Conformational profile of bombesin assessed using different computational protocols

The present work involves the study of the conformational profile of bombesin using different computational procedures used to explore the configurational space based on molecular dynamics simulations. Specifically, the present study describes the effect of using Berendsen's versus Langevin's thermostat and on the other hand, the use of the multicanonical replica exchange molecular dynamics as compared to standard molecular dynamics. In these simulations the solvent was modeled using the Onufriev, Bashford and Case implementation of Generalized Born procedure. The detailed computational analysis agrees well with the aggregated information previously reported in the NMR study of the peptide in a mixture of trifluoroethanol/water. Present results show a clear preference for the peptide to attain a helical structure on the segment 6-14, with a tendency to adopt a α-helix at the C-terminus aligning the aromatic residues Trp8 and His12 together with Gln7, known to be important for peptide mediated activation. Finally, the three methodologies used in the present work yield similar structural results, although a detailed analysis reveals biases that need to be considered when performing this kind of studies.     

Quantitative online prediction of peptide binding to the major histocompatibility complex

With its implications for vaccine discovery, the accurate prediction of T cell epitopes is one of the key aspirations of computational vaccinology. We have developed a robust multivariate statistical method, based on partial least squares, for the quantitative prediction of peptide binding to major histocompatibility complexes (MHC), the principal checkpoint on the antigen presentation pathway. As a service to the immunobiology community, we have made a Perl implementation of the method available via a World Wide Web server. We call this server MHCPred. Access to the server is freely available from the URL: http://www.jenner.ac.uk/MHCPred. We have exemplified our method with a model for peptides binding to the common human MHC molecule HLA-B*3501.     

Visual Abstractions of Solvent Pathlines near Protein Cavities

Water is known to play a crucial role in protein structure, flexibility and activity. The use of molecular dynamics simulations allows detailed studies of complex protein‐solvent interactions. Cluster analysis and density‐based approaches have been successfully used for the identification and analysis of conserved water molecules and hydration patterns of proteins. However, appropriate tools for analysing long‐time molecular dynamics simulations with respect to tracking and visualising the paths of solvent molecules are lacking. Our method focuses on visualising the solvent paths entering and leaving cavities of the protein and allows to study the route and dynamics of the exchange of tightly bound internal water molecules with the bulk solvent. The proposed visualisation also represents dynamic properties such as direction and velocity in the solvent. Especially, by clustering similar path‐lines with respect to designated properties the visualisation can be abstracted to represent the principal paths of solvent molecules through the cavities. Its application in the analysis of long‐time scale molecular dynamics simulations not only confirmed conjectures based on previous manual observations made by chance, but also led to novel insights into the dynamical and structural role of water molecules and its interplay with protein structure.     

Reactions in complex biologically relevant systems: challenges for computational approaches

The ever increasing power of computational architectures allows to investigate reactive phenomena in complex biomolecular systems and environments. Results from such calculations can be compared with experiment and give important insight into microscopic aspects of chemical reactions. We discuss two examples of biologically relevant reaction mechanisms: double proton transfer in a DNA–base pair analogue for which detailed experimental information is available and ligand (re)binding in myoglobin (Mb). For the double proton transfer in the DNA–base pair analogue ab initio molecular dynamics simulations provide direct information on the infrared spectrum that has also been observed experimentally. In the case of ligand rebinding in MbNO, we discuss results from reactive molecular dynamics simulations to investigate the rebinding probability after photodissociation of the NO from myoglobin and compare the findings with experiment. Both reaction types pose different challenges and we highlight and address the computational difficulties in each case. In particular, we explore and discuss the possibilities and limitations of current computational methods to understand reactive processes in systems where several degrees of freedom are important.     

Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.     

Predicting the structures of complexes between phosphoinositide 3-kinase (PI3K) and romidepsin-related compounds for the drug design of PI3K/histone deacetylase dual inhibitors using computational docking and the ligand-based drug design approach

Predictions of the three-dimensional (3D) structures of the complexes between phosphoinositide 3-kinase (PI3K) and two inhibitors were conducted using computational docking and the ligand-based drug design approach. The obtained structures were refined by structural optimizations and molecular dynamics (MD) simulations. The ligands were located deep inside the ligand binding pocket of the p110α subunit of PI3K, and the hydrogen bond formations and hydrophobic effects of the surrounding amino acids were predicted. Although rough structures were obtained for the PI3K–inhibitor complexes before the MD simulations, the refinement of the structures by these simulations clarified the hydrogen bonding patterns of the complexes.     

An artificial neural network-based multiscale method for hybrid atomistic-continuum simulations

This paper presents an artificial neural network-based multiscale method for coupling continuum and molecular simulations. Molecular dynamics modelling is employed as a local “high resolution” refinement of computational data required by the continuum computational fluid dynamics solver. The coupling between atomistic and continuum simulations is obtained by an artificial neural network (ANN) methodology. The ANN aims to optimise the transfer of information through minimisation of (1) the computational cost by avoiding repetitive atomistic simulations of nearly identical states, and (2) the fluctuation strength of the atomistic outputs that are fed back to the continuum solver. Results are presented for prototype flows such as the isothermal Couette flow with slip boundary conditions and the slip Couette flow with heat transfer.     

Theoretical studies on the interactions of XIAP-BIR3 domain with bicyclic and tricyclic core monovalent Smac mimetics

X-linked IAP can bind caspase-9 and inhibit its activity. Mitochondrial protein Smac/DIABLO can also interact with XIAP and relieve the inhibition on caspase-9 to induce apoptosis. A series of artificial Smac mimetics have been used to mimic the Smac N-terminal tetrapeptide AVPI to bind to XIAP-BIR3, but these structural diverse mimetics exhibited distinct binding affinities. To get an insight into the binding nature and optimize the structures, molecular docking and dynamics simulations were used to study the binding of XIAP-BIR3 with three groups of Smac mimetics. The docking results reveal that these Smac mimetics anchored on the surface groove of XIAP-BIR3 and superimposed well with AVPI. The modifications on the seven-membered ring of bicyclic core segment do not strengthen the binding affinity, while a benzyl introduced to the five-membered ring is favorable to the binding. Molecular dynamics simulations on three typical systems show that these complexes are very stable. Hydrogen bonds between the bicyclic core segment and Thr308 play critical roles in maintaining the stability of complex. The binding free energies calculated by MM_PBSA method are consistent with the experimental results.     

Molecular dynamics simulation of non-covalent single-walled carbon nanotube functionalization with surfactant peptides

Non-covalent functionalized single-walled carbon nanotubes (SWCNTs) with improved solubility and biocompatibility can successfully transfer drugs, DNA, RNA, and proteins into the target cells. Theoretical studies such as molecular docking and molecular dynamics simulations in fully atomistic scale were used to investigate the hydrophobic and aromatic π–π-stacking interaction of designing four novel surfactant peptides for non-covalent functionalization of SWCNTs. The results indicated that the designed peptides have binding affinity towards SWCNT with constant interactions during MD simulation times, and it can even be improved by increasing the number of tryptophan residues. The aromatic content of the peptides plays a significant role in their adsorption in SWCNT wall. The data suggest that π–π stacking interaction between the aromatic rings of tryptophan and π electrons of SWCNTs is more important than hydrophobic effects for dispersing carbon nanotubes; nevertheless SWCNTs are strongly hydrophobic in front of smooth surfaces. The usage of aromatic content of peptides for forming SWCNT/peptide complex was proved successfully, providing new insight into peptide design strategies for future nano-biomedical applications.     

Sampling activated mechanisms in proteins with the activation-relaxation technique

The activated dynamics of proteins occur on time scales of milliseconds and longer. Standard all-atom molecular dynamics simulations are limited to much shorter times, of the order of tens of nanoseconds. Therefore, many activated mechanisms that are crucial for long-time dynamics will not be observed in such molecular dynamics simulation; different methods are required. Here, we describe in detail the activation-relaxation technique (ART) that generates directly activated mechanisms. The method is defined in the configurational energy landscape and defines moves in a two step fashion: (a) a configuration is first brought from a local minimum to a nearby first-order saddle point (the activation); and (b) the configuration is relaxed to a new metastable state (the relaxation). The method has already been applied to a wide range of problems in condensed matter, including metallic glasses, amorphous semiconductors and silica glass. We review the algorithm in detail, discuss some previously published results and present simulations of activated mechanisms for a two-helix bundle protein using an all-atom energy function.     

Comparison of homology models and X-ray structures of the nuclear receptor CAR: assessing the structural basis of constitutive activity

The constitutive androstane receptor (CAR) possesses an intrinsic basal activity whose structural basis has been analysed during the last decade. Recently, we published a homology model of the CAR ligand binding domain (LBD) based on the X-ray structures of the closely related pregnane X (PXR) and vitamin D (VDR) receptor. A detailed analysis of the homology model and molecular dynamics (MD) simulations afforded us to propose a potential mechanism underlying the constitutive activity of CAR. Almost simultaneously, X-ray structures of human and mouse CAR LBD were released. In the present study, a detailed analysis and comparison of homology model and X-ray structures is carried out in order to evaluate the quality and reliability of our homology modelling procedure. The hypothesis of the constitutive activity which we proposed on the basis of our modelling results was tested for consistency with the crystal structures. In addition, the features stated to be essential for the basal activity based on the X-ray data were investigated by means of molecular dynamics simulations. Our results show that the homology modelling procedure was able to predict the CAR LBD structure with high accuracy. Structural features that have been revealed as critical for constitutive activity in the model are also observed in the X-ray structures. Furthermore, the MD simulations of the CAR X-ray structures and a detailed analysis of other NRs clarify the role of distinct structural features that have been assigned an important role for the constitutive activity.     

Stability tests on known and misfolded structures with discrete and all atom molecular dynamics simulations

The brevity of molecular dynamics simulations often limits their utility in developing and evaluating structural models of proteins. The duration of simulations can be increased greatly using discrete molecular dynamics (DMD). However, the trade off is that coarse graining, implicit solvent, and other time-saving procedures reduce the accuracy of DMD simulations. Here we address some of these issues by comparing results of DMD and conventional all atom MD simulations on proteins of known structure and misfolded proteins. DMD simulations were performed at a range of temperatures to identify a 'physiological' temperature for DMD that mimicked molecular motions of conventional MD simulations at 310K. We also compared results obtained with a new implicit solvent model developed here based on Miyazawa-Jernigan interaction pair potential to those obtained with a previously used model based on Kyte-Doolittle hydropathy scale. We compared DMD and all atom molecular dynamics with explicit water by simulating both correctly and incorrectly folded structures, and monomeric and dimeric α β-barrel structures to analyze the ability of these procedures to distinguish between good and bad models. Deviations from the correct structures were substantially greater with DMD, as would be expected from coarse-graining and longer simulation time. Deviations were smallest for β-strands and greatest for coiled loops. Structures of the incorrectly folded models were very poorly preserved during the DMD simulations; but both methods were able to distinguish between the correct and the incorrect structures based on differences in the magnitudes of the root mean squared deviation (RMSD) from the starting conformation.     

Rational design of cyclic peptides to disrupt TGF-Β/SMAD7 signaling in heterotopic ossification

The human TGF-β/SMAD7 signaling has been recognized as an attractive target of heterotopic ossification (HO). Here, we report a successful rational design of cyclic peptides to disrupt the signaling pathway by targeting TGF-β–receptor complex. The intermolecular interaction between TGF-β and its cognate receptor is characterized in detail using molecular dynamics simulation, binding energetic analysis, and alanine scanning. With the computational analysis a binding loop of receptor protein is identified that plays an essential role in the peptide-mediated TGF-β–receptor interaction. Subsequently, the loop is stripped from the protein context to generate a linear peptide segment, which possesses considerable flexibility and intrinsic disorder, and thus would incur a large entropy penalty upon binding to TGF-β. In order to minimize the unfavorable entropic effect, the linear peptide is cyclized by adding a disulfide bond between the N- and C-terminal cysteine residues of the peptide, resulting in a cyclic peptide. In vitro fluorescence anisotropy assays substantiate that the cyclic peptide can bind tightly to TGF-β with determined K_d value of 54 μM. We also demonstrated that structural optimization can further improve the peptide affinity by site-directed mutagenesis of selected residues based on the computationally modeled complex structure of TGF-β with the cyclic peptide.     

Practical estimation of high dimensional stochastic differential mixed-effects models

Stochastic differential equations (SDEs) are established tools for modeling physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE, intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework for modeling dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics, using SDEs. These stochastic differential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein-Uhlenbeck (OU) and the square root models.     

Three dimensional numerical simulations of long-span bridge aerodynamics, using block-iterative coupling and DES

The design of long-span bridges often depends on wind tunnel testing of sectional or full aeroelastic models. Some progress has been made to find a computational alternative to replace these physical tests. In this paper, an innovative computational fluid dynamics (CFD) method is presented, where the fluid-structure interaction (FSI) is solved through a self-developed code combined with an ANSYS-CFX solver. Then an improved CFD method based on block-iterative coupling is also proposed. This method can be readily used for two dimensional (2D) and three dimensional (3D) structure modelling. Detached-Eddy simulation for 3D viscous turbulent incompressible flow is applied to the 3D numerical analysis of bridge deck sections. Firstly, 2D numerical simulations of a thin airfoil demonstrate the accuracy of the present CFD method. Secondly, numerical simulations of a U-shape beam with both 2D and 3D modelling are conducted. The comparisons of aerodynamic force coefficients thus obtained with wind tunnel test results well meet the prediction that 3D CFD simulations are more accurate than 2D CFD simulations. Thirdly, 2D and 3D CFD simulations are performed for two generic bridge deck sections to produce their aerodynamic force coefficients and flutter derivatives. The computed values agree well with the available computational and wind tunnel test results. Once again, this demonstrates the accuracy of the proposed 3D CFD simulations. Finally, the 3D based wake flow vision is captured, which shows another advantage of 3D CFD simulations. All the simulation results demonstrate that the proposed 3D CFD method has good accuracy and significant benefits for aerodynamic analysis and computational FSI studies of long-span bridges and other slender structures.     

A putative bioactive conformation for the altered peptide ligand of myelin basic protein and inhibitor of experimental autoimmune encephalomyelitis [Arg91, Ala96] MBP87-99

[Arg(91), Ala(96)] MBP(87-99) is an altered peptide ligand (APL) of myelin basic protein (MBP), shown to actively inhibit experimental autoimmune encephalomyelitis (EAE), which is studied as a model of multiple sclerosis (MS). The APL has been rationally designed by substituting two of the critical residues for recognition by the T-cell receptor. A conformational analysis of the APL has been sought using a combination of 2D NOESY nuclear magnetic resonance (NMR) experiments and detailed molecular dynamics (MD) calculations, in order to comprehend the stereoelectronic requirements for antagonistic activity, and to propose a putative bioactive conformation based on spatial proximities of the native peptide in the crystal structure. The proposed structure presents backbone similarity with the native peptide especially at the N-terminus, which is important for major histocompatibility complex (MHC) binding. Primary (Val(87), Phe(90)) and secondary (Asn(92), Ile(93), Thr(95)) MHC anchors occupy the same region in space, whereas T-cell receptor (TCR) contacts (His(88), Phe(89)) have different orientation between the two structures. A possible explanation, thus, of the antagonistic activity of the APL is that it binds to MHC, preventing the binding of myelin epitopes, but it fails to activate the TCR and hence to trigger the immunologic response. NMR experiments coupled with theoretical calculations are found to be in agreement with X-ray crystallography data and open an avenue for the design and synthesis of novel peptide restricted analogues as well as peptide mimetics that rises as an ultimate goal.     

The three-dimensional structure of Arabidopsis thaliana O-methyltransferase predicted by homology-based modelling

O-methylation of flavonoid compounds is an important enzymatic reaction since it not only reduces the chemical reactivity of their phenolic hydroxyl groups but also increases their lipophilicity and, hence, their intracellular compartmentation. Several genes encoding flavonoid O-methyltransferases (OMTs) have been isolated and characterized both at the molecular and biochemical levels. In contrast with mammalian enzymes, plant OMTs exhibit narrow substrate specificities as well as position-specific activities, so that the homology comparison, derived using programs such as BLAST can not provide sufficient information on the enzyme function or its substrate preference. In order to study these characteristics, therefore, another approach, homology-based modelling is being carried out. We report here the determination of the 3-D structure of Arabidopsis thaliana O-methyltransferase, AtOMT1 as well as its dynamics when complexed with its substrate. The predicted structure obtained by homology-based modelling is conserved during molecular dynamics simulations. AtOMT1 exhibits a structure similar to that of caffeic acid O-methyltransferase, COMT when the latter was used as a template. Whereas COMT includes 20 alpha-helices and nine beta-sheets, AtOMT1 has 16 and 9, respectively. Although the homology between both proteins is higher than 77% and all amino acids surrounding the active sites, except one residue, are similar in their primary sequences, the two proteins exhibit different substrate preferences. The differences in substrate specificity may be explained on the basis of the predicted structures of the protein and its complex with the substrate. In addition, docking the substrate into the active site of the protein allowed the study of the structural change of the active site on the dihedral angle distribution of the residues surrounding the active site.     

Homology modeling and molecular dynamics simulation of the HIF2α degradation-related HIF2α-VHL complex

BackgroundHypoxia-inducible factor 2 alpha (HIF2α), prolyl hydroxylase domain protein 2 (PHD2), and the von Hippel Lindau tumor suppressor protein (pVHL) are three principal proteins in the oxygen-sensing pathway. Under normoxic conditions, a conserved proline in HIF2α is hydroxylated by PHD2 in an oxygen-dependent manner, and then pVHL binds and promotes the degradation of HIF2α. However, the crystal structure of the HIF2α-pVHL complex has not yet been established, and this has limited research on the interaction between HIF and pVHL. Here, we constructed a structural model of a 23-residue HIF2α peptide (528–550)-pVHL-ElonginB-ElonginC complex by using homology modeling and molecular dynamics simulations. We also applied these methods to HIF2α mutants (HYP531PRO, F540L, A530 V, A530T, and G537R) to reveal structural defects that explain how these mutations weaken the interaction with pVHL.MethodsHomology modeling and molecular dynamics simulations were used to construct a three-dimensional (3D) structural model of the HIF2α-VHL complex. Subsequently, MolProbity, an active validation tool, was used to analyze the reliability of the model. Molecular mechanics energies combined with the generalized Born and surface area continuum solvation (MM-GBSA) and solvated interaction energy (SIE) methods were used to calculate the binding free energy between HIF2a and pVHL, and the stability of the simulation system was evaluated by using root mean square deviation (RMSD) analysis. We also determined the secondary structure of the system by using the definition of secondary structure of proteins (DSSP) algorithm. Finally, we investigated the structural significance of specific point mutations known to have clinical implications.ResultsWe established a reliable structural model of the HIF2α-pVHL complex, which is similar to the crystal structure of HIF1α in 1LQB. Furthermore, we compared the structural model of the HIF2α-pVHL complex and the HIF2α (HYP531P, F540L, A530V, A530T, and G537R)-pVHL mutants on the basis of RMSD, DSSP, binding free energy, and hydrogen bonding. The experimental data indicate that the stability of the structural model of the HIF2α-pVHL complex is higher than that of the mutants, consistently with clinical observations.ConclusionsThe structural model of the HIF2α-pVHL complex presented in this study enhances understanding of how HIF2α is captured by pVHL. Moreover, the important contact amino acids that we identified may be useful in the development of drugs to treat HIF2a-related diseases.     

Catalytic mechanism of l,l-diaminopimelic acid with diaminopimelate epimerase by molecular docking simulations

Peptidoglycan, a key constituent of bacterial cell walls, is currently the target of broad spectrum antibiotics and a new research field involves both design and synthesis of inhibitors of its biosynthesis. Most bacteria require either lysine, or its biosynthetic precursor, diaminopimelate (meso-DAP), as a component of the peptidoglycan layer of the cell wall. In this paper, molecular modelling studies were undertaken in order to shed light on the molecular basis of interaction between (2S,6S)-diaminopimelic acid (l,l-DAP) (1) with its target enzyme DAP-epimerase, since this is a key step in the lysine biosynthetic path leading to (2R,6S)-diaminopimelic acid (meso-DAP) (2). In particular, the docking of the ligand-enzyme complex was studied by means of MD simulations and DFT computations in order to ascertain the optimal structural requirements for the epimerization reaction. Molecular dynamics simulations clearly showed that the configuration of the distal carbon C6 of l,l-DAP is critical for complex formation since both amino and carboxylate groups are involved in Hbonding interactions with the active site residues. Furthermore, the interactions occurring between the functional groups bonded to the C2 and some residues of the binding cavity immobilize the ligand in a position appropriate for the epimerization reaction, i.e., exactly in the middle of the two catalytic residues Cys73 and Cys217 as confirmed by DFT quantum mechanical computation of the Michaelis complex. All this mechanistic information could be useful for the rational design of new potential antibiotic drugs effective as inhibitors of peptidoglycan biosynthesis.