Oriented control of Al locations in the framework of Al-Ge-ITQ-13 for catalyzing methanol conversion to propene

Locations of Al in the Al-Ge-ITQ-13 framework were regulated by adding different amounts of Ge and AI for the purpose of developing a potential methanol-to-propene catalyst. Ge had no significant influence on acid site number, strength, and type, but influenced acid site distribution and catalytic properties. The Al sited at T2, T5, and particularly T9 sites rapidly deactivated Al-Ge-ITQ-13, but attempts to locate AI at T3 and T6 by adjusting Ge and Al content remarkably increased its catalytic life and propene selectivity, by 686% and 44% respectively. A simple method for roughly estimating the great effects of acid site distribution and density on the catalytic life was developed by measuring the intensity of Al-27 MAS NMR signal around 51.4-53.4 ppm, even though changes in acid site density are always accompanied by alterations of acid site distribution. The hydrothermal treatment of Al-Ge-ITQ-13 confirms its potential for use in industry. (C) 2016 Elsevier Inc. All rights reserved

Oriented control of Al locations in the framework of Al-Ge-ITQ-13 for catalyzing methanol conversion to propene

Locations of Al in the Al-Ge-ITQ-13 framework were regulated by adding different amounts of Ge and AI for the purpose of developing a potential methanol-to-propene catalyst. Ge had no significant influence on acid site number, strength, and type, but influenced acid site distribution and catalytic properties. The Al sited at T2, T5, and particularly T9 sites rapidly deactivated Al-Ge-ITQ-13, but attempts to locate AI at T3 and T6 by adjusting Ge and Al content remarkably increased its catalytic life and propene selectivity, by 686% and 44% respectively. A simple method for roughly estimating the great effects of acid site distribution and density on the catalytic life was developed by measuring the intensity of Al-27 MAS NMR signal around 51.4-53.4 ppm, even though changes in acid site density are always accompanied by alterations of acid site distribution. The hydrothermal treatment of Al-Ge-ITQ-13 confirms its potential for use in industry. (C) 2016 Elsevier Inc. All rights reserved

Immunopeptidomic Analysis Reveals That Deamidated HLA-bound Peptides Arise Predominantly from Deglycosylated Precursors

The presentation of post-translationally modified (PTM) peptides by cell surface HLA molecules has the potential to increase the diversity of targets for surveilling T cells. Although immunopeptidomics studies routinely identify thousands of HLA-bound peptides from cell lines and tissue samples, in-depth analyses of the proportion and nature of peptides bearing one or more PTMs remains challenging. Here we have analyzed HLA-bound peptides from a variety of allotypes and assessed the distribution of mass spectrometry-detected PTMs, finding deamidation of asparagine or glutamine to be highly prevalent. Given that asparagine deamidation may arise either spontaneously or through enzymatic reaction, we assessed allele-specific and global motifs flanking the modified residues. Notably, we found that the N-linked glycosylation motif NX(S/T) was highly abundant across asparagine-deamidated HLA-bound peptides. This finding, demonstrated previously for a handful of deamidated T cell epitopes, implicates a more global role for the retrograde transport of nascently N-glycosylated polypeptides from the ER and their subsequent degradation within the cytosol to form HLA-ligand precursors. Chemical inhibition of Peptide:N-Glycanase (PNGase), the endoglycosidase responsible for the removal of glycans from misfolded and retrotranslocated glycoproteins, greatly reduced presentation of this subset of deamidated HLA-bound peptides. Importantly, there was no impact of PNGase inhibition on peptides not containing a consensus NX(S/T) motif. This indicates that a large proportion of HLA-I bound asparagine deamidated peptides are generated from formerly glycosylated proteins that have undergone deglycosylation via the ER-associated protein degradation (ERAD) pathway. The information herein will help train deamidation prediction models for HLA-peptide repertoires and aid in the design of novel T cell therapeutic targets derived from glycoprotein antigens

PERISCOPE-Opt:Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in<i> Escherichia coli</i>

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu)