Direct Detection of Multiple Acidic Proton Sites in Zeolite HZSM‑5

Direct observation of multiple reactive sites in the zeolite HZSM-5, a member of the MFI family of zeolite structures, contradicts the traditional view of only one type of active protonic species in industrially important zeolites. In addition to the well-known Brönsted acid site proton, two other protonic species undergo room-temperature hydrogen–deuterium exchange with an alkane hydrocarbon reagent, including one zeolite moiety characterized by a broad ¹H chemical shift at ca. 12–15 ppm that is reported here for the first time. Although the ca. 13 ppm chemical shift value is consistent with computational predictions from the literature for a surface-stabilized hydroxonium ion in a zeolite, data suggest that the signal does not arise from hydroxonium species but rather from hydroxyls on extra-lattice aluminol species proximate to Brönsted lattice sites, i.e., a small population of highly deshielded acid sites. Double-resonance experiments show that this species is proximate to Al atoms, similar to the Brönsted acid site proton. These sites can be removed by appropriate postsynthesis chemical treatment, yielding a catalyst with reduced activity for isotopic H/D exchange reactions. Additionally, other extra-lattice aluminum hydroxyl groups previously discussed in the literature but whose protons were considered unreactive are also shown for the first time to react with hydrocarbon probe molecules. Two-dimensional exchange NMR reveals direct proton exchange between the Brönsted site and these two types of extra-lattice Al–OH species, and it also reveals unexpected proton exchange between extra-lattice Al–OH species and an alkane reagent

Engineering elastic properties into an anti-TNFα monoclonal antibody

<p>Injecting anti-tumor necrosis factor (TNF)α antibodies into patient joints at the site of inflammation, inter-articular (IA) delivery, has demonstrated modest success for treatment of Spondyloarthritis (SpA), Rheumatoid Arthritis (RA), and osteoarthritis. However, IA delivery is not the treatment route of choice due to rapid clearance from the site of administration. Elastin-like polypeptides (ELPs) are reported to undergo phase transition; forming reversible aggregates above their transition temperature, which when injected into IA space have a 25-fold longer half-life compared to the soluble form. Here, we fused an ELP repeat to the C-terminus of each heavy chain of an anti-TNFα monoclonal antibody (mAb) and provide detailed characterization of the fusion IgG molecule. Expression and purification yielded homogenous protein confirmed by gels, hydrophobic-interaction chromatography, Capilary Electrophoresis (CE), Mass Spectrometry (MS), and analytical ultracentrifugation. The ELPs altered hydrophobicity and pI of the parent mAb and new elastic properties were imparted to the molecule; forming large stable complexes with a hydrodynamic radius of 40 nm above 39°C that dissociated into soluble, active monomer below 37°C. The fusion mAb retained its affinity and ability to neutralize TNFα as determined by surface plasmon resonance and cell-based assay, respectively, with equal potency to unmodified anti-TNFα mAb. Differential-scanning calorimetry studies show stabilization of adjacent C_H2 and C_H3 domains in the fusion molecule and the aggregated molecule was found to be fully functional after 7 days at 37°C. For the first time, we reveal architecture of an ELP-fusion mAb and binding to antigen using single-particle-transmission-electron microscopy. Unstructured ELP was visualized at the C-terminus and binding to antigen was shown at the <i>N</i>-terminus. Collectively, these studies indicate that it is possible to impart elastic properties to a monoclonal antibody while retaining purity, stability, and ability to effectively bind and neutralize antigen.</p

Overview over datasets with training and test data used in the competition.

<p>Overview over datasets with training and test data used in the competition.</p

Top algorithms make highly correlated predictions.

<p><b>A.-B.</b> Example cells from the test set for dataset 1 (OGB-1) and dataset 3 (GCaMP6s) show highly similar predictions between most algorithms. <b>C.</b> Average correlation coefficients between predictions of different algorithms across all cells in the test set at 25 Hz (40 ms bins).</p

Different spike inference metrics reach similar conclusions.

<p><b>A.</b> Area under the curve (AUC) of the inferred spike rate used as a binary predictor for the presence of spikes (evaluated at 25 Hz, 50 ms bins) on the test set. Colors indicate different datasets. Black dots are mean correlation coefficients across all <i>N</i> = 32 cells in the test set. Colored dots are jittered for better visibility. STM: Spike-triggered mixture model [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006157#pcbi.1006157.ref015" target="_blank">15</a>]; f-oopsi: fast non-negative deconvolution [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006157#pcbi.1006157.ref009" target="_blank">9</a>] <b>B.</b> Information gain of the inferred spike rate about the true spike rate on the test set (evaluated at 25 Hz, 40 ms bins).</p

Summary of algorithm performance.

<p>Δ correlation is computed as the mean difference in correlation coefficient compared to the STM algorithm. Δ var. exp. in % is computed as the mean relative improvement variance explained (<i>r</i>²). Note that since variance explained is a nonlinear function of correlation, algorithms can be ranked differently according to the two measures. All means are taken over <i>N</i> = 32 recordings in the test set, except for training correlation, which is computed over <i>N</i> = 60 recordings in the training set.</p

Overview over different strategies used by DNN-based algorithms.

<p>Architecture briefly summarizes main components. conv: convolutional layers, typically with non-linearity; lstm: recurrent long-short-term memory unit; residual: residual blocks; max: max-pooling layers; inception: inception cells. For details, refer to the descriptions of the algorithms in the supplementary material.</p