833 resultados para Approches in silico
Resumo:
Computational modelling of mechanisms underlying processes in the real world can be of great value in understanding complex biological behaviours. Uptake in general biology and ecology has been rapid. However, it often requires specific data sets that are overly costly in time and resources to collect. The aim of the current study was to test whether a generic behavioural ecology model constructed using published data could give realistic outputs for individual species. An individual-based model was developed using the Pattern-Oriented Modelling (POM) strategy and protocol, based on behavioural rules associated with insect movement choices. Frugivorous Tephritidae (fruit flies) were chosen because of economic significance in global agriculture and the multiple published data sets available for a range of species. The Queensland fruit fly (Qfly), Bactrocera tryoni, was identified as a suitable individual species for testing. Plant canopies with modified architecture were used to run predictive simulations. A field study was then conducted to validate our model predictions on how plant architecture affects fruit flies’ behaviours. Characteristics of plant architecture such as different shapes, e.g., closed-canopy and vase-shaped, affected fly movement patterns and time spent on host fruit. The number of visits to host fruit also differed between the edge and centre in closed-canopy plants. Compared to plant architecture, host fruit has less contribution to effects on flies’ movement patterns. The results from this model, combined with our field study and published empirical data suggest that placing fly traps in the upper canopy at the edge should work best. Such a modelling approach allows rapid testing of ideas about organismal interactions with environmental substrates in silico rather than in vivo, to generate new perspectives. Using published data provides a saving in time and resources. Adjustments for specific questions can be achieved by refinement of parameters based on targeted experiments.
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Hantaviruses, members of the genus Hantavirus in the Bunyaviridae family, are enveloped single-stranded RNA viruses with tri-segmented genome of negative polarity. In humans, hantaviruses cause two diseases, hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS), which vary in severity depending on the causative agent. Each hantavirus is carried by a specific rodent host and is transmitted to humans through excreta of infected rodents. The genome of hantaviruses encodes four structural proteins: the nucleocapsid protein (N), the glycoproteins (Gn and Gc), and the polymerase (L) and also the nonstructural protein (NSs). This thesis deals with the functional characterization of hantavirus N protein with regard to its structure. Structural studies of the N protein have progressed slowly and the crystal structure of the whole protein is still not available, therefore biochemical assays coupled with bioinformatical modeling proved essential for studying N protein structure and functions. Presumably, during RNA encapsidation, the N protein first forms intermediate trimers and then oligomers. First, we investigated the role of N-terminal domain in the N protein oligomerization. The results suggested that the N-terminal region of the N protein forms a coiled-coil, in which two antiparallel alpha helices interact via their hydrophobic seams. Hydrophobic residues L4, I11, L18, L25 and V32 in the first helix and L44, V51, L58 and L65 in the second helix were crucial for stabilizing the structure. The results were consistent with the head-to-head, tail-to-tail model for hantavirus N protein trimerization. We demonstrated that an intact coiled-coil structure of the N terminus is crucial for the oligomerization capacity of the N protein. We also added new details to the head-to-head, tail-to-tail model of trimerization by suggesting that the initial step is based on interaction(s) between intact intra-molecular coiled-coils of the monomers. We further analyzed the importance of charged aa residues located within the coiled-coil for the N protein oligomerization. To predict the interacting surfaces of the monomers we used an upgraded in silico model of the coiled-coil domain that was docked into a trimer. Next the predicted target residues were mutated. The results obtained using the mammalian two-hybrid assay suggested that conserved charged aa residues within the coiled-coil make a substantial contribution to the N protein oligomerization. This contribution probably involves the formation of interacting surfaces of the N monomers and also stabilization of the coiled-coil via intramolecular ionic bridging. We proposed that the tips of the coiled-coils are the first to come into direct contact and thus initiate tight packing of the three monomers into a compact structure. This was in agreement with the previous results showing that an increase in ionic strength abolished the interaction between N protein molecules. We also showed that residues having the strongest effect on the N protein oligomerization are not scattered randomly throughout the coiled-coil 3D model structure, but form clusters. Next we found evidence for the hantaviral N protein interaction with the cytoplasmic tail of the glycoprotein Gn. In order to study this interaction we used the GST pull-down assay in combination with mutagenesis technique. The results demonstrated that intact, properly folded zinc fingers of the Gn protein cytoplasmic tail as well as the middle domain of the N protein (that includes aa residues 80 248 and supposedly carries the RNA-binding domain) are essential for the interaction. Since hantaviruses do not have a matrix protein that mediates the packaging of the viral RNA in other negatve stranded viruses (NSRV), hantaviral RNPs should be involved in a direct interaction with the intraviral domains of the envelope-embedded glycoproteins. By showing the N-Gn interaction we provided the evidence for one of the crucial steps in the virus replication at which RNPs are directed to the site of the virus assembly. Finally we started analysis of the N protein RNA-binding region, which is supposedly located in the middle domain of the N protein molecule. We developed a model for the initial step of RNA-binding by the hantaviral N protein. We hypothesized that the hantaviral N protein possesses two secondary structure elements that initiate the RNA encapsidation. The results suggest that amino acid residues (172-176) presumably act as a hook to catch vRNA and that the positively charged interaction surface (aa residues 144-160) enhances the initial N-RNA interacation. In conclusion, we elucidated new functions of hantavirus N protein. Using in silico modeling we predicted the domain structure of the protein and using experimental techniques showed that each domain is responsible for executing certain function(s). We showed that intact N terminal coiled-coil domain is crucial for oligomerization and charged residues located on its surface form a interaction surface for the N monomers. The middle domain is essential for interaction with the cytoplasmic tail of the Gn protein and RNA binding.
Resumo:
Veri-aivoeste suojelee aivoja verenkierron vierasaineilta. Veri-aivoestettä tutkivia in vivo ja in vitro -menetelmiä on raportoitu laajasti kirjallisuudessa. Yhdisteiden farmakokinetiikka aivoissa kuvaavia tietokonemalleja on esitetty vain muutamia. Tässä tutkimuksessa kerättiin kirjallisuudesta aineisto eri in vitro ja in vivo -menetelmillä määritetyistä veri-aivoesteen permeabiliteettikertoimista. Lisäksi tutkimuksessa rakennettiin kaksi veri-aivoesteen farmakokineettista tietokonemallia, mikrodialyysimalli ja efluksimalli. Mikrodialyysimalli on yksinkertainen kahdesta tilasta (verenkierto ja aivot) koostuva farmakokineettinen malli. Mikrodialyysimallilla simuloitiin in vivo määritettyjen parametrien perusteella viiden yhdisteen pitoisuuksia rotan aivoissa ja verenkierrossa. Mallilla ei saatu täsmällisesti in vivo -tilannetta vastaavia pitoisuuskuvaajia johtuen mallin rakenteessa tehdyistä yksinkertaistuksista, kuten aivokudostilan ja kuljetinproteiinien kinetiikan puuttuminen. Efluksimallissa on kolme tilaa, verenkierto, veri-aivoesteen endoteelisolutila ja aivot. Efluksimallilla tutkittiin teoreettisten simulaatioiden avulla veri-aivoesteen luminaalisella membraanilla sijaitsevan aktiivisen efluksiproteiinin ja passiivisen permeaation merkitystä yhdisteen pitoisuuksiin aivojen solunulkoisessa nesteessä. Tutkittava parametri oli vapaan yhdisteen pitoisuuksien suhde aivojen ja verenkierron välillä vakaassa tilassa (Kp,uu). Tuloksissa havaittiin efluksiproteiinin vaikutus pitoisuuksiin Michaelis-Mentenin kinetiikan mukaisesti. Efluksimalli sopii hyvin teoreettisten simulaatioiden tekemiseen. Malliin voidaan lisätä aktiivisia kuljettimia. Teoreettisten simulaatioiden avulla voidaan yhdistää in vitro ja in vivo tutkimuksien tuloksia ja osatekijöitä voidaan tutkia yhdessä simulaatiossa.
Resumo:
QSPR-malli kuvaa kvantitatiivista riippuvuutta muuttujien ja biologisen ominaisuuden välillä. Näin ollen QSPR mallit ovat käyttökelpoisia lääkekehityksen apuvälineitä. Kirjallisessa osassa kerrotaan sarveiskalvon, suoliston ja veriaivoesteen permeabiliteetin malleista. Useimmin käytettyjä muuttujia ovat yhdisteen rasvaliukoisuus, polaarinen pinta-ala, vetysidosten muodostuminen ja varaus. Myös yhdisteen koko vaikuttaa läpäisevyyteen, vaikka tutkimuksissa onkin erilaista tietoa tämän merkittävyydestä. Malliin vaikuttaa myös muiden kuin mallissa mukana olevien muuttujien suuruusluokka esimerkkinä Lipinskin ‖rule of 5‖ luokittelu. Tässä luokittelussa yhdisteen ominaisuus ei saa ylittää tiettyjä raja-arvoja. Muussa tapauksessa sen imeytyminen suun kautta otettuna todennäköisesti vaarantuu. Lisäksi kirjallisessa osassa tutustuttiin kuljetinproteiineihin ja niiden toimintaan silmän sarveiskalvossa, suolistossa ja veriaivoesteessä. Nykyisin on kehitetty erilaisia QSAR-malleja kuljetinproteiineille ennustamaan mahdollisten substraatittien tai inhibiittorien vuorovaikutuksia kuljetinproteiinin kanssa. Kokeellisen osan tarkoitus oli rakentaa in silico -malli sarveiskalvon passiiviselle permeabiliteetille. Työssä tehtiin QSPR-malli 54 yhdisteen ACDLabs-ohjelmalla laskettujen muuttujien arvojen avulla. Permeabiliteettikertoimien arvot saatiin kirjallisuudesta kanin sarveiskalvon läpäisevyystutkimuksista. Lopullisen mallin muuttujina käytettiin oktanoli-vesijakaantumiskerrointa (logD) pH:ssa 7,4 ja vetysidosatomien kokonaismäärää. Yhtälö oli muotoa log10(permeabiliteettikerroin) = -3,96791 - 0,177842Htotal + 0,311963logD(pH7,4). R2-korrelaatiokerroin oli 0,77 ja Q2-korrelaatiokerroin oli 0,75. Lopullisen mallin hyvyyttä arvioitiin 15 yhdisteen ulkoisella testijoukolla, jolloin ennustettua permeabiliteettia verrattiin kokeelliseen permeabiliteettiin. QSPR-malli arvioitiin myös farmakokineettisen simulaation avulla. Simulaatiossa laskettiin seitsemän yhdisteen kammionestepitoisuudet in vivo vakaassa tilassa käyttäen simulaatioissa QSPR mallilla ennustettuja permeabiliteettikertoimia. Lisäksi laskettiin sarveiskalvon imeytymisen nopeusvakio (Kc) 13 yhdisteelle farmakokineettisen simulaation avulla ja verrattiin tätä lopullisella mallilla ennustettuun permeabiliteettiin. Tulosten perusteella saatiin tilastollisesti hyvä QSPR-malli kuvaamaan sarveiskalvon passiivista permeabiliteettia, jolloin tätä mallia voidaan käyttää lääkekehityksen alkuvaiheessa. QSPR-malli ennusti permeabiliteettikertoimet hyvin, mikä nähtiin vertaamalla mallilla ennustettuja arvoja kokeellisiin tuloksiin. Lisäksi yhdisteiden kammionestepitoisuudet voitiin simuloida käyttäen apuna QSPR-mallilla ennustettuja permeabiliteettikertoimien arvoja.
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The blood-brain barrier (BBB) is a unique barrier that strictly regulates the entry of endogenous substrates and xenobiotics into the brain. This is due to its tight junctions and the array of transporters and metabolic enzymes that are expressed. The determination of brain concentrations in vivo is difficult, laborious and expensive which means that there is interest in developing predictive tools of brain distribution. Predicting brain concentrations is important even in early drug development to ensure efficacy of central nervous system (CNS) targeted drugs and safety of non-CNS drugs. The literature review covers the most common current in vitro, in vivo and in silico methods of studying transport into the brain, concentrating on transporter effects. The consequences of efflux mediated by p-glycoprotein, the most widely characterized transporter expressed at the BBB, is also discussed. The aim of the experimental study was to build a pharmacokinetic (PK) model to describe p-glycoprotein substrate drug concentrations in the brain using commonly measured in vivo parameters of brain distribution. The possibility of replacing in vivo parameter values with their in vitro counterparts was also studied. All data for the study was taken from the literature. A simple 2-compartment PK model was built using the Stella™ software. Brain concentrations of morphine, loperamide and quinidine were simulated and compared with published studies. Correlation of in vitro measured efflux ratio (ER) from different studies was evaluated in addition to studying correlation between in vitro and in vivo measured ER. A Stella™ model was also constructed to simulate an in vitro transcellular monolayer experiment, to study the sensitivity of measured ER to changes in passive permeability and Michaelis-Menten kinetic parameter values. Interspecies differences in rats and mice were investigated with regards to brain permeability and drug binding in brain tissue. Although the PK brain model was able to capture the concentration-time profiles for all 3 compounds in both brain and plasma and performed fairly well for morphine, for quinidine it underestimated and for loperamide it overestimated brain concentrations. Because the ratio of concentrations in brain and blood is dependent on the ER, it is suggested that the variable values cited for this parameter and its inaccuracy could be one explanation for the failure of predictions. Validation of the model with more compounds is needed to draw further conclusions. In vitro ER showed variable correlation between studies, indicating variability due to experimental factors such as test concentration, but overall differences were small. Good correlation between in vitro and in vivo ER at low concentrations supports the possibility of using of in vitro ER in the PK model. The in vitro simulation illustrated that in the simulation setting, efflux is significant only with low passive permeability, which highlights the fact that the cell model used to measure ER must have low enough paracellular permeability to correctly mimic the in vivo situation.
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The cis-regulatory regions on DNA serve as binding sites for proteins such as transcription factors and RNA polymerase. The combinatorial interaction of these proteins plays a crucial role in transcription initiation, which is an important point of control in the regulation of gene expression. We present here an analysis of the performance of an in silico method for predicting cis-regulatory regions in the plant genomes of Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) on the basis of free energy of DNA melting. For protein-coding genes, we achieve recall and precision of 96% and 42% for Arabidopsis and 97% and 31% for rice, respectively. For noncoding RNA genes, the program gives recall and precision of 94% and 75% for Arabidopsis and 95% and 90% for rice, respectively. Moreover, 96% of the false-positive predictions were located in noncoding regions of primary transcripts, out of which 20% were found in the first intron alone, indicating possible regulatory roles. The predictions for orthologous genes from the two genomes showed a good correlation with respect to prediction scores and promoter organization. Comparison of our results with an existing program for promoter prediction in plant genomes indicates that our method shows improved prediction capability.
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An in silico approach was adopted to identify potential cyclooxygenase-2 inhibitors through molecular docking studies. The in vivo studies indicated that synthetic palmitoyl derivatives of salicylic acid, para amino phenol, para amino benzoic acid, and anthranilic acid possessed significant pharmacological activities like anti-inflammatory, analgesic, and antipyretic activities. None of the tested substances produced any significant gastric lesions in experimental animals. In an attempt to understand the ligandprotein interactions in terms of the binding affinity, the above synthetic molecules were subjected to docking analysis using AutoDock. The palmitoyl derivatives palmitoyl anthranilic acid, palmitoyl para amino benzoic acid, palmitoyl para amino phenol, and palmitoyl salicylic acid showed better binding energy than the known inhibitor diclofenac bound to 1PXX. All the palmitoyl derivatives made similar interactions with the binding site residues of cyclooxygenase-2 as compared to that of the known inhibitor. Thus, structure-based drug discovery approach was successfully employed to identify some promising pro-drugs for the treatment of pain and inflammation.
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Convergence of the vast sequence space of proteins into a highly restricted fold/conformational space suggests a simple yet unique underlying mechanism of protein folding that has been the subject of much debate in the last several decades. One of the major challenges related to the understanding of protein folding or in silico protein structure prediction is the discrimination of non-native structures/decoys from the native structure. Applications of knowledge-based potentials to attain this goal have been extensively reported in the literature. Also, scoring functions based on accessible surface area and amino acid neighbourhood considerations were used in discriminating the decoys from native structures. In this article, we have explored the potential of protein structure network (PSN) parameters to validate the native proteins against a large number of decoy structures generated by diverse methods. We are guided by two principles: (a) the PSNs capture the local properties from a global perspective and (b) inclusion of non-covalent interactions, at all-atom level, including the side-chain atoms, in the network construction accommodates the sequence dependent features. Several network parameters such as the size of the largest cluster, community size, clustering coefficient are evaluated and scored on the basis of the rank of the native structures and the Z-scores. The network analysis of decoy structures highlights the importance of the global properties contributing to the uniqueness of native structures. The analysis also exhibits that the network parameters can be used as metrics to identify the native structures and filter out non-native structures/decoys in a large number of data-sets; thus also has a potential to be used in the protein `structure prediction' problem.
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Two transcription termination mechanisms - intrinsic and Rho-dependent - have evolved in bacteria. The Rho factor occurs in most bacterial lineages, and has been hypothesized to play a global regulatory role. Genome-wide studies using microarray, 2D-gel electrophoresis and ChIP-chip provided evidence that Rho serves to silence transcription from horizontally acquired genes and prophages in Escherichia coli K-12, implicating the factor to be a part of the ``cellular immune mechanism'' protecting against deleterious phages and aberrant gene expression from acquired xenogenic DNA. We have investigated this model by adopting an alternate in silico approach and have extended the study to other species. Our analysis shows that several genomic islands across diverse phyla have under-representation of intrinsic terminators, similar to that experimentally observed in E. coli K-12. This implies that Rho-dependent termination is the predominant process operational in these islands and that silencing of foreign DNA is a conserved function of Rho. From the present analysis, it is evident that horizontally acquired islands have lost intrinsic terminators to facilitate Rho-dependent termination. These results underscore the importance of Rho as a conserved, genome-wide sentinel that regulates potentially toxic xenogenic DNA. (C) 2012 Elsevier B.V. All rights reserved.
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Abrin, an A/B toxin obtained from the Abrus precatorius plant is extremely toxic and a potential bio-warfare agent. Till date there is no antidote or vaccine available against this toxin. The only known neutralizing monoclonal antibody against abrin, namely D6F10, has been shown to rescue the toxicity of abrin in cells as well as in mice. The present study focuses on mapping the epitopic region to understand the mechanism of neutralization of abrin by the antibody D6F10. Truncation and mutational analysis of abrin A chain revealed that the amino acids 74-123 of abrin A chain contain the core epitope and the residues Thr112, Gly114 and Arg118 are crucial for binding of the antibody. In silico analysis of the position of the mapped epitope indicated that it is present close to the active site cleft of abrin A chain. Thus, binding of the antibody near the active site blocks the enzymatic activity of abrin A chain, thereby rescuing inhibition of protein synthesis by the toxin in vitro. At 1: 10 molar concentration of abrin: antibody, the antibody D6F10 rescued cells from abrin-mediated inhibition of protein synthesis but did not prevent cell attachment of abrin. Further, internalization of the antibody bound to abrin was observed in cells by confocal microscopy. This is a novel finding which suggests that the antibody might function intracellularly and possibly explains the rescue of abrin's toxicity by the antibody in whole cells and animals. To our knowledge, this study is the first report on a neutralizing epitope for abrin and provides mechanistic insights into the poorly understood mode of action of anti-A chain antibodies against several toxins including ricin.
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The understanding of protein-protein interactions is indispensable in comprehending most of the biological processes in a cell. Small-scale experiments as well as large-scale high-throughput techniques over the past few decades have facilitated identification and analysis of protein-protein interactions which form the basis of much of our knowledge on functional and regulatory aspects of proteins. However, such rich catalog of interaction data should be used with caution when establishing protein-protein interactions in silico, as the high-throughput datasets are prone to false positives. Numerous computational means developed to pursue genome-wide studies on protein-protein interactions at times overlook the mechanistic and molecular details, thus questioning the reliability of predicted protein-protein interactions. We review the development, advantages, and shortcomings of varied approaches and demonstrate that by providing a structural viewpoint in terms of shape complementarity and interaction energies at protein-protein interfaces coupled with information on expression and localization of proteins homologous to an interacting pair, it is possible to assess the credibility of predicted interactions in biological context. With a focus on human pathogen Mycobacterium tuberculosis H37Rv, we show that such scrupulous use of details at the molecular level can predict physicochemically viable protein-protein interactions across host and pathogen. Such predicted interactions have the potential to provide molecular basis of probable mechanisms of pathogenesis and hence open up ways to explore their usefulness as targets in the light of drug discovery. (c) 2014 IUBMB Life, 66(11):759-774, 2014
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Electronic structures and dynamics are the key to linking the material composition and structure to functionality and performance.
An essential issue in developing semiconductor devices for photovoltaics is to design materials with optimal band gaps and relative positioning of band levels. Approximate DFT methods have been justified to predict band gaps from KS/GKS eigenvalues, but the accuracy is decisively dependent on the choice of XC functionals. We show here for CuInSe2 and CuGaSe2, the parent compounds of the promising CIGS solar cells, conventional LDA and GGA obtain gaps of 0.0-0.01 and 0.02-0.24 eV (versus experimental values of 1.04 and 1.67 eV), while the historically first global hybrid functional, B3PW91, is surprisingly the best, with band gaps of 1.07 and 1.58 eV. Furthermore, we show that for 27 related binary and ternary semiconductors, B3PW91 predicts gaps with a MAD of only 0.09 eV, which is substantially better than all modern hybrid functionals, including B3LYP (MAD of 0.19 eV) and screened hybrid functional HSE06 (MAD of 0.18 eV).
The laboratory performance of CIGS solar cells (> 20% efficiency) makes them promising candidate photovoltaic devices. However, there remains little understanding of how defects at the CIGS/CdS interface affect the band offsets and interfacial energies, and hence the performance of manufactured devices. To determine these relationships, we use the B3PW91 hybrid functional of DFT with the AEP method that we validate to provide very accurate descriptions of both band gaps and band offsets. This confirms the weak dependence of band offsets on surface orientation observed experimentally. We predict that the CBO of perfect CuInSe2/CdS interface is large, 0.79 eV, which would dramatically degrade performance. Moreover we show that band gap widening induced by Ga adjusts only the VBO, and we find that Cd impurities do not significantly affect the CBO. Thus we show that Cu vacancies at the interface play the key role in enabling the tunability of CBO. We predict that Na further improves the CBO through electrostatically elevating the valence levels to decrease the CBO, explaining the observed essential role of Na for high performance. Moreover we find that K leads to a dramatic decrease in the CBO to 0.05 eV, much better than Na. We suggest that the efficiency of CIGS devices might be improved substantially by tuning the ratio of Na to K, with the improved phase stability of Na balancing phase instability from K. All these defects reduce interfacial stability slightly, but not significantly.
A number of exotic structures have been formed through high pressure chemistry, but applications have been hindered by difficulties in recovering the high pressure phase to ambient conditions (i.e., one atmosphere and room temperature). Here we use dispersion-corrected DFT (PBE-ulg flavor) to predict that above 60 GPa the most stable form of N2O (the laughing gas in its molecular form) is a 1D polymer with an all-nitrogen backbone analogous to cis-polyacetylene in which alternate N are bonded (ionic covalent) to O. The analogous trans-polymer is only 0.03-0.10 eV/molecular unit less stable. Upon relaxation to ambient conditions both polymers relax below 14 GPa to the same stable non-planar trans-polymer, accompanied by possible electronic structure transitions. The predicted phonon spectrum and dissociation kinetics validate the stability of this trans-poly-NNO at ambient conditions, which has potential applications as a new type of conducting polymer with all-nitrogen chains and as a high-energy oxidizer for rocket propulsion. This work illustrates in silico materials discovery particularly in the realm of extreme conditions.
Modeling non-adiabatic electron dynamics has been a long-standing challenge for computational chemistry and materials science, and the eFF method presents a cost-efficient alternative. However, due to the deficiency of FSG representation, eFF is limited to low-Z elements with electrons of predominant s-character. To overcome this, we introduce a formal set of ECP extensions that enable accurate description of p-block elements. The extensions consist of a model representing the core electrons with the nucleus as a single pseudo particle represented by FSG, interacting with valence electrons through ECPs. We demonstrate and validate the ECP extensions for complex bonding structures, geometries, and energetics of systems with p-block character (C, O, Al, Si) and apply them to study materials under extreme mechanical loading conditions.
Despite its success, the eFF framework has some limitations, originated from both the design of Pauli potentials and the FSG representation. To overcome these, we develop a new framework of two-level hierarchy that is a more rigorous and accurate successor to the eFF method. The fundamental level, GHA-QM, is based on a new set of Pauli potentials that renders exact QM level of accuracy for any FSG represented electron systems. To achieve this, we start with using exactly derived energy expressions for the same spin electron pair, and fitting a simple functional form, inspired by DFT, against open singlet electron pair curves (H2 systems). Symmetric and asymmetric scaling factors are then introduced at this level to recover the QM total energies of multiple electron pair systems from the sum of local interactions. To complement the imperfect FSG representation, the AMPERE extension is implemented, and aims at embedding the interactions associated with both the cusp condition and explicit nodal structures. The whole GHA-QM+AMPERE framework is tested on H element, and the preliminary results are promising.
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Viral infections remain a serious global health issue. Metagenomic approaches are increasingly used in the detection of novel viral pathogens but also to generate complete genomes of uncultivated viruses. In silico identification of complete viral genomes from sequence data would allow rapid phylogenetic characterization of these new viruses. Often, however, complete viral genomes are not recovered, but rather several distinct contigs derived from a single entity are, some of which have no sequence homology to any known proteins. De novo assembly of single viruses from a metagenome is challenging, not only because of the lack of a reference genome, but also because of intrapopulation variation and uneven or insufficient coverage. Here we explored different assembly algorithms, remote homology searches, genome-specific sequence motifs, k-mer frequency ranking, and coverage profile binning to detect and obtain viral target genomes from metagenomes. All methods were tested on 454-generated sequencing datasets containing three recently described RNA viruses with a relatively large genome which were divergent to previously known viruses from the viral families Rhabdoviridae and Coronaviridae. Depending on specific characteristics of the target virus and the metagenomic community, different assembly and in silico gap closure strategies were successful in obtaining near complete viral genomes.
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In a multi-target complex network, the links (L-ij) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K-i, K-m, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.
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A doença de Parkinson (DP) é a desordem neurodegenerativa motora mais frequente, com uma prevalência de, aproximadamente, 1% entre indivíduos com mais de 60 anos de idade, aumentando para 4 a 5% entre os indivíduos com idade superior a 85 anos. Esta condição é caracterizada pela perda seletiva dos neurônios dopaminérgicos da substância negra e pela presença de inclusões protéicas ricas em α-sinucleína nos neurônios sobreviventes. Pouco se sabe sobre a etiologia e a patogênese da DP. A maioria dos casos aparece esporadicamente, podendo estar associados a diversos fatores de risco ambientais e genéticos. Na última década, estudos de ligação identificaram 15 loci cromossômicos (PARK1 a PARK15) relacionados à DP e, nestes, um novo gene, ATP13A2, tem sido associado a casos de DP de início precoce. Esse gene está situado no 1p36 e codifica a proteína ATPase tipo-P da subfamília P5, de localização lisossômica, que é expressa em diversos tecidos, principalmente no cérebro. Mutações em ATP13A2 levam à formação de proteínas truncadas que ficam retidas no reticulo endoplasmático e posteriormente são degradadas pelo proteossomo, podendo causar a disfunção proteossômica, decorrente da sobrecarga gerada pela proteína mutante, ou causar a disfunção lisossômica, ambas gerando agregação tóxica. Este trabalho tem como objetivo realizar a análise molecular do gene ATP13A2 em uma amostra de 116 pacientes brasileiros com DP, de manifestação precoce (<50 anos), de forma a avaliar se mutações neste gene representam um fator de risco para a DP. O DNA foi extraído a partir de leucócitos do sangue periférico ou de saliva e a análise molecular dos éxons 2, 3, 12, 13, 14, 15, 16, 26 e 27, bem como, dos limites íntronéxons foi realizada por sequenciamento automático dos produtos da PCR. Identificamos oito variantes de sequência: quatro variantes intrônicas (uma no íntron 2, uma no íntron 13 e duas no íntron 27) e quatro variantes silenciosas (uma no éxon 3, 16, 26 e 27). Com base em dados da literatura e através de análises in silico e comparação com amostras controle, classificamos a alteração intrônica c.3084- 3C>T, e as alterações silenciosas c.2970G>A e c.3192C>T como não patogênicas; as alterações intrônicas c.106-30G>T, c.1306+42_1306+43 insC e c.3083+24C>T, e as alterações silenciosas c.132A>G e c.1610G>T foram classificadas como provavelmente não patogênicas. Nosso achados corroboram àqueles encontrados em outras populações e indicam que mutações no gene ATP13A2 não são uma causa comum de DP na amostra de pacientes brasileiros analisados. No entanto, se faz necessário estender nossas análises para outras regiões gênicas, a fim de determinar o real papel deste gene na etiologia da DP em nossa população.