensional structure of the extracellular binding domains of the apical sodium-dependent bile acid transporter (SLC10A2).

The apical sodium-dependent bile acid transporter (ASBT, SLC10A2) facilitates the enterohepatic circulation of bile salts and plays a key role in cholesterol metabolism. The membrane topology of ASBT was initially scanned using a consensus topography analysis that predominantly predicts a seven transmembrane (TM) domain configuration adhering to the "positive inside" rule. Membrane topology was further evaluated and confirmed by N-glycosylation-scanning mutagenesis, as reporter sites inserted in the putative extracellular loops 1 and 3 were glycosylated. On the basis of a 7TM topology, we built a three-dimensional model of ASBT using an approach of homology-modeling and remote-threading techniques for the extramembranous domains using bacteriorhodopsin as a scaffold for membrane attachment points; the model was refined using energy minimizations and molecular dynamics simulations. Ramachandran scores and other geometric indicators show that the model is comparable in quality to the crystal structures of similar proteins. Simulated annealing and docking of cholic acid, a natural substrate, onto the protein surface revealed four distinct binding sites. Subsequent site-directed mutagenesis of the predicted binding domain further validated the model. This model agrees further with available data for a pathological mutation (P290S) because the mutant model after in silico mutagenesis loses the ability to bind bile acids.

Amino Acid Sequence↗

PubMed2005

The impact of protein flexibility on protein-protein docking.

Accounting for protein flexibility in protein-protein docking algorithms is challenging, and most algorithms therefore treat proteins as rigid bodies or permit side-chain motion only. While the consequences are obvious when there are large conformational changes upon binding, the situation is less clear for the modest conformational changes that occur upon formation of most protein-protein complexes. We have therefore studied the impact of local protein flexibility on protein-protein association by means of rigid body and torsion angle dynamics simulation. The binding of barnase and barstar was chosen as a model system for this study, because the complexation of these 2 proteins is well-characterized experimentally, and the conformational changes accompanying binding are modest. On the side-chain level, we show that the orientation of particular residues at the interface (so-called hotspot residues) have a crucial influence on the way contacts are established during docking from short protein separations of approximately 5 A. However, side-chain torsion angle dynamics simulations did not result in satisfactory docking of the proteins when using the unbound protein structures. This can be explained by our observations that, on the backbone level, even small (2 A) local loop deformations affect the dynamics of contact formation upon docking. Complementary shape-based docking calculations confirm this result, which indicates that both side-chain and backbone levels of flexibility influence short-range protein-protein association and should be treated simultaneously for atomic-detail computational docking of proteins.

Computer Simulation↗

PubMedDate not supplied

Computer simulation of the receptor-ligand system.

A series of Monte Carlo simulations were carried out for receptor-ligand systems taken from the Protein Data Bank. The models of both objects were based on a simplified low-resolution model. The model chains consisted of alpha carbons and side groups represented by united atoms. The excluded volume effect and specific pairwise contact potential were introduced into the model. The process of docking was simulated step-by-step by random translations and reorientations of the ligand. The stability of complexes formed was investigated for a simple statistical potential between amino acid side groups. The feasibility of the model for finding the binding site on the receptor surface and the proper orientation of docked ligand were also studied.

Algorithms↗

PubMed1999

Identification of substrate binding site of cyclin-dependent kinase 5.

Cyclin-dependent kinase 5 (CDK5), unlike other CDKs, is active only in neuronal cells where its neuron-specific activator p35 is present. However, it phosphorylates serines/threonines in S/TPXK/R-type motifs like other CDKs. The tail portion of neurofilament-H contains more than 50 KSP repeats, and CDK5 has been shown to phosphorylate S/T specifically only in KS/TPXK motifs, indicating highly specific interactions in substrate recognition. CDKs have been shown to have a high preference for a basic residue (lysine or arginine) as the n+3 residue, n being the location in the primary sequence of a phosphoacceptor serine or threonine. Because of the lack of a crystal structure of a CDK-substrate complex, the structural basis for this specific interaction is unknown. We have used site-directed mutagenesis ("charged to alanine") and molecular modeling techniques to probe the recognition interactions for substrate peptide (PKTPKKAKKL) derived from histone H1 docked in the active site of CDK5. The experimental data and computer simulations suggest that Asp86 and Asp91 are key residues that interact with the lysines at positions n+2 and/or n+3 of the substrates.

Amino Acid Sequence↗

PubMed2003

Insight into the mechanism of the IMP-1 metallo-beta-lactamase by molecular dynamics simulations.

Two models, a purely nonbonded model and a cationic dummy atom approach, were examined for the modeling of the binuclear zinc-containing IMP-1 metallo-beta-lactamase in complex with a mercaptocarboxylate inhibitor. The cationic dummy atom approach had substantial advantages as it maintained the initial, experimentally determined geometry of the metal-containing active site during molecular dynamics simulations in water. The method was extended to the modeling of the free enzyme and the enzyme in complex with a cephalosporin substrate docked in an intermediate structure. For all three systems, the modeled complexes and the tetrahedral coordination of the zinc ions were stable. The average zinc-zinc distance increased by approximately 1 A in the substrate complex compared with the inhibitor complex and the free enzyme in which a hydroxide ion acts as a bridging ligand. Thus, the zinc ions are predicted to undergo a back and forth movement upon the cycle of hydrolysis. In contrast to previous assumptions, no interaction of the Asn167 side chain with the bound cephalosporin substrate was observed. Our observations are in agreement with quantum-mechanical calculations and experimental data and indicate that the cationic dummy atom approach is useful to model zinc-containing metallo-beta-lactamases as free proteins, in complex with inhibitors and in complex with substrates.

Binding Sites↗

PubMed2005

Docking simulation with a purine nucleoside specific homology model of deoxycytidine kinase, a target enzyme for anticancer and antiviral therapy.

5'-Phosphorylation, catalyzed by human deoxycytidine kinase (dCK), is a crucial step in the metabolic activation of anticancer and antiviral nucleoside antimetabolites, such as cytarabine (AraC), gemcitabine, cladribine (CdA), and lamivudine. Recently, crystal structures of dCK (dCKc) with various pyrimidine nucleosides as substrates have been reported. However, there is no crystal structure of dCK with a bound purine nucleoside, although purines are good substrates for dCK. We have developed a model of dCK (dCKm) specific for purine nucleosides based on the crystal structure of purine nucleoside bound deoxyguanosine kinase (dGKc) as the template. dCKm is essential for computer aided molecular design (CAMD) of novel anticancer and antiviral drugs that are based on purine nucleosides since these did not bind to dCKc in our docking experiments. The active site of dCKm was larger than that of dCKc and the amino acid (aa) residues of dCKm and dCKc, in particular Y86, Q97, D133, R104, R128, and E197, were not in identical positions. Comparative docking simulations of deoxycytidine (dC), cytidine (Cyd), AraC, CdA, deoxyadenosine (dA), and deoxyguanosine (dG) with dCKm and dCKc were carried out using the FlexX docking program. Only dC (pyrimidine nucleoside) docked into the active site of dCKc but not the purine nucleosides dG and dA. As expected, the active site of dCKm appeared to be more adapted to bind purine nucleosides than the pyrimidine nucleosides. While water molecules were essential for docking experiments using dCKc, the absence of water molecules in dCKm did not affect the ability to correctly dock various purine nucleosides.

Amino Acid Sequence↗

PubMed1996

The simulated binding of (+/-)-2,3-dihydro-5,6-dimethoxy-2-[[1-(phenylmethyl)-4-piperidinyl]meth yl] -1H-inden-1-one hydrochloride (E2020) and related inhibitors to free and acylated acetylcholinesterases and corresponding structure-activity analyses.

The simulated binding profiles of acetylcholine, ACh, and the inhibitor (+/-)-2,3-dihydro-5,6- dimethoxy-2-[[1-(phenylmethyl)-4-piperidinyl]methyl]-1H-inden-1-on e hydrochloride (E2020), 1, and some of its analogs to acetylcholinesterase, AChE, were determined using full force field energetics and allowing complete conformational flexibility in both the ligand and receptor. A new mode of binding of ACh to AChE was found which involves the carboxyl oxygen of ACh interacting with Gly 118 and 119. Multiple modes of binding of 1 and some of its analogs were found which include alignment models observed in previous more restricted modeling studies. The key ligand-receptor interactions identified, and the corresponding energetics, are consistent on a relative basis, with observed binding constants for both the individual isomers of each of the inhibitors, as well as among the inhibitors themselves. The multiple modes of binding of 1 to AChE arises from small changes in binding at a single subsite and also from multiple subsite changes. Thus, an independent subsite model for ligand-receptor binding holds for some modes of binding, but not for others. A comparison of the simulated AChE-1 (and analog inhibitors) binding models to the receptor-independent 3D-QSARs previously developed for this class of inhibitors reveals extensive mutual consistency. The findings from these two modeling studies provides greater guidelines for inhibitor design than can be realized from either one. The combined docking and 3D-QSAR studies permit a detailed understanding of the SAR of more than 100 compound 1 analog inhibitors. A simple molecular recognition model can also be gleaned from the docking studies. A cylindrical "plug" (the inhibitor) having a large dipole moment must sterically fit into a cylindrical hole (the active site gorge of AChE), the lining of which also has a large dipole moment. Our simulations suggest that the dynamic "back door" to the active site of AChE does not form a large enough opening for sufficiently long time periods so as to be an effective entrance/exit pathway.

Acetylcholine↗

PubMed2005

Homology modeling and molecular dynamics simulations of the mu opioid receptor in a membrane-aqueous system.

Three types of opioid receptors-mu, delta, and kappa-belong to the rhodopsin subfamily in the G protein-coupled receptor superfamily. With the recent characterization of the high-resolution X-ray crystal structure of bovine rhodopsin, considerable attention has been focused on molecular modeling of these transmembrane proteins. In this study, a homology model of the mu opioid receptor was constructed based on the X-ray crystal structure of bovine rhodopsin. A phospholipid bilayer was built around the receptor, and two water layers were placed on both surfaces of the lipid bilayer. Molecular-dynamics simulations were carried out by using CHARMM for the entire system, which consisted of 316 amino acid residues, 92 phospholipid molecules, 8327 water molecules, and 11 chloride counter ions-40 931 atoms altogether. The whole system was equilibrated for 250 ps followed by another 2 ns dynamic simulation. The opioid ligand naltrexone was docked into the optimized model, and the critical amino acid residues for binding were identified. The mu opioid receptor homology model optimized in a complete membrane-aqueous system should provide a good starting point for further characterization of the binding modes for opioid ligands. Furthermore, the method developed herein will be applicable to molecular model building to other opioid receptors as well as other GPCRs.

Animals↗

PubMed2006

Finding ligands for G protein-coupled receptors based on the protein-compound affinity matrix.

We developed a novel method of identifying new active ligands based on information related to known active compounds using protein-compound docking simulations, even when the tertiary structure of the actual target receptor protein is unknown. This method was used to find ligands of G protein-coupled receptors (GPCRs), i.e., agonists and antagonists of histamine, adrenaline, serotonin and dopamine receptors. The principal component analysis (PCA) method was applied to the protein-compound affinity matrix, which was given by thorough docking calculations between sets of many protein pockets and chemical compounds. The set of protein pockets did not necessary include the target protein. Each compound was depicted as a point in the PCA space. Compounds in a sphere, whose center was set to the known active compound in the multi-dimensional PCA space or to the average position of several known active compounds, were selected as candidate-hit compounds. Our method was found to be effective for finding the ligands of GPCRs based on known native ligands, even when only the soluble protein structures were used in the docking simulations.

Binding Sites↗

PubMed2002

Identification and mapping of small-molecule binding sites in proteins: computational tools for structure-based drug design.

The number of protein structures is currently increasing at an impressive rate. The growing wealth of data calls for methods to efficiently exploit structural information for medicinal and pharmaceutical purposes. Given the three-dimensional (3D) structure of a validated protein target, the identification of functionally relevant binding sites and the analysis ('mapping') of these sites with respect to molecular recognition properties are important initial tasks in structure-based drug design. To address these tasks, a variety of computational tools have been developed. Approaches to identify binding pockets include geometric analyses of protein surfaces, comparisons of protein structures, similarity searches in databases of protein cavities, and docking scans to reveal areas of high ligand complementarity. In the context of binding-site analysis, powerful data mining tools help to retrieve experimental information about related protein-ligand complexes. To identify interaction hot spots, various potential functions and knowledge-based approaches are available for mapping binding regions. The results may subsequently be used to guide virtual screenings for new ligands via pharmacophore searches or docking simulations.

Artificial Intelligence↗

PubMed2002

Characterization of the retinoid orphan-related receptor-alpha coactivator binding interface: a structural basis for ligand-independent transcription.

The retinoid orphan-related receptor-alpha (RORalpha) is a member of the ROR subfamily of orphan receptors and acts as a constitutive activator of transcription in the absence of exogenous ligands. To understand the basis of this activity, we constructed a homology model of RORalpha using the closely related TRbeta as a template. Molecular modeling suggested that bulky hydrophobic side chains occupy the RORalpha ligand cavity leaving a small but distinct cavity that may be involved in receptor stabilization. This model was subject to docking simulation with a receptor-interacting peptide from the steroid receptor coactivator, GR-interacting protein-1, which delineated a coactivator binding surface consisting of the signature motif spanning helices 3-5 and helix 12 [activation function 2 (AF2)]. Probing this surface with scanning alanine mutagenesis showed structural and functional equivalence between homologous residues of RORalpha and TRbeta. This was surprising (given that RORalpha is a ligand-independent activator, whereas TRbeta has an absolute requirement for ligand) and prompted us to use molecular modeling to identify differences between RORalpha and TRbeta in the way that the AF2 helix interacts with the rest of the receptor. Modeling highlighted a nonconserved amino acid in helix 11 of RORalpha (Phe491) and a short-length of 3.10 helix at the N terminus of AF2 which we suggest 1) ensures that AF2 is locked permanently in the holoconformation described for other liganded receptors and thus 2) enables ligand-independent recruitment of coactivators. Consistent with this, mutation of RORalpha Phe491 to either methionine or alanine (methionine is the homologous residue in TRbeta), reduced and ablated transcriptional activation and recruitment of coactivators, respectively. Furthermore, we were able to reconstitute transcriptional activity for both a deletion mutant of RORalpha lacking AF2, and Phe491Met, by overexpression of a GAL-AF2 fusion protein, demonstrating ligand-independent recruitment of AF2 and a role for Phe491 in recruiting AF2.

Alanine↗

PubMed2000

Single-coordinate-driving method for molecular docking: application to modeling of guest inclusion in cyclodextrin.

An extension of the computer program CICADA has been developed that allows us to use the single-coordinate-driving (SCD) method for flexible molecular docking. The docking procedure is composed of three independent space rotations, three independent translations, and the torsions selected by the user. One of the coordinates is driven; the other coordinates are relaxed. This procedure follows low-energy wells on the potential energy surface of the entire system. The program allows us to dock more than one ligand molecule to the receptor. We ran two test examples, docking N,N-dimethylformamide into alpha-cyclodextrin and R-phenoxypropionic acid into beta-cyclodextrin. The test examples showed that the SCD approach is able to overcome high-energy barriers and to cover the entire box within which the search is performed. The limitations of molecular dynamics docking in comparison with our approach also are discussed. The philosophy of the newly developed approach is not only to find the best dock for the receptor-ligand(s) system, but also to describe all the important binding modes and provide a good starting point for studying the dynamics within the cavity during the docking process.

Carbohydrate Conformation↗

PubMed2000

The role of cavities in protein dynamics: crystal structure of a photolytic intermediate of a mutant myoglobin.

We determined the structure of the photolytic intermediate of a sperm whale myoglobin (Mb) mutant called Mb-YQR [Leu-(B10)-->Tyr; His(E7)-->Gln; Thr(E10)-->Arg] to 1.4-A resolution by ultra-low temperature (20 K) x-ray diffraction. Starting with the CO complex, illumination leads to photolysis of the Fe-CO bond, and migration of the photolyzed carbon monoxide (CO*) to a niche in the protein 8.1 A from the heme iron; this cavity corresponds to that hosting an atom of Xe when the crystal is equilibrated with xenon gas at 7 atmospheres [Tilton, R. F., Jr., Kuntz, I. D. & Petsko, G. A. (1984) Biochemistry 23, 2849-2857]. The site occupied by CO* corresponds to that predicted by molecular dynamics simulations previously carried out to account for the NO geminate rebinding of Mb-YQR observed in laser photolysis experiments at room temperature. This secondary docking site differs from the primary docking site identified by previous crystallographic studies on the photolyzed intermediate of wild-type sperm whale Mb performed at cryogenic temperatures [Teng et al. (1994) Nat. Struct. Biol. 1, 701-705] and room temperature [Srajer et al. (1996) Science 274, 1726-1729]. Our experiment shows that the pathway of a small molecule in its trajectory through a protein may be modified by site-directed mutagenesis, and that migration within the protein matrix to the active site involves a limited number of pre-existing cavities identified in the interior space of the protein.

Amino Acid Sequence↗

PubMed1996

Scoring noncovalent protein-ligand interactions: a continuous differentiable function tuned to compute binding affinities.

Exploitation of protein structures for potential drug leads by molecular docking is critically dependent on methods for scoring putative protein-ligand interactions. An ideal function for scoring must exhibit predictive accuracy and high computational speed, and must be tolerant of variations in the relative protein-ligand molecular alignment and conformation. This paper describes the development of an empirically derived scoring function, based on the binding affinities of protein-ligand complexes coupled with their crystallographically determined structures. The function's primary terms involve hydrophobic and polar complementarity, with additional terms for entropic and solvation effects. The issue of alignment/conformation dependence was solved by constructing a continuous differentiable nonlinear function with the requirement that maxima in ligand conformation/alignment space corresponded closely to crystallographically determined structures. The expected error in the predicted affinity based on cross-validation was 1.0 log unit. The function is sufficiently fast and accurate to serve as the objective function of a molecular-docking search engine. The function is particularly well suited to the docking problem, since it has spatially narrow maxima that are broadly accessible via gradient descent.

Bacterial Proteins↗