An SOA-based model for the integrated provisioning of cloud and grid resources

In the last years, the availability and models of use of networked computing resources within reach of e-Science are rapidly changing and see the coexistence of many disparate paradigms: high-performance computing, grid, and recently cloud. Unfortunately, none of these paradigms is recognized as the ultimate solution, and a convergence of them all should be pursued. At the same time, recent works have proposed a number of models and tools to address the growing needs and expectations in the field of e-Science. In particular, they have shown the advantages and the feasibility of modeling e-Science environments and infrastructures according to the service-oriented architecture. In this paper, we suggest a model to promote the convergence and the integration of the different computing paradigms and infrastructures for the dynamic on-demand provisioning of resources from multiple providers as a cohesive aggregate, leveraging the service-oriented architecture. In addition, we propose a design aimed at endorsing a flexible, modular, workflow-based computing model for e-Science. The model is supplemented by a working prototype implementation together with a case study in the applicative domain of bioinformatics, which is used to validate the presented approach and to carry out some performance and scalability measurements

Theoretical evidence for efficient p-type doping of GaN using beryllium

Ab initio calculations predict that Be is a shallow acceptor in GaN. Its thermal ionization energy is 0.06 eV in wurtzite GaN; the level is valence resonant in the zincblende phase. Be incorporation is severely limited by the formation of Be_3N_2. We show however that co-incorporation with reactive species can enhance the solubility. H-assisted incorporation should lead to high doping levels in MOCVD growth after post-growth annealing at about 850 K. Be-O co-incorporation produces high Be and O concentrations at MBE growth temperatures.Comment: revised Feb 24 199

Common recognition topology of mex transporters of Pseudomonas aeruginosa revealed by molecular modelling

The secondary transporters of the resistance-nodulation-cell division (RND) superfamily mediate multidrug resistance in Gram-negative bacteria like Pseudomonas aeruginosa. Among these RND transporters, MexB, MexF, and MexY, with partly overlapping specificities, have been implicated in pathogenicity. Only the structure of the former has been resolved experimentally, which together with the lack of data about the functional dynamics of the full set of transporters, limited a systematic investigation of the molecular determinants defining their peculiar and shared features. In a previous work (Ramaswamy et al., Front. Microbiol., 2018, 9, 1144), we compared at an atomistic level the two main putative recognition sites (named access and deep binding pockets) of MexB and MexY. In this work, we expand the comparison by performing extended molecular dynamics (MD) simulations of these transporters and the pathologically relevant transporter MexF. We employed a more realistic model of the inner phospholipid membrane of P. aeruginosa and more accurate force-fields. To elucidate structure/dynamics-activity relationships we performed physico-chemical analyses and mapped the binding propensities of several organic probes on all transporters. Our data revealed the presence, also in MexF, of a few multifunctional sites at locations equivalent to the access and deep binding pockets detected in MexB. Furthermore, we report for the first time about the multidrug binding abilities of two out of five gates of the channels deputed to peripheral (early) recognition of substrates. Overall, our findings help to define a common “recognition topology” characterizing Mex transporters, which can be exploited to optimize transport and inhibition propensities of antimicrobial compounds

Interaction of Radiopharmaceuticals with Somatostatin Receptor 2 Revealed by Molecular Dynamics Simulations

The development of drugs targeting somatostatin receptor 2 (SSTR2), generally overexpressed in neuroendocrine tumors, is focus of intense research. A few molecules in conjugation with radionuclides are in clinical use for both diagnostic and therapeutic purposes. These radiopharmaceuticals are composed of a somatostatin analogue biovector conjugated to a chelator moiety bearing the radionuclide. To date, despite valuable efforts, a detailed molecular-level description of the interaction of radiopharmaceuticals in complex with SSTR2 has not yet been accomplished. Therefore, in this work, we carefully analyzed the key dynamical features and detailed molecular interactions of SSTR2 in complex with six radiopharmaceutical compounds selected among the few already in use (64Cu/68Ga-DOTATATE, 68Ga-DOTATOC, 64Cu-SARTATE) and some in clinical development (68Ga-DOTANOC, 64Cu-TETATATE). Through molecular dynamics simulations and exploiting recently available structures of SSTR2, we explored the influence of the different portions of the compounds (peptide, radionuclide, and chelator) in the interaction with the receptor. We identified the most stable binding modes and found distinct interaction patterns characterizing the six compounds. We thus unveiled detailed molecular interactions crucial for the recognition of this class of radiopharmaceuticals. The microscopically well-founded analysis presented in this study provides guidelines for the design of new potent ligands targeting SSTR2

Impact of synthetic conditions on the anisotropic thermal conductivity of poly(3,4-ethylenedioxythiophene) (PEDOT) : a molecular dynamics investigation

In this work we study the effect of different synthetic conditions on thermal transport properties of poly(3,4-ethylenedioxythiophene) (PEDOT) by focusing in particular on the role of proton scavengers. To this aim, different PEDOT samples were generated in silico using a novel computational algorithm based on a combination of first-principles density functional theory and classical molecular dynamics simulations. The corresponding thermal conductivities were then estimated using the approach to equilibrium molecular dynamics methodology. The results show that the initial synthetic conditions strongly affect the corresponding thermal conductivities, which display variations up to a factor of ∼2 depending on the proton scavenger. By decomposing the thermal conductivity tensor along the direction of maximum chain alignment and the corresponding perpendicular directions, we attribute the thermal conductivity differences to the variations in the average polymer chain length λave. A dependence of the thermal conductivity with the polydispersity index was finally observed, suggesting a possible role of intercrystallite chains in enhancing thermal transport properties. By means of the Green-Kubo modal analysis, we eventually characterize the vibrational modes involved in PEDOT thermal transport and investigate how they are related to the thermal conductivity values of the samples

Recognition of quinolone antibiotics by the multidrug efflux transporter MexB of Pseudomonas aeruginosa

The drug/proton antiporter MexB is the engine of the major efflux pump MexAB-OprM in Pseudomonas aeruginosa. This protein is known to transport a large variety of compounds, including antibiotics, thus conferring a multi-drug resistance phenotype. Due to the difficulty of producing co-crystals, only two X-ray structures of MexB in a complex with ligands are available to date, and mechanistic aspects are largely hypothesized based on the body of data collected for the homologous protein AcrB of Escherichia coli. In particular, a recent study (Ornik-Cha, Wilhelm, Kobylka et al., Nat. Commun., 2021, 12, 6919) reported a co-crystal structure of AcrB in a complex with levofloxacin, an antibiotic belonging to the important class of (fluoro)-quinolones. In this work, we performed a systematic ensemble docking campaign coupled to the cluster analysis and molecular-mechanics optimization of docking poses to study the interaction between 36 quinolone antibiotics and MexB. We additionally investigated surface complementarity between each molecule and the transporter and thoroughly assessed the computational protocol adopted against the known experimental data. Our study reveals different binding preferences of the investigated compounds towards the sub-sites of the large deep binding pocket of MexB, supporting the hypothesis that MexB substrates oscillate between different binding modes with similar affinity. Interestingly, small changes in the molecular structure translate into significant differences in MexB-quinolone interactions. All the predicted binding modes are available for download and visualization at the following link: https://www.dsf.unica.it/dock/mexb/quinolones

Molecular simulations of SSTR2 dynamics and interaction with ligands

The cyclic peptide hormone somatostatin regulates physiological processes involved in growth and metabolism, through its binding to G-protein coupled somatostatin receptors. The isoform 2 (SSTR2) is of particular relevance for the therapy of neuroendocrine tumours for which different analogues to somatostatin are currently in clinical use. We present an extensive and systematic computational study on the dynamics of SSTR2 in three different states: active agonist-bound, inactive antagonist-bound and apo inactive. We exploited the recent burst of SSTR2 experimental structures to perform μs-long multi-copy molecular dynamics simulations to sample conformational changes of the receptor and rationalize its binding to different ligands (the agonists somatostatin and octreotide, and the antagonist CYN154806). Our findings suggest that the apo form is more flexible compared to the holo ones, and confirm that the extracellular loop 2 closes upon the agonist octreotide but not upon the antagonist CYN154806. Based on interaction fingerprint analyses and free energy calculations, we found that all peptides similarly interact with residues buried into the binding pocket. Conversely, specific patterns of interactions are found with residues located in the external portion of the pocket, at the basis of the extracellular loops, particularly distinguishing the agonists from the antagonist. This study will help in the design of new somatostatin-based compounds for theranostics of neuroendocrine tumours

Ag/In lead‐free double perovskites

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Molecular Insights Into Binding and Activation of the Human KCNQ2 Channel by Retigabine

Voltage-gated potassium channels of the Kv7.x family are involved in a plethora of biological processes across many tissues in animals, and their misfunctioning could lead to several pathologies ranging from diseases caused by neuronal hyperexcitability, such as epilepsy, or traumatic injuries and painful diabetic neuropathy to autoimmune disorders. Among the members of this family, the Kv7.2 channel can form hetero-tetramers together with Kv7.3, forming the so-called M-channels, which are primary regulators of intrinsic electrical properties of neurons and of their responsiveness to synaptic inputs. Here, prompted by the similarity between the M-current and that in Kv7.2 alone, we perform a computational-based characterization of this channel in its different conformational states and in complex with the modulator retigabine. After validation of the structural models of the channel by comparison with experimental data, we investigate the effect of retigabine binding on the two extreme states of Kv7.2 (resting-closed and activated-open). Our results suggest that binding, so far structurally characterized only in the intermediate activated-closed state, is possible also in the other two functional states. Moreover, we show that some effects of this binding, such as increased flexibility of voltage sensing domains and propensity of the pore for open conformations, are virtually independent on the conformational state of the protein. Overall, our results provide new structural and dynamic insights into the functioning and the modulation of Kv7.2 and related channels

Molecular determinants of avoidance and inhibition of Pseudomonas aeruginosa MexB efflux pump

: Transporters of the resistance-nodulation-cell division (RND) superfamily of proteins are the dominant multidrug efflux power of Gram-negative bacteria. The major RND efflux pump of Pseudomonas aeruginosa is MexAB-OprM, in which the inner membrane transporter MexB is responsible for the recognition and binding of compounds. The high importance of this pump in clinical antibiotic resistance made it a subject of intense investigations and a promising target for the discovery of efflux pump inhibitors. This study is focused on a series of peptidomimetic compounds developed as effective inhibitors of MexAB-OprM. We performed multi-copy molecular dynamics simulations, machine-learning (ML) analyses, and site-directed mutagenesis of MexB to investigate interactions of MexB with representatives of efflux avoiders, substrates, and inhibitors. The analysis of both direct and water-mediated protein-ligand interactions revealed characteristic patterns for each class, highlighting significant differences between them. We found that efflux avoiders poorly interact with the access binding site of MexB, and inhibition engages amino acid residues that are not directly involved in binding and transport of substrates. In agreement, machine-learning models selected different residues predictive of MexB substrates and inhibitors. The differences in interactions were further validated by site-directed mutagenesis. We conclude that the substrate translocation and inhibition pathways of MexB split at the interface (between the main putative binding sites) and at the deep binding pocket and that interactions outside of the hydrophobic patch contribute to the inhibition of MexB. This molecular-level information could help in the rational design of new inhibitors and antibiotics less susceptible to the efflux mechanism. IMPORTANCE Multidrug transporters recognize and expel from cells a broad range of ligands including their own inhibitors. The difference between the substrate translocation and inhibition routes remains unclear. In this study, machine learning and computational and experimental approaches were used to understand dynamics of MexB interactions with its ligands. Our results show that some ligands engage a certain combination of polar and charged residues in MexB binding sites to be effectively expelled into the exit funnel, whereas others engage aromatic and hydrophobic residues that slow down or hinder the next step in the transporter cycle. These findings suggest that all MexB ligands fit into this substrate-inhibitor spectrum depending on their physico-chemical structures and properties