Relative edge energy in the stability of transition metal nanoclusters of different motifs

When a structure is reduced to a nanometer scale, the proportion of the lowly-coordinated edge atoms increases significantly, which can play a crucial role in determining both their geometric and electronic properties, as demonstrated by the recently established generalized Wulff construction principle [S. F. Li, et al., Phys. Rev. Lett., 2013, 111, 115501]. Consequently, it is of great interest to clarify quantitatively the role of the edge atoms that dominate the motifs of these nanostructures. In principle, establishing an effective method valid for determining the absolute value of the surface energy and particularly the edge energy for a given nanostructure is expected to resolve such a problem. However, hitherto, it is difficult to obtain the absolute edge energy of transition metal clusters, particularly when their sizes approach the nanometer regime. In this paper, taking Ru nanoclusters as a prototypical example, our first-principles calculations introduce the concept of relative edge energy (REE), reflecting the net edge atom effect over the surface (facet) atom effect, which is fairly powerful to quasi-quantitatively estimate the critical size at which the crossover occurs between different configurations of a given motif, such as from an icosahedron to an fcc nanocrystal. By contrast, the bulk effect should be re-considered to rationalize the power of the REE in predicting the relative stability of larger nanostructures between different motifs, such as fcc-like and hcp-like nanocrystals

Substrate co-doping modulates electronic metal-support interactions and significantly enhances single-atom catalysis

Transitional metal nanoparticles or atoms deposited on appropriate substrates can lead to highly economical, efficient, and selective catalysis. One of the greatest challenges is to control the electronic metal–support interactions (EMSI) between the supported metal atoms and the substrate so as to optimize their catalytic performance. Here, from first-principles calculations, we show that an otherwise inactive Pd single adatom on TiO2(110) can be tuned into a highly effective catalyst, e.g. for O2 adsorption and CO oxidation, by purposefully selected metal–nonmetal co-dopant pairs in the substrate. Such an effect is proved here to result unambiguously from a significantly enhanced EMSI. A nearly linear correlation is noted between the strength of the EMSI and the activation of the adsorbed O2 molecule, as well as the energy barrier for CO oxidation. Particularly, the enhanced EMSI shifts the frontier orbital of the deposited Pd atom upward and largely enhances the hybridization and charge transfer between the O2 molecule and the Pd atom. Upon co-doping, the activation barrier for CO oxidation on the Pd monomer is also reduced to a level comparable to that on the Pd dimer which was experimentally reported to be highly efficient for CO oxidation. The present findings provide new insights into the understanding of the EMSI in heterogeneous catalysis and can open new avenues to design and fabricate cost-effective single-atom-sized and/or nanometer-sized catalysts

An oxidized magnetic Au single atom on doped TiO2(110) becomes a high performance CO oxidation catalyst due to the charge effect

Catalysis using gold nanoparticles supported on oxides has been under extensive investigation for many important application processes. However, how to tune the charge state of a given Au species to perform a specific chemical reaction, e.g. CO oxidation, remains elusive. Here, using first-principles calculations, we show clearly that an intrinsically inert Au anion deposited on oxygen-deficient TiO2(110) (Au@TiO2(110)) can be tuned and optimized into a highly effective single atom catalyst (SAC), due to the depletion of the d-orbital by substrate doping. Particularly, Ni- and Cu-doped Au@TiO2 complexes undergo a reconstruction driven by one of the two dissociated O atoms upon CO oxidation. The remaining O atom heals the surface oxygen vacancy and results in a stable bow-shaped surface “O–Au–O” species; thereby the highly oxidized Au single atom now exhibits magnetism and dramatically enhanced activity and stability for O2 activation and CO oxidation, due to the emergence of high density of states near the Fermi level. Based on further extensive calculations, we establish the “charge selection rule” for O2 activation and CO oxidation on Au: the positively charged Au SAC is more active than its negatively charged counterpart for O2 activation, and the more positively charged the Au, the more active it is

Simultaneous 13N-Ammonia and gadolinium first-pass myocardial perfusion with quantitative hybrid PET-MR imaging: a phantom and clinical feasibility study

Background Positron emission tomography (PET) is the non-invasive reference standard for myocardial blood flow (MBF) quantification. Hybrid PET-MR allows simultaneous PET and cardiac magnetic resonance (CMR) acquisition under identical experimental and physiological conditions. This study aimed to determine feasibility of simultaneous 13N-Ammonia PET and dynamic contrast-enhanced CMR MBF quantification in phantoms and healthy volunteers. Methods Images were acquired using a 3T hybrid PET-MR scanner. Phantom study: MBF was simulated at different physiological perfusion rates and a protocol for simultaneous PET-MR perfusion imaging was developed. Volunteer study: five healthy volunteers underwent adenosine stress. 13N-Ammonia and gadolinium were administered simultaneously. PET list mode data was reconstructed using ordered subset expectation maximisation. CMR MBF was quantified using Fermi function-constrained deconvolution of arterial input function and myocardial signal. PET MBF was obtained using a one-tissue compartment model and image-derived input function. Results Phantom study: PET and CMR MBF measurements demonstrated high repeatability with intraclass coefficients 0.98 and 0.99, respectively. There was high correlation between PET and CMR MBF (r = 0.98, p < 0.001) and good agreement (bias − 0.85 mL/g/min; 95% limits of agreement 0.29 to − 1.98). Volunteer study: Mean global stress MBF for CMR and PET were 2.58 ± 0.11 and 2.60 ± 0.47 mL/g/min respectively. On a per territory basis, there was moderate correlation (r = 0.63, p = 0.03) and agreement (bias − 0.34 mL/g/min; 95% limits of agreement 0.49 to − 1.18). Conclusion Simultaneous MBF quantification using hybrid PET-MR imaging is feasible with high test repeatability and good to moderate agreement between PET and CMR. Future studies in coronary artery disease patients may allow cross-validation of techniques

High inertness of W@Si-12 cluster toward O-2 molecule

The geometry, electronic structure, and reactivity with O2 molecules of an isolated W@Si12 cluster have been investigated by first principles simulations. The results confirm that O2 can weakly adsorb on the HP-W@Si12 cage with a binding energy of 0.004 to 0.027 eV. O2 may dissociate on the cluster by overcoming energy barrier of at least 0.593 eV. However, this is a spin-forbidden reaction, rendering the high inertness of the HP-W@Si12 cluster toward O2. These results confirm the high inertness of the W@Si12 cluster toward O2 molecules in ambient conditions, in close agreement with experimental observations of magic cluster of W@Si12

A novel series of compositionally biased substitution matrices for comparing Plasmodium proteins

<p>Abstract</p> <p>Background</p> <p>The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent a fully accurate substitution model for proteins characterized by a biased amino acid composition. This problem has been addressed recently by adjusting existing matrices, however, to date, no empirical approach has been taken to build matrices which offer a substitution model for comparing proteins sharing an amino acid compositional bias. Here, we present a novel procedure to construct series of symmetrical substitution matrices to align proteins from similarly biased <it>Plasmodium </it>proteomes.</p> <p>Results</p> <p>We generated substitution matrices by selecting from the BLOCKS database those multiple alignments with a compositional bias similar to that of <it>P. falciparum </it>and <it>P. yoelii </it>proteins. A novel 'fuzzy' clustering method was adopted to group sequences within these alignments, showing that this method retains more complete information on the amino acid substitutions when compared to hierarchical clustering. We assessed the performance against the BLOSUM62 series and showed that the usage of our matrices results in an improvement in the performance of BLAST database searches, greatly reducing the number of false positive hits. We then demonstrated applications of the use of novel matrices to improve the annotation of homologs between the two <it>Plasmodium </it>species and to classify members of the <it>P. falciparum </it>RIFIN/STEVOR family.</p> <p>Conclusion</p> <p>We confirmed that in the case of compositionally biased proteins, standard BLOSUM matrices are not suited for optimal alignments, and specific substitution matrices are required. In addition, we showed that the usage of these matrices leads to a reduction of false positive hits, facilitating the automatic annotation process.</p

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

Increased retention of functional fusions to toxic genes in new two-hybrid libraries of the E. coli strain MG1655 and B. subtilis strain 168 genomes, prepared without passaging through E. coli

BACKGROUND: Cloning of genes in expression libraries, such as the yeast two-hybrid system (Y2H), is based on the assumption that the loss of target genes is minimal, or at worst, managable. However, the expression of genes or gene fragments that are capable of interacting with E. coli or yeast gene products in these systems has been shown to be growth inhibitory, and therefore these clones are underrepresented (or completely lost) in the amplified library. RESULTS: Analysis of candidate genes as Y2H fusion constructs has shown that, while stable in E. coli and yeast for genetic studies, they are rapidly lost in growth conditions for genomic libraries. This includes the rapid loss of a fragment of the E. coli cell division gene ftsZ which encodes the binding site for ZipA and FtsA. Expression of this clone causes slower growth in E. coli. This clone is also rapidly lost in yeast, when expressed from a GAL1 promoter, relative to a vector control, but is stable when the promoter is repressed. We have demonstrated in this report that the construction of libraries for the E. coli and B. subtilis genomes without passaging through E. coli is practical, but the number of transformants is less than for libraries cloned using E. coli as a host. Analysis of several clones in the libraries that are strongly growth inhibitory in E. coli include genes for many essential cellular processes, such as transcription, translation, cell division, and transport. CONCLUSION: Expression of Y2H clones capable of interacting with E. coli and yeast targets are rapidly lost, causing a loss of complexity. The strategy for preparing Y2H libraries described here allows the retention of genes that are toxic when inappropriately expressed in E. coli, or yeast, including many genes that represent potential antibacterial targets. While these methods are generally applicable to the generation of Y2H libraries from any source, including mammalian and plant genomes, the potential of functional clones interacting with host proteins to inhibit growth would make this approach most relevant for the study of prokaryotic genomes

GPS-CCD: A Novel Computational Program for the Prediction of Calpain Cleavage Sites

As one of the most essential post-translational modifications (PTMs) of proteins, proteolysis, especially calpain-mediated cleavage, plays an important role in many biological processes, including cell death/apoptosis, cytoskeletal remodeling, and the cell cycle. Experimental identification of calpain targets with bona fide cleavage sites is fundamental for dissecting the molecular mechanisms and biological roles of calpain cleavage. In contrast to time-consuming and labor-intensive experimental approaches, computational prediction of calpain cleavage sites might more cheaply and readily provide useful information for further experimental investigation. In this work, we constructed a novel software package of GPS-CCD (Calpain Cleavage Detector) for the prediction of calpain cleavage sites, with an accuracy of 89.98%, sensitivity of 60.87% and specificity of 90.07%. With this software, we annotated potential calpain cleavage sites for hundreds of calpain substrates, for which the exact cleavage sites had not been previously determined. In this regard, GPS-CCD 1.0 is considered to be a useful tool for experimentalists. The online service and local packages of GPS-CCD 1.0 were implemented in JAVA and are freely available at: http://ccd.biocuckoo.org/