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Stimulus-induced in situ forming hydrogels possess the characteristics of easy management and minimal invasiveness via simple injection at target sites with a liquid and easy forming bulk gels. In the present study, a photoreactive monomer, N’-(2-nitrobenzyl)-N-acryloyl glycinamide (NBNAGA) was introduced to modify polyacrylamide (PAM) hydrogel preparation with stimuli responsiveness. Firstly, poly(acrylamide-co-N’-(2-nitrobenzyl)-N-acryloyl glycinamide), P(AM-co-NBNAGA), copolymer solution was prepared via reversible addition fragmentation chain transfer (RAFT) polymerization using the monomers mixture of AM, NBNAGA, and N,N’- methylene bis-acrylamide (BIS). The obtained polymer solution with viscous, transparent, and flowable appearance contained weak single hydrogen bonding and slight chemical crosslinking in a microscopic perspective. Secondly, without further purification, after irradiation using UV light at 365 nm, poly(acrylamide-co-N-acryloyl glycinamide) (P(AM-co-NAGA)) hydrogel conveniently in situ formed due to the cleavage of o-nitrobenzyl groups and the corresponding emergency of dual hydrogen bonding among “uncaged” dual amide moieties. P(AM-co-NAGA) hydrogel depicted both favorable temperature sensitivity and self-healing properties, then the heating induced in vitro release profiles of doxorubicin (DOX) was analyzed.

Novel organic/inorganic hybrid star polymer was prepared dually crosslinked within inner-core via divinylbenzene (DVB) and outer-surface via octafunctional polyhedral oligomeric silsesquioxane (POSS). Core cross-linked star polymers bearing dialkynyl-terminated polystyrene arms, (dialkynyl-PS)n-CCL, were synthesized at first by the atom transfer radical polymerization (ATRP) of DVB using α,α-dialkynyl-terminated PS macroinitiator, followed by the subsequent fractionation. Under high dilution conditions, (dialkynyl-PS)n-CCL was subjected to surface cross-linking with octa(3-azidopropyl) polyhedral oligomeric silsesquioxane, POSS-(N3)8, via click reaction, affording POSS-functionalized hybrid polymer doubly cross-linked within core and surface regions, SCL-(PS)n-CCL. FT-IR, 1H NMR, GPC, and elemental analysis results revealed that on average, the obtained hybrid polymer possesses a cross-linked PDVB inner core, ∼14 linear PS arms (the Mw per arm of 5.1 kDa), and ∼4-5 POSS moieties at outer surface. Differential scanning calorimetry (DSC) thermograms and thermogravimetric analysis (TGA) revealed that after surface cross-linking the thermal stability of SCL-(PS)n-CCL is considerably improved. This work provides a proof-of-concept example for the preparation of dually cross-linked hybrid star polymer, which represents a novel category of organic/inorganic composite materials with unique chain architectures.

The N-end rule pathway was one of the first ubiquitin (Ub)-dependent degradation pathways to be identified. Ubr1, a single-chain E3 ligase, targets proteins bearing a destabilizing residue at the N-terminus (N-degron) for rapid K48-linked ubiquitination and proteasome-dependent degradation. How Ubr1 catalyses the initiation of ubiquitination on the substrate and elongation of the Ub chain in a linkage-specific manner through a single E2 ubiquitin-conjugating enzyme (Ubc2) remains unknown. Here, we report the cryo-electron microscopy structures of two complexes representing the initiation and elongation intermediates of Ubr1 captured using chemical approaches. In these two structures, Ubr1 adopts different conformations to facilitate the transfer of Ub from Ubc2 to either an N-degron peptide or a monoubiquitinated degron. These structures not only reveal the architecture of the Ubr1 complex but also provide mechanistic insights into the initiation and elongation steps of ubiquitination catalysed by Ubr1.

MotivationIt has been a challenge for biologists to determine 3D shapes of proteins from a linear chain of amino acids and understand how proteins carry out life’s tasks. Experimental techniques, such as X-ray crystallography or Nuclear Magnetic Resonance, are time-consuming. This highlights the importance of computational methods for protein structure predictions. In the field of protein structure prediction, ranking the predicted protein decoys and selecting the one closest to the native structure is known as protein model quality assessment (QA), or accuracy estimation problem. Traditional QA methods don’t consider different types of features from the protein decoy, lack various features for training machine learning models, and don’t consider the relationship between features. In this research, we used multi-scale features from energy score to topology of the protein structure, and proposed a hierarchical architecture for training machine learning models to tackle the QA problem.ResultsWe introduce a new single-model QA method that incorporates multi-scale features from protein structures, utilizes the hierarchical architecture of training machine learning models, and predicts the quality of any protein decoy. Based on our experiment, the new hierarchical architecture is more accurate compared to traditional machine learning-based methods. It also considers the relationship between features and generates additional features so machine learning models can be trained more accurately. We trained our new tool, SynthQA, on the CASP dataset (CASP10 to CASP12), and validated our method on 33 targets from the latest CASP 14 dataset. The result shows that our method is comparable to other state-of-the-art single-model QA methods, and consistently outperforms each of the 14 used features.Availabilityhttps://github.com/Cao-Labs/SynthQA.gitContactcaora@plu.edu

MotivationAccurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm.ResultsWe trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours.AvailabilityThe repository of this project will be published.Contactdongsi@uw.eduSupplementary informationSupplementary data will be available at Bioinformatics online.

The construction of intelligent supramolecular nanocarriers has received much attention for their potential application in chemotherapy. Herein, we report the successful design and synthesis of a photoreactive monomer, N′-(2-nitrobenzyl)-N-acryloyl glycinamide (NBNAGA). Using a poly(ethylene glycol) (PEG)-based macro-RAFT agent, the amphiphilic diblock copolymer (BCP), PEG-b-PNBNAGA, was prepared through a reversible addition–fragmentation chain-transfer (RAFT) polymerization. Then, photoresponsive polymeric micelles (PMs) were fabricated with the hydrophilic PEG shielding coronas and hydrophobic PNBNAGA inner cores via the self-assembly of PEG-b-PNBNAGA BCPs. Upon 365 nm UV light irradiation, the o-nitrobenzyl groups in the micellar cores were removed with freshly formed amide moieties. 1. Combined with the original amide moieties dual hydrogen bond interactions in the side chains came into being, subsequently changing the PMs’ cores from hydrophobic to hydrophilic. Thus, the photo-induced dual hydrogen-bonding complex rather than a hydrophobic interaction assembly with inviable nanostructures was achieved, which gave rise to the first stage of doxorubicin (DOX) release. During the second period, the noncovalent cross-linked PMs underwent further structural disintegration upon heating with dissociation of the dual hydrogen bonds, resulting in the sequential DOX release. In other words, periodic drug release was successfully accomplished via a photoirradiation-induced mechanism modification of micellar cores formation and then by subsequent heating-induced hydrogen-bonding complex disruption, thus indicating its promise for use in therapeutics synergistic delivery in severe disease therapy.

Quality Assessment (QA) plays an important role in protein structure prediction. Traditional multimodel QA method usually suffer from searching databases or comparing with other models for making predictions, which usually fail when the poor quality models dominate the model pool. We propose a novel protein single-model QA method which is built on a new representation that converts raw atom information into a series of carbon-alpha (Cα) atoms with side-chain information, defined by their dihedral angles and bond lengths to the prior residue. An LSTM network is used to predict the quality by treating each amino acid as a time-step and consider the final value returned by the LSTM cells. To the best of our knowledge, this is the first time anyone has attempted to use an LSTM model on the QA problem; furthermore, we use a new representation which has not been studied for QA. In addition to angles, we make use of sequence properties like secondary structure parsed from protein structure at each time-step without using any database, which is different than all existed QA methods. Our model achieves an overall correlation of 0.651 on the CASP12 testing dataset. Our experiment points out new directions for QA problem and our method could be widely used for protein structure prediction problem. The software is freely available at GitHub: https://github.com/caorenzhi/AngularQA

With soy protein isolate (SPI) as the main raw material, and ethylene glycol (EG) and polyethylene glycol (PEG) as two additives, a number of SPI-based films were prepared through the solution casting method. Results show that the film flexibility is obviously improved after adding EG and the film tensile strength and elongation at break could reach more than 10.5 MPa and 140%, respectively. The antimicrobial results exhibit EG has higher antibacterial effect against both bacteria ofE. coliandS. aureusthan PEG-400.

As a common kind of surface defects on multi-crystalline silicon solar cells (about 65% of total defects). Based on HALCON image processing library, an automatic silicon wafer surface spot defects detection and classification system has been developed: captured color images via CCD image sensor, located wafers and spots, extracted the spot features, computed and separated the qualified regions from the unqualified ones by SVM classifier. The experimental result showed a high accuracy of 95.7% and fast image analyzing and classifying process (less than 600ms).

In order to discuss the relationship among the initial water content, matric suction and swelling behaviors, both swelling behaviors and matric suction were measured in laboratory test. Results of swelling tests indicated that with the increase of initial water content, the maximum swelling pressure was reduced in a lower water content range and almost constant in the higher water content range. The maximum swelling strain was linearly reduced with the increase of initial water content. The influence of initial water content on the swelling behaviors was more obvious for specimens with higher initial dry density or lower sand content ratio. Relationship between matric suction and initial water content ratio was obtained, and results showed that matric suction decreased with the increase of water content for specimens with the same dry density, which was more obvious when the water content was less than 25%. The extent of the impact by water content, however, was found to be dependent upon the characterist...

The accuracy of the secondary structure element (SSE) identification from volumetric protein density maps is critical for de-novo backbone structure derivation in electron cryo-microscopy (cryoEM). It is still challenging to detect the SSE automatically and accurately from the density maps at medium resolutions (∼5–10 A). We present a machine learning approach, SSELearner, to automatically identify helices and β-sheets by using the knowledge from existing volumetric maps in the Electron Microscopy Data Bank. We tested our approach using 10 simulated density maps. The averaged specificity and sensitivity for the helix detection are 94.9% and 95.8%, respectively, and those for the β-sheet detection are 86.7% and 96.4%, respectively. We have developed a secondary structure annotator, SSID, to predict the helices and β-strands from the backbone Cα trace. With the help of SSID, we tested our SSELearner using 13 experimentally derived cryo-EM density maps. The machine learning approach shows the specificity and sensitivity of 91.8% and 74.5%, respectively, for the helix detection and 85.2% and 86.5% respectively for the β-sheet detection in cryoEM maps of Electron Microscopy Data Bank. The reduced detection accuracy reveals the challenges in SSE detection when the cryoEM maps are used instead of the simulated maps. Our results suggest that it is effective to use one cryoEM map for learning to detect the SSE in another cryoEM map of similar quality. © 2012 Wiley Periodicals, Inc. Biopolymers 97: 698–708, 2012.

A series of 1-[(1,3,4-thiadiazol-2-yl)methyl]-1H-1,2,4-triazole derivatives were prepared and evaluated for their antifungal activities. The chemical structures of these compounds were determined by means of elemental analyses, 1H NMR, and X-ray crystallography. Quantitative structure–activity relationship (QSAR) studies were performed on these compounds using physicochemical parameters as independent parameters and antifungal activity as a dependent parameter, where antifungal activity correlated best (r > 0.9) with hydrophobic parameters (π) and indicator (H). Moreover, the results are interpreted on the basis of a multiple regression model. The model has been internally and externally validated. Furthermore, the domain of applicability which indicates the area of reliable predictions is defined.

Four compounds have been synthesized and characterized by1H NMR spectroscopy and elemental analyses, among which 1-(4,5-dihydro-3-phenyl-pyrazol-1-yl)-2-(1H-1,2,4-triazol-1-yl)ethanone 3a was further confirmed by X-ray crystallographic analysis. The biological activities of these compounds were tested, with the result that they displayed moderate fungicidal activities. In addition, a MO calculations using density functional theory (DFT) at B3LYP/6-31G* level have also been carried out, and the structure–activity relationships for these compounds are discussed.

The two compounds 1-[1-(2',4'-dichlorobenzoyl)-1-(3-phenylthiazolidin-2-ylidene)methyl]-1,2,4-triazole (3a) and 1-[1-(4'-fluorobenzoyl)-1-(3-phenylthiazolidin-2-ylidene)methyl]-1,2,4-triazole (3b) were prepared by reaction of phenyl isothiocyanate, 1,2-dibromoethane with alpha-(1,2,4-triazol-1-yl)-substituted-acetylbenzene. Their structures were identified by means of elemental analysis, IR, and H-1 NMR spectra. The single crystal of compound 3b was also obtained. It crystallizes in triclinic system with space group P (1) over bar and a=0.9390(2) nm, b=0.9661(2) nm, c=1.0929(2) nm, alpha=111.53(3)degrees, beta=100.46(3)degrees, gamma=102.08(3)degrees, Z=2, V=0.8647(3) nm(3), D-c=1.407 g/cm(3), mu=0.213 mm(-1), F(000)=380, final R-1=0.073. There is obvious potentially weak C-H center dot center dot center dot Y (Y=N, F, 0) intermolecular interaction between the molecules in the crystal lattice, which stabilizes the crystal structure. The result of the biological test showed that the two compounds have certain fungicidal activities.

Exogenous and endogenous cannabinoids have been shown to have neuroprotective effects in vitro and in vivo. Although many of the pharmacological effects of cannabinoids have been identified, the mechanism of neuroprotection still represents a controversy. Here we demonstrate for the first time protective effects of the synthetic cannabinoid dexanabinol by inhibiting apoptosis in a neuron-like cell line using nuclear staining and FACS analysis and in primary neurons. We provide further evidence of inhibition of nuclear factor-kappakappa B (NF-kappaB) by dexanabinol: Dexanabinol inhibits (1) phosphorylation and degradation of the inhibitor of NF-kappaB IkappaBalpha and translocation of NF-kappaB to the nucleus; dexanabinol reduces (2) the transcriptional activity of NF-kappaB and (3) mRNA accumulation of the NF-kappaB target genes tumor necrosis factor-alpha and interleukin-6 (TNF-alpha and IL-6). Dexanabinol does not bind to cannabinoid (CB) receptors 1 and 2. To investigate the mechanism of action, we employed the non-antioxidant CB1 receptor agonist WIN 55,212-2 and the antioxidant cannabinol, which binds to CB1 receptors only weakly. Both cannabinoids mimicked the effect of dexanabinol on NF-kappaB and apoptosis. This suggests that neither the antioxidant properties of cannabinoids nor binding to CB1 or CB2 receptors are responsible for the inhibition of NF-kappaB activity and apoptosis. Our results clearly demonstrate that dexanabinol inhibits NF-kappaB. NF-kappaB has been shown to be involved in brain damage and to promote neuronal cell death in vitro and in in vivo models of ischemic and neurodegenerative neurological diseases.

The two compounds 1-[1-(2',4'-dichlorobenzoyl)-1-(3-phenylthiazolidin-2-ylidene)methyl]-1,2,4-triazole (3a) and 1-[1-(4'-fluorobenzoyl)-1-(3-phenylthiazolidin-2-ylidene)methyl]-1,2,4-triazole (3b) were prepared by reaction of phenyl isothiocyanate, 1,2-dibromoethane with alpha-(1,2,4-triazol-1-yl)-substituted-acetylbenzene. Their structures were identified by means of elemental analysis, IR, and H-1 NMR spectra. The single crystal of compound 3b was also obtained. It crystallizes in triclinic system with space group P (1) over bar and a=0.9390(2) nm, b=0.9661(2) nm, c=1.0929(2) nm, alpha=111.53(3)degrees, beta=100.46(3)degrees, gamma=102.08(3)degrees, Z=2, V=0.8647(3) nm(3), D-c=1.407 g/cm(3), mu=0.213 mm(-1), F(000)=380, final R-1=0.073. There is obvious potentially weak C-H center dot center dot center dot Y (Y=N, F, 0) intermolecular interaction between the molecules in the crystal lattice, which stabilizes the crystal structure. The result of the biological test showed that the two compounds have certain fungicidal activities.

In the title compound, C14H10ClNO3S, the mean planes of benzisothia­zole moiety and the 4-chlorobenzene ring make a dihedral angle of 66.2 (5)°. Weak inter­molecular C—H⋯O hydrogen bonds link mol­ecules into linear chains extending along the b axis.

In the title compound, C13H15N7OS, the dihedral angles made by the plane of the thione-substituted triazole ring with the planes of the other triazole ring and the benzene ring are 88.69 (3) and 2.94 (2)°, respectively. Inter- and intra­molecular hydrogen-bond and π–π stacking inter­actions stabilize the structure.

In the title compound, C12H11N7OS, the dihedral angles made by the thione-substituted triazole ring with the other triazole ring and the benzene ring are 71.56 (2) and 47.89 (3)°, respectively. Inter- and intra­molcular hydrogen-bond inter­actions stabilize the structure.

In the title compound, C12H12N6OS, the dihedral angles made by the plane of the thione-substituted triazolidine ring with the planes of the triazole ring and the benzene ring are 87.77 (2) and 52.07 (3)°, respectively. In the crystal structure, weak inter­molecular N—H⋯N, C—H⋯N and C—H⋯O hydrogen bonds stabilize the packing.

In the title compound, C12H10ClN7S, the dihedral angles made by the plane of the thione-substituted triazole ring with the planes of the other triazole ring and the benzene ring are 73.57 (3) and 46.65 (2)degrees, respectively. Inter-and intramolcular hydrogen bonds and pi-pi stacking interactions stabilize the structure.

In the title compound, C11H11NO5S, the saccharin and ethyl acetate moieties are nearly orthogonal. There are some weak intermolecular hydrogen-bond interactions which stabilize the crystal structure.

In the title mol­ecule, C15H9F3O2, the bond lengths and angles are within normal ranges. The crystal packing is stabilized by weak inter­molecular C—H⋯O hydrogen bonds.

The title compound, C18H16N4OS, crystallizes in space group P21/n with two independent mol­ecules in the asymmetric unit. The bond lengths and angles in both mol­ecules are within normal ranges. There are some weak intermolecular hydrogen-bond interactions in the crystal structure, providing stabilization.

The title compound, C17H15NOS, crystallizes in space group P\overline 1 with two independent mol­ecules in the asymmetric unit. The dihedral angles between the two benzene rings and the heterocyclic five-membered mean ring in the two independent mol­ecules are different: 35.62 (7) and 58.80 (7)° in one mol­ecule and 14.80 (12) and 60.09 (6)° in the other.

The title compound, C13H15NO2S2, the morpholine ring adopts the usual chair conformation and the four C atoms deviate only slightly from coplanarity. The crystal structure is stabilized by some weak intramolecular C—H⋯S hydrogen-bond interactions.

In the title compound, C15H15FN4O2S2, the morpholine ring adopts a chair conformation. The dihedral angle between the benzene and triazole rings is 77.1 (7)°. There are some weak intermolecular and intramolecular interactions in the crystal structure, providing stabilization.

Objectives To investigate the association of ACS with the clinical risk factors. Methods 60 patients were collected. They were divided into acute coronary syndrome group, stable angina pectoris group and healthy group. The clinical data were recorded. Based on the SIMCA-P software, by the method of partial least squares regression (partial least-squares regression, PLS) the association of ACS with the clinical risk factors were analysis. Results The male, age, blood glucose, cholesterol, lipoprotein (a), low density lipoprotein cholesterol, creatinine, blood urea nitrogen, white blood cell, red blood cell, MMP2, BMI, mean arterial pressure and acute coronary syndrome have positive correlation. Women and acute coronary syndrome have negative correlation. VIP analysis showed low density lipoprotein cholesterol is most important risk factor, followed by acute coronary syndrome, triglyceride and low density lipoprotein cholesterol. The PLS regression equation was established. The Model fitting is satisfactory. Conclusions The low density lipoprotein cholesterol and cholesterol are most important risk factor in patients with ACS.

A resistometric model based on microscopic analysis of electromigration failure mechanism is built. An extraction method for failure parameters of electromigration in copper interconnects is proposed from resistometric characteristics including the slope and step height. The results show that the failure time can be considered as the time to deplete grains at the cathode line end under a given stressing current. Two dominant failure modes with resuling slit and trench voids are observed in electromigration induced failures. The resistance curve for the trench-voiding failure mode consists of two characteristic regions,i.e., a step jump and an oblique line. The grain size and the extracted critical void length are lognormally distributed with close parameters. The variation in the slop of the oblique line in resistance curve with temperature obeys an exponential law. Activation energy of approximately 0.9 eV obtained from the resisometric model is consistent with that from Black equation.