Low-sugar universal mRNA vaccine against coronavirus variants with deletion of glycosites in the S2 or stem of SARS-CoV-2 spike messenger RNA (mRNA).

Since the outbreak of Severe Acute Respiratory Syndrome Virus-2 (SARS-CoV-2) in 2019, more than 15 million spike protein sequences have been identified, raising a new challenge for the development of a broadly protective vaccine against the various emerging variants. We found that the virus, like most other human viruses, depends on host-made glycans to shield the conserved epitopes on spike protein from immune response and demonstrated that deletion of the glycan shields exposed highly conserved epitopes and elicited broadly protective immune responses. In this study, we identified 17 conserved epitopes from 14 million spike protein sequences and 11 of the conserved epitopes are in the S2 domain, including the six most conserved epitopes in the stem region. We also demonstrated that deletion of the glycosites in the spike messenger RNA (mRNA) S2 domain or the stem region exposed the highly conserved epitopes and elicited broadly protective immune responses, particularly CD-8+ T cell response against various SARS-CoV-2 variants, and other human coronaviruses including MERS, SARS viruses, and those causing common cold.

Glycosite-deleted mRNA of SARS-CoV-2 spike protein as a broad-spectrum vaccine.

Development of the messenger RNA (mRNA) vaccine has emerged as an effective and speedy strategy to control the spread of new pathogens. After vaccination, the mRNA is translated into the real protein vaccine, and there is no need to manufacture the protein in vitro. However, the fate of mRNA and its posttranslational modification inside the cell may affect immune response. Here, we showed that the mRNA vaccine of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with deletion of glycosites in the receptor-binding domain (RBD) or especially the subunit 2 (S2) domain to expose more conserved epitopes elicited stronger antibody and CD8+ T cell responses with broader protection against the alpha, beta, gamma, delta, and omicron variants, compared to the unmodified mRNA. Immunization of such mRNA resulted in accumulation of misfolded spike protein in the endoplasmic reticulum, causing the up-regulation of BiP/GRP78, XBP1, and p-eIF2α to induce cell apoptosis and strong CD8+ T cell response. In addition, dendritic cells (DCs) incubated with S2-glysosite deleted mRNA vaccine increased class I major histocompatibility complex (MHC I) expression. This study provides a direction for the development of broad-spectrum mRNA vaccines which may not be achieved with the use of expressed proteins as antigens.

Identification of existing pharmaceuticals and herbal medicines as inhibitors of SARS-CoV-2 infection.

The outbreak of COVID-19 caused by SARS-CoV-2 has resulted in more than 50 million confirmed cases and over 1 million deaths worldwide as of November 2020. Currently, there are no effective antivirals approved by the Food and Drug Administration to contain this pandemic except the antiviral agent remdesivir. In addition, the trimeric spike protein on the viral surface is highly glycosylated and almost 200,000 variants with mutations at more than 1,000 positions in its 1,273 amino acid sequence were reported, posing a major challenge in the development of antibodies and vaccines. It is therefore urgently needed to have alternative and timely treatments for the disease. In this study, we used a cell-based infection assay to screen more than 3,000 agents used in humans and animals, including 2,855 small molecules and 190 traditional herbal medicines, and identified 15 active small molecules in concentrations ranging from 0.1 nM to 50 µM. Two enzymatic assays, along with molecular modeling, were then developed to confirm those targeting the virus 3CL protease and the RNA-dependent RNA polymerase. Several water extracts of herbal medicines were active in the cell-based assay and could be further developed as plant-derived anti-SARS-CoV-2 agents. Some of the active compounds identified in the screen were further tested in vivo, and it was found that mefloquine, nelfinavir, and extracts of Ganoderma lucidum (RF3), Perilla frutescens, and Mentha haplocalyx were effective in a challenge study using hamsters as disease model.

Dual-probe ligation without PCR for fluorescent sandwich assay of EGFR nucleotide variants in magnetic gene capture platform.

A simple, rapid, and highly efficient fluorescent detection technique without PCR through dual-probe ligation with the genetic capture of magnetic beads and reported probe was developed for determination of epidermal growth factor receptor (EGFR) gene exon 19 deletions. The EGFR exon 19 deletion mutation makes up 48% of all mutations associated with anti-tyrosine kinase inhibition sensitivity, and thus, the EGFR nucleotide variant is very important in clinical diagnosis. In this approach, the dual-probe ligation was designed to target exon 19 deletion. The magnetic genetic captured system was then applied to capture the successful dual-probe ligation. Thereafter, a reporter probe which is coupled with 6-fluorescein amidite (6-FAM) was introduced to hybridize with dual-probe ligation product on the surface of streptavidin magnetic beads, and finally, the supernatant was taken for fluorescence measurements for distinguishing mutant types from wild types. After optimization (the RSD of the fluorescent intensity was less than 4.5% (n = 3) under the optimal condition), 20 blind DNA samples from the population were analyzed by this technique and further confirmed by direct sequencing. The results of our assay matched to those from direct sequencing data, evidencing that the developed method is accurate and successful. These 20 blind DNA samples were diagnosed as wild and then spiked with different percentages of the mutant gene to quantify the ratio of the wild and mutant genes. This strategy was also successfully applied to quantify the ratio of the wild and mutant genes with good linearity at the λex/λem of 480 nm/520 nm (r = 0.996), and the limit of detection reached 1.0% mutant type. This simple fluorescent detection of nucleotide variants shows its potential to be considered a tool in biological and clinical diagnosis. Importantly, this strategy offers a universal detection capability for any kind of mutation (point, deletion, insertion, or substitution) in a gene of interest.

Programmable One-Pot Synthesis of Oligosaccharides.

Carbohydrates make up one of the four major classes of biomolecules, often conjugated with proteins as glycoproteins or with lipids as glycolipids, and participate in many important biochemical functions in living species. However, glycoproteins or glycolipids often exist as mixtures, and as a consequence, it is difficult to isolate individual glycoproteins or glycolipids as pure forms to understand the role carbohydrates play in the glycoconjugate. Currently, the only feasible way to obtain pure glycoconjugates is through synthesis, and of the many methods developed for the synthesis of oligosaccharides, those with automatic and programmable potential are considered to be more effective for addressing the issues of carbohydrate diversity and related functions. In this Perspective, we describe how data science, including algorithm and machine learning, can be used to assist the chemical synthesis of oligosaccharide in a programmable and one-pot manner and how the programmable method can be used to accelerate the construction of diverse oligosaccharides to facilitate our understanding of glycosylation in biology.

MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification.

MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS(2) spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html.

MAGIC: an automated N-linked glycoprotein identification tool using a Y1-ion pattern matching algorithm and in silico MS² approach.

Glycosylation is a highly complex modification influencing the functions and activities of proteins. Interpretation of intact glycopeptide spectra is crucial but challenging. In this paper, we present a mass spectrometry-based automated glycopeptide identification platform (MAGIC) to identify peptide sequences and glycan compositions directly from intact N-linked glycopeptide collision-induced-dissociation spectra. The identification of the Y1 (peptideY0 + GlcNAc) ion is critical for the correct analysis of unknown glycoproteins, especially without prior knowledge of the proteins and glycans present in the sample. To ensure accurate Y1-ion assignment, we propose a novel algorithm called Trident that detects a triplet pattern corresponding to [Y0, Y1, Y2] or [Y0-NH3, Y0, Y1] from the fragmentation of the common trimannosyl core of N-linked glycopeptides. To facilitate the subsequent peptide sequence identification by common database search engines, MAGIC generates in silico spectra by overwriting the original precursor with the naked peptide m/z and removing all of the glycan-related ions. Finally, MAGIC computes the glycan compositions and ranks them. For the model glycoprotein horseradish peroxidase (HRP) and a 5-glycoprotein mixture, a 2- to 31-fold increase in the relative intensities of the peptide fragments was achieved, which led to the identification of 7 tryptic glycopeptides from HRP and 16 glycopeptides from the mixture via Mascot. In the HeLa cell proteome data set, MAGIC processed over a thousand MS(2) spectra in 3 min on a PC and reported 36 glycopeptides from 26 glycoproteins. Finally, a remarkable false discovery rate of 0 was achieved on the N-glycosylation-free Escherichia coli data set. MAGIC is available at http://ms.iis.sinica.edu.tw/COmics/Software_MAGIC.html .

Prediction of B-cell epitopes using evolutionary information and propensity scales.

BACKGROUND: Development of computational tools that can accurately predict presence and location of B-cell epitopes on pathogenic proteins has a valuable application to the field of vaccinology. Because of the highly variable yet enigmatic nature of B-cell epitopes, their prediction presents a great challenge to computational immunologists. METHODS: We propose a method, BEEPro (B-cell epitope prediction by evolutionary information and propensity scales), which adapts a linear averaging scheme on 16 properties using a support vector machine model to predict both linear and conformational B-cell epitopes. These 16 properties include position specific scoring matrix (PSSM), an amino acid ratio scale, and a set of 14 physicochemical scales obtained via a feature selection process. Finally, a three-way data split procedure is used during the validation process to prevent over-estimation of prediction performance and avoid bias in our experiment results. RESULTS: In our experiment, first we use a non-redundant linear B-cell epitope dataset curated by Sollner et al. for feature selection and parameter optimization. Evaluated by a three-way data split procedure, BEEPro achieves significant improvement with the area under the receiver operating curve (AUC) = 0.9987, accuracy = 99.29%, mathew's correlation coefficient (MCC) = 0.9281, sensitivity = 0.9604, specificity = 0.9946, positive predictive value (PPV) = 0.9042 for the Sollner dataset. In addition, the same parameters are used to evaluate performance on other independent linear B-cell epitope test datasets, BEEPro attains an AUC which ranges from 0.9874 to 0.9950 and an accuracy which ranges from 93.73% to 97.31%. Moreover, five-fold cross-validation on one benchmark conformational B-cell epitope dataset yields an accuracy of 92.14% and AUC of 0.9066. CONCLUSIONS: Compared with other current models, our method achieves a significant improvement with respect to AUC, accuracy, MCC, sensitivity, specificity, and PPV. Thus, we have shown that an appropriate combination of evolutionary information and propensity scales with a support vector machine model can significantly enhance the prediction performance of both linear and conformational B-cell epitopes.

Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices.

BACKGROUND: Since membrane protein structures are challenging to crystallize, computational approaches are essential for elucidating the sequence-to-structure relationships. Structural modeling of membrane proteins requires a multidimensional approach, and one critical geometric parameter is the rotational angle of transmembrane helices. Rotational angles of transmembrane helices are characterized by their folded structures and could be inferred by the hydrophobic moment; however, the folding mechanism of membrane proteins is not yet fully understood. The rotational angle of a transmembrane helix is related to the exposed surface of a transmembrane helix, since lipid exposure gives the degree of accessibility of each residue in lipid environment. To the best of our knowledge, there have been few advances in investigating whether an environment descriptor of lipid exposure could infer a geometric parameter of rotational angle. RESULTS: Here, we present an analysis of the relationship between rotational angles and lipid exposure and a support-vector-machine method, called TMexpo, for predicting both structural features from sequences. First, we observed from the development set of 89 protein chains that the lipid exposure, i.e., the relative accessible surface area (rASA) of residues in the lipid environment, generated from high-resolution protein structures could infer the rotational angles with a mean absolute angular error (MAAE) of 46.32˚. More importantly, the predicted rASA from TMexpo achieved an MAAE of 51.05˚, which is better than 71.47˚ obtained by the best of the compared hydrophobicity scales. Lastly, TMexpo outperformed the compared methods in rASA prediction on the independent test set of 21 protein chains and achieved an overall Matthew's correlation coefficient, accuracy, sensitivity, specificity, and precision of 0.51, 75.26%, 81.30%, 69.15%, and 72.73%, respectively. TMexpo is publicly available at http://bio-cluster.iis.sinica.edu.tw/TMexpo. CONCLUSIONS: TMexpo can better predict rASA and rotational angles than the compared methods. When rotational angles can be accurately predicted, free modeling of transmembrane protein structures in turn may benefit from a reduced complexity in ensembles with a significantly less number of packing arrangements. Furthermore, sequence-based prediction of both rotational angle and lipid exposure can provide essential information when high-resolution structures are unavailable and contribute to experimental design to elucidate transmembrane protein functions.

TMPad: an integrated structural database for helix-packing folds in transmembrane proteins.

α-helical transmembrane (TM) proteins play an important role in many critical and diverse biological processes, and specific associations between TM helices are important determinants for membrane protein folding, dynamics and function. In order to gain insights into the above phenomena, it is necessary to investigate different types of helix-packing modes and interactions. However, such information is difficult to obtain because of the experimental impediment and a lack of a well-annotated source of helix-packing folds in TM proteins. We have developed the TMPad (TransMembrane Protein Helix-Packing Database) which addresses the above issues by integrating experimentally observed helix-helix interactions and related structural information of membrane proteins. Specifically, the TMPad offers pre-calculated geometric descriptors at the helix-packing interface including residue backbone/side-chain contacts, interhelical distances and crossing angles, helical translational shifts and rotational angles. The TMPad also includes the corresponding sequence, topology, lipid accessibility, ligand-binding information and supports structural classification, schematic diagrams and visualization of the above structural features of TM helix-packing. Through detailed annotations and visualizations of helix-packing, this online resource can serve as an information gateway for deciphering the relationship between helix-helix interactions and higher levels of organization in TM protein structure and function. The website of the TMPad is freely accessible to the public at http://bio-cluster.iis.sinica.edu.tw/TMPad.

Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.

BACKGROUND: Identification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the post-genomic era, determining protein localization based on biological experiments can be expensive and time-consuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%~70% and 0.250~0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies. RESULTS: In this study, first we propose PSLNuc (Protein Subcellular Localization prediction for Nucleus) for predicting nuclear localization in proteins. First, for feature representation, a protein is represented by gapped-dipeptides and the feature values are weighted by homology information from a smoothed position-specific scoring matrix. After that, we incorporate probabilistic latent semantic indexing (PLSI) for feature reduction. Finally, the reduced features are used as input for a support vector machine (SVM) classifier. In addition to PSLNuc, we further identify gapped-dipeptide signatures for putative NLSs and NESs to develop a prediction method, PSLNTS (Protein Subcellular Localization prediction using Nuclear Translocation Signals). We apply PLSI to generate gapped-dipeptide signatures from both nuclear and non-nuclear proteins, and propose candidate sequence motifs for putative NLSs and NESs. Then, we incorporate only the proposed gapped-dipeptide signatures in an SVM classifier to mimic biological properties of NLSs and NESs for predicting nuclear localization in PSLNTS. CONCLUSIONS: Experiment results demonstrate that the proposed method shows a significant improvement for nuclear localization prediction. To compare our predictive performance with other approaches, we incorporate two non-redundant benchmark data sets, a training set and an independent test set. Evaluated by five-fold cross-validation on the training set, PSLNuc attains an overall accuracy of 79.7%, which is 4.8% improvement over the state-of-the-art system. In addition, our method also enhances the MCC from 0.497 to 0.595. Compared on the independent test set, PSLNuc outperforms other predictors by 3.9%~19.9% on accuracy and 0.077~0.207 on MCC. This suggests that, in addition to NLSs, which have been shown important for nuclear proteins, NESs can also be an effective indicator to detect non-nuclear proteins. Most notably, using only a few proposed gapped-dipeptide signatures as input features for the SVM classifier, PSLNTS further enhances the accuracy and MCC to 80.9% and 0.618, respectively. Our results demonstrate that gapped-dipeptide signatures can better discriminate nuclear and non-nuclear proteins. Moreover, the proposed gapped-dipeptide signatures can be biologically interpreted and used in further experiment analyses of nuclear translocation signals, including NLSs and NESs.

A chemometric experimental design with three-step stacking capillary electrophoresis for analysis of five tobacco-specific nitrosamines in cigarette products.

Tobacco-specific nitrosamines (TSNAs) as carcinogens endanger our health and life from cigarette products. However, the safe range of TSNAs levels in commercial cigarette products has not yet been established. For the purpose of safety and supervision, a three-step stacking approach including field amplified sample injection (FASI), sweeping, and analyte focusing by micelle collapse (AFMC), was developed for the simultaneous determination of five TSNAs levels in cigarette products. This approach also involved aspects of chemometric experimental design, including fractional factorial design and central composite design. After the multilevel optimization of the experimental design, the five TSNAs were well separated. The LOD (S/N = 3) values of the N´-nitrosonornicotine (NNN), N´-nitrosoanatabine (NAT), N´-nitrosoanabasine (NAB), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) in the FASI-sweeping-AFMC CE approach were 1.000 ng/mL, 0.500 ng/mL, 0.125 ng/mL, 1.000 ng/mL, and 0.500 ng/mL respectively. The results of relative standard deviation (RSD) and relative error (RE) were all less than 3.35%, demonstrating good precision and accuracy. Finally, this novel approach was further applied to monitor three commercial cigarette products, and a range of 250.1-336.6 ng/g for NNN, 481.6-526.7 ng/g for NAT, 82.2-247.6 ng/g for NAB, 167.7-473.7 ng/g for NNAL, and 39.4-246.7 ng/g for NNK could be observed among these. Based on these results, the novel CE stacking strategy was successfully applied for the analysis of five TSNAs levels in cigarette products and could serve as a tool for assays of quality control of nitrosamines.

A three-step stacking capillary electrophoresis of field-amplified sample injection, sweeping, and micellar collapse for determination of dabigatran and its active metabolite in human plasma.

A three-step stacking capillary electrophoresis (CE) composed of field-amplified sample injection, sweeping, and analyte focusing by micellar collapse (FASI-sweeping-AFMC) was developed to determine dabigatran (D) and its major active metabolite, dabigatran acyl-beta-d-glucuronide (DAG), in human plasma. After optimization and validation, this novel approach was further applied to monitor 5 real samples, and the 25.2-186.8 ng mL-1 D could be observed among those. Based on these results, the novel CE stacking strategy was successfully applied for the analysis of D and DAG in human plasma and could be served as a tool for clinical assays.

Vaccination with SARS-CoV-2 spike protein lacking glycan shields elicits enhanced protective responses in animal models.

A major challenge to end the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is to develop a broadly protective vaccine that elicits long-term immunity. As the key immunogen, the viral surface spike (S) protein is frequently mutated, and conserved epitopes are shielded by glycans. Here, we revealed that S protein glycosylation has site-differential effects on viral infectivity. We found that S protein generated by lung epithelial cells has glycoforms associated with increased infectivity. Compared to the fully glycosylated S protein, immunization of S protein with N-glycans trimmed to the mono-GlcNAc-decorated state (SMG) elicited stronger immune responses and better protection for human angiotensin-converting enzyme 2 (hACE2) transgenic mice against variants of concern (VOCs). In addition, a broadly neutralizing monoclonal antibody was identified from SMG-immunized mice that could neutralize wild-type SARS-CoV-2 and VOCs with subpicomolar potency. Together, these results demonstrate that removal of glycan shields to better expose the conserved sequences has the potential to be an effective and simple approach for developing a broadly protective SARS-CoV-2 vaccine.

Stable Luminescent Poly(Allylaminehydrochloride)-Templated Copper Nanoclusters for Selectively Turn-Off Sensing of Deferasirox in ß-Thalassemia Plasma.

Highly stable and facile one-pot copper nanoclusters (Cu NCs) coated with poly(allylamine hydrochloride) (PAH) have been synthesized for selectively sensing deferasirox (DFX) in ß-thalassemia plasma. DFX is an important drug used for treating iron overloading in ß-thalassemia, but needs to be monitored due to certain toxicity. In this study, the PAH-Cu NCs showed highly stable fluorescence with emission wavelengths at 450 nm. The DFX specifically interacted with the copper nanocluster to turn off the fluorescence of the PAH-Cu NCs, and could be selectively quantified through the fluorescence quenching effect. The linear range of DFX in plasma analyzed by PAH-Cu NCs was 1.0-100.0 µg/mL (r = 0.985). The relative standard deviation (RSD) and relative error (RE) were lower than 6.51% and 7.57%, respectively, showing excellent reproducibility of PAH-Cu NCs for sensing DFX in plasma. This method was also successfully applied for an analysis of three clinical plasma samples from ß-thalassemia patients taking DFX. The data presented high similarity with that obtained through a capillary electrophoresis method. According to the results, the PAH-Cu NCs could be used as a tool for clinically sensing DFX in human plasma for clinical surveys.

Dimethylimidazolium-Functionalized Polybenzimidazole and Its Organic-Inorganic Hybrid Membranes for Anion Exchange Membrane Fuel Cells.

A quaternized polybenzimidazole (PBI) membrane was synthesized by grafting a dimethylimidazolium end-capped side chain onto PBI. The organic-inorganic hybrid membrane of the quaternized PBI was prepared via a silane-induced crosslinking process with triethoxysilylpropyl dimethylimidazolium chloride. The chemical structure and membrane morphology were characterized using NMR, FTIR, TGA, SEM, EDX, AFM, SAXS, and XPS techniques. Compared with the pristine membrane of dimethylimidazolium-functionalized PBI, its hybrid membrane exhibited a lower swelling ratio, higher mechanical strength, and better oxidative stability. However, the morphology of hydrophilic/hydrophobic phase separation, which facilitates the ion transport along hydrophilic channels, only successfully developed in the pristine membrane. As a result, the hydroxide conductivity of the pristine membrane (5.02 × 10-2 S cm-1 at 80 °C) was measured higher than that of the hybrid membrane (2.22 × 10-2 S cm-1 at 80 °C). The hydroxide conductivity and tensile results suggested that both membranes had good alkaline stability in 2M KOH solution at 80 °C. Furthermore, the maximum power densities of the pristine and hybrid membranes of dimethylimidazolium-functionalized PBI reached 241 mW cm-2 and 152 mW cm-2 at 60 °C, respectively. The fuel cell performance result demonstrates that these two membranes are promising as AEMs for fuel cell applications.

Nanocomposite membranes of polybenzimidazole and amine-functionalized carbon nanofibers for high temperature proton exchange membrane fuel cells.

Carbon nanofibers functionalized with aminobenzoyl groups (CNF-aminobenzoyl) were prepared via direct Friedel-Crafts acylation in polyphosphoric acid. The functionalization of CNFs was characterized using XPS, FTIR, TGA, and Raman analyses. Hexafluoroisopropylidene-containing polybenzimidazole (6FPBI) composite membranes containing pristine CNFs or CNF-aminobenzoyl were prepared using solvent-assisted dispersion and solvent-casting methods. In this work, the influence of the incorporation of functionalized CNFs on several physicochemical properties of the 6FPBI nanocomposite membranes, including their thermal stability, mechanical strength, and acid doping level, was studied. The results showed that CNF-aminobenzoyl provided better mechanical reinforcement for the nanocomposite membrane, compared to pristine CNF. The SEM observation confirmed the good compatibility between the CNF-aminobenzoyl fillers and the 6FPBI matrix. For the 0.3 wt% CNF-aminobenzoyl/6FPBI composite membrane, the tensile stress was increased by 12% to be 78.9 MPa (as compared to the 6FPBI membrane), the acid doping level was improved to 12.0, and the proton conductivity at 160 °C was measured above 0.2 S cm-1. Furthermore, the fuel cell performance of the membrane electrolyte assembly (MEA) for each nanocomposite membrane was evaluated. The maximum power density at 160 °C was found up to 461 mW cm-2 for the MEA based on the 0.3 wt% CNF-aminobenzoyl/6FPBI composite membrane.

Multi-Q 2 software facilitates isobaric labeling quantitation analysis with improved accuracy and coverage.

Mass spectrometry-based proteomics using isobaric labeling for multiplex quantitation has become a popular approach for proteomic studies. We present Multi-Q 2, an isobaric-labeling quantitation tool which can yield the largest quantitation coverage and improved quantitation accuracy compared to three state-of-the-art methods. Multi-Q 2 supports identification results from several popular proteomic data analysis platforms for quantitation, offering up to 12% improvement in quantitation coverage for accepting identification results from multiple search engines when compared with MaxQuant and PatternLab. It is equipped with various quantitation algorithms, including a ratio compression correction algorithm, and results in up to 336 algorithmic combinations. Systematic evaluation shows different algorithmic combinations have different strengths and are suitable for different situations. We also demonstrate that the flexibility of Multi-Q 2 in customizing algorithmic combination can lead to improved quantitation accuracy over existing tools. Moreover, the use of complementary algorithmic combinations can be an effective strategy to enhance sensitivity when searching for biomarkers from differentially expressed proteins in proteomic experiments. Multi-Q 2 provides interactive graphical interfaces to process quantitation and to display ratios at protein, peptide, and spectrum levels. It also supports a heatmap module, enabling users to cluster proteins based on their abundance ratios and to visualize the clustering results. Multi-Q 2 executable files, sample data sets, and user manual are freely available at http://ms.iis.sinica.edu.tw/COmics/Software_Multi-Q2.html .

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis.

This article presents a general experimental protocol for programmable one-pot oligosaccharide synthesis and demonstrates how to use Auto-CHO software for generating potential synthetic solutions. The programmable one-pot oligosaccharide synthesis approach is designed to empower fast oligosaccharide synthesis of large amounts using thioglycoside building blocks (BBLs) with the appropriate sequential order of relative reactivity values (RRVs). Auto-CHO is a cross-platform software with a graphical user interface that provides possible synthetic solutions for programmable one-pot oligosaccharide synthesis by searching a BBL library (containing about 150 validated and >50,000 virtual BBLs) with accurately predicted RRVs by support vector regression. The algorithm for hierarchical one-pot synthesis has been implemented in Auto-CHO and uses fragments generated by one-pot reactions as new BBLs. In addition, Auto-CHO allows users to give feedback for virtual BBLs to keep valuable ones for further use. One-pot synthesis of stage-specific embryonic antigen 4 (SSEA-4), which is a pluripotent human embryonic stem cell marker, is demonstrated in this work.

Predicting RNA-binding sites of proteins using support vector machines and evolutionary information.

BACKGROUND: RNA-protein interaction plays an essential role in several biological processes, such as protein synthesis, gene expression, posttranscriptional regulation and viral infectivity. Identification of RNA-binding sites in proteins provides valuable insights for biologists. However, experimental determination of RNA-protein interaction remains time-consuming and labor-intensive. Thus, computational approaches for prediction of RNA-binding sites in proteins have become highly desirable. Extensive studies of RNA-binding site prediction have led to the development of several methods. However, they could yield low sensitivities in trade-off for high specificities. RESULTS: We propose a method, RNAProB, which incorporates a new smoothed position-specific scoring matrix (PSSM) encoding scheme with a support vector machine model to predict RNA-binding sites in proteins. Besides the incorporation of evolutionary information from standard PSSM profiles, the proposed smoothed PSSM encoding scheme also considers the correlation and dependency from the neighboring residues for each amino acid in a protein. Experimental results show that smoothed PSSM encoding significantly enhances the prediction performance, especially for sensitivity. Using five-fold cross-validation, our method performs better than the state-of-the-art systems by 4.90%-6.83%, 0.88%-5.33%, and 0.10-0.23 in terms of overall accuracy, specificity, and Matthew's correlation coefficient, respectively. Most notably, compared to other approaches, RNAProB significantly improves sensitivity by 7.0%-26.9% over the benchmark data sets. To prevent data over fitting, a three-way data split procedure is incorporated to estimate the prediction performance. Moreover, physicochemical properties and amino acid preferences of RNA-binding proteins are examined and analyzed. CONCLUSION: Our results demonstrate that smoothed PSSM encoding scheme significantly enhances the performance of RNA-binding site prediction in proteins. This also supports our assumption that smoothed PSSM encoding can better resolve the ambiguity of discriminating between interacting and non-interacting residues by modelling the dependency from surrounding residues. The proposed method can be used in other research areas, such as DNA-binding site prediction, protein-protein interaction, and prediction of posttranslational modification sites.