Delayed postoperative radiotherapy might improve the long-term prognosis of locally advanced esophageal squamous cell carcinoma.

OBJECTIVE: There is no consensus on the optimal timing of postoperative radiotherapy (PORT) for locally advanced esophageal squamous cell carcinoma (ESCC). We aimed to determine whether the timing of PORT affects the long-term prognosis of ESCC, and plotted nomograms to predict survival. METHODS: We retrospectively analyzed 351 ESCC patients who underwent radical surgery and PORT. Receiver operating characteristic curves were used to estimate the optimal cutoff point of the time interval between surgery and PORT. Cox proportional hazards regression was used to identify prognostic predictors. Overall survival (OS) and progression-free survival (PFS) were predicted using nomograms. RESULTS: The median follow-up was 53 months (range: 3-179 months). Compared to early PORT, PORT at >48 days after surgery was associated with better OS (adjusted hazard ratio [HR]: 1.406, p = 0.037) and PFS (adjusted HR: 1.475, p = 0.018). In the chemotherapy subgroup, incorporation of chemotherapy timing into the analysis suggested that 2-4 chemotherapy cycles followed by PORT was the optimal treatment schedule as compared to 0-1 chemotherapy cycle followed by PORT and concurrent chemoradiotherapy (5-year PFS: 65.9% vs. 51.0% vs. 50.1%; p = 0.049). The nomograms for OS and PFS were superior to the TNM classification (concordance indices: 0.721 vs. 0.626 and 0.716 vs. 0.610, respectively). CONCLUSIONS: Delayed PORT (>48 days) provides better survival benefit than early PORT among ESCC patients. PORT following 2-4 chemotherapy cycles might lead to the best survival rate. The nomogram plotted in this study effectively predicted survival and may help guide treatment.

Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.

Lysine 2-hydroxyisobutyrylation (Khib) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of Khib sites is a first-crucial step to decipher its molecular mechanism and urgently needed. In this work, based on a large benchmark datasets in multi-species, a novel online species-specific prediction tool, namely KhibPred, was developed to identify Khib sites. Four types of feature strategies, including sequence-based information, physicochemical properties and evolutionary-derived information, were applied to represent a wide range of protein sequences, and the random forest was used to build the optimal feature datasets. Moreover, six representative machine learning (ML) methods were trained and comprehensively discussed and compared for each organism. Data analyses suggested that the unique protein sequence preferences were discovered for each species. When evaluated on independent test datasets, the area under the receiver operating characteristic curves (AUCs) achieved 0.807, 0.781, 0.825 and 0.831 for Saccharomyces cerevisiaes, Physcomitrella patens, Rice Seeds and HeLa cells, respectively. The satisfactory results imply that KhibPred is a promising computational tool. The online predictor can be freely available at: http://bioinfo.ncu.edu.cn/KhibPred.aspx.

Design and synthesis of a vanadate-based ratiometric fluorescent probe for sequential recognition of Cu2+ ions and biothiols.

A novel YVO4:Eu3+@CDs core-shell nanomaterial with two main emission peaks at 405 and 617 nm was synthesized through a simple mixing method, in which the carbon quantum dots (CDs) self-assembled with the YVO4:Eu3+ nanoparticle, due to the high affinity of oxygen-containing groups such as -COOH or -OH of CDs to the metal ions on the surface of YVO4:Eu3+. The red fluorescence of YVO4:Eu3+@CDs located at 617 nm can be quenched by Cu2+ ions efficiently, while the blue emission remains invariable; based on this, we construct a ratio fluorescent probe YVO4:Eu3+@CDs for Cu2+ ion detection, in which the blue emission of CDs is selected as the reference signal, and the red emission of YVO4:Eu3+ acts as an output signal. Furthermore, the addition of biothiol recovers the quenched red fluorescence quickly, which can be completed in 18 minutes. Thus, YVO4:Eu3+@CDs can also be used as a 'turn on' ratio fluorescent probe for biothiol rapid detection. Taking l-cysteine (l-Cys) as the model, the fluorescence intensity of the 617 nm peak increases with increasing Cys, and the ratio of F617/F405 is linear to the concentration of Cys in the range of 0.1 µM to 10 µM with a detection limit of 41 nM. Compared with these single wavelength emission biothiol fluorescent probes, an obvious change in the fluorescence color from blue to pink can be conveniently observed by the naked eye under a UV lamp. Meanwhile, this ratiometric probe has also been demonstrated to be used for the visual identification of biothiols in real blood serum samples.

Curved Fragmented Graphenic Hierarchical Architectures for Extraordinary Charging Capacities.

An approach to assemble hierarchically ordered 3D arrangements of curved graphenic nanofragments for energy storage devices is described. Assembling them into well-defined interconnected macroporous networks, followed by removal of the template, results in spherical macroporous, mesoporous, and microporous carbon microball (3MCM) architectures with controllable features spanning nanometer to micrometer length scales. These structures are ideal porous electrodes and can serve as lithium-ion battery (LIB) anodes as well as capacitive deionization (CDI) devices. The LIBs exhibit high reversible capacity (up to 1335 mAh g-1 ), with great rate capability (248 mAh g-1 at 20 C) and a long cycle life (60 cycles). For CDI, the curved graphenic networks have superior electrosorption capacity (i.e., 5.17 mg g-1 in 0.5 × 10-3 m NaCl) over conventional carbon materials. The performance of these materials is attributed to the hierarchical structure of the graphenic electrode, which enables faster ion diffusion and low transport resistance.

Using support vector machines to identify protein phosphorylation sites in viruses.

Phosphorylation of viral proteins plays important roles in enhancing replication and inhibition of normal host-cell functions. Given its importance in biology, a unique opportunity has arisen to identify viral protein phosphorylation sites. However, experimental methods for identifying phosphorylation sites are resource intensive. Hence, there is significant interest in developing computational methods for reliable prediction of viral phosphorylation sites from amino acid sequences. In this study, a new method based on support vector machine is proposed to identify protein phosphorylation sites in viruses. We apply an encoding scheme based on attribute grouping and position weight amino acid composition to extract physicochemical properties and sequence information of viral proteins around phosphorylation sites. By 10-fold cross-validation, the prediction accuracies for phosphoserine, phosphothreonine and phosphotyrosine with window size of 23 are 88.8%, 95.2% and 97.1%, respectively. Furthermore, compared with the existing methods of Musite and MDD-clustered HMMs, the high sensitivity and accuracy of our presented method demonstrate the predictive effectiveness of the identified phosphorylation sites for viral proteins.

Outcome of patients with bulky IB (≥ 6 cm) cervical squamous cell carcinoma with and without cisplatin-based neoadjuvant chemotherapy.

OBJECTIVE: To study the surgical morbidity and outcomes of patients with markedly bulky cervical squamous cell carcinoma (≥ 6 cm Cx-SCC) who underwent radical hysterectomy (RH) with and without neoadjuvant chemotherapy (NACT). MATERIALS AND METHODS: This retrospective study enrolled patients with International Federation of Gynecology and Obstetrics (FIGO) IB markedly bulky Cx-SCC who were treated with either three courses of weekly single agent cisplatin NACT (50 mg/m2) and subsequent radical hysterectomy (NACT-RH) or direct radical hysterectomy (RH) between 1996 and 2001. A total of 60 patients fulfilled the criteria, including 35 and 25 patients with NsACT-RH and RH, respectively. RESULTS: There was no statistically significant difference in basic characteristics between the two groups, except the smaller pathological tumor size, less blood loss, and lower immediate complication rate in the NACT-RH group. Median survival was 143.8 months in the NACT-RH group and 129.8 months in the RH group, respectively, without a statistically significant difference. Multivariate analysis showed that large pathological tumor size [hazard ratio (HR) 10.66, 95% confidence interval (CI) 2.93-38.80], the presence of para-aortic lymph node metastases and an immediate complication (HR 8.33 and 4.55, 95% CI 1.66-41.75 and 1.35-15.27, respectively) contributed to a worse outcome. CONCLUSION: Weekly single agent cisplatin NACT indeed reduced the pathological tumor size and immediate complication rate during the RH, supporting the feasibility of subsequent RH in the management of patients with bulky Cx-SCC.

Electro-enhanced removal of copper ions from aqueous solutions by capacitive deionization.

This study was performed to determine the feasibility of electrosorptive removal of copper ions from aqueous solutions using a capacitive deionization process. The electrosorptive potential of copper ions was determined using cyclic voltammetry measurements, and copper electrodeposition could be suppressed at a voltage less than 0.8 V. Importantly, the experimental results demonstrated a significant enhancement of electrosorption capability of copper ions using the activated carbon electrodes under electro-assistance, associated with electrical double-layer charging. At 0.8 V, the equilibrium electrosorption capacity was enhanced to 24.57 mg/g based on the Langmuir model, and the electrosorption constant rate was increased to 0.038 min(-1) simulated by a first-order kinetics model. Moreover, the activated carbon electrode showed great regeneration performance for the removal of low level copper ions. Additional experiments regarding electrosorption selectivity were performed in the presence of sodium chloride, natural organic matter, or dissolved silica. Copper ions that were preferentially electroadsorbed on the electrode surface can be effectively removed in a competitive environment. Therefore, the electrosorption process using activated carbon electrodes can be recommended to treat copper solutions at low concentrations for wastewater treatment and water purification.

Optimization of reaction conditions towards multiple types of framework isomers and periodic-increased porosity: luminescence properties and selective CO2 adsorption over N2.

Three new metal-organic frameworks (MOFs) were prepared by solvo(hydro)thermolysis and further characterized as framework isomers. The structural transformation from non-porous to porous MOFs and the purity of these products can be modulated by controlling the reaction temperature. The periodic-increased porosity observed was further confirmed by CO2 adsorption isotherms. Owing to the presence of acylamide groups in the pore walls and the flexible nature of the skeleton of these MOFs, highly selective CO2 adsorption over N2 was observed, as well as structure-dependent periodic varieties in luminescence properties.

A novel two-dimensional metal-organic supramolecular framework including π-π stacking and hydrogen-bond interactions.

[µ-N,N'-Bis(pyridin-3-yl)benzene-1,4-dicarboxamide-1:2κ(2)N:N']bis{[N,N'-bis(pyridin-3-yl)benzene-1,4-dicarboxamide-κN]diiodidomercury(II)}, [Hg2I4(C18H14N4O2)3], is an S-shaped dinuclear molecule, composed of two HgI2 units and three N,N'-bis(pyridin-3-yl)benzene-1,4-dicarboxamide (L) ligands. The central L ligand is centrosymmetric and coordinated to two Hg(II) cations via two pyridine N atoms, in a syn-syn conformation. The two terminal L ligands are monodentate, with one uncoordinated pyridine N atom, and each adopts a syn-anti conformation. The HgI2 units show highly distorted tetrahedral (sawhorse) geometry, as the Hg(II) centres lie only 0.34 (2) or 0.32 (2) Šfrom the planes defined by the I and pyridine N atoms. Supramolecular interactions, thermal stability and solid-state luminescence properties were also measured.

Incorporating key position and amino acid residue features to identify general and species-specific Ubiquitin conjugation sites.

MOTIVATION: Systematic dissection of the ubiquitylation proteome is emerging as an appealing but challenging research topic because of the significant roles ubiquitylation play not only in protein degradation but also in many other cellular functions. High-throughput experimental studies using mass spectrometry have identified many ubiquitylation sites, primarily from eukaryotes. However, the vast majority of ubiquitylation sites remain undiscovered, even in well-studied systems. Because mass spectrometry-based experimental approaches for identifying ubiquitylation events are costly, time-consuming and biased toward abundant proteins and proteotypic peptides, in silico prediction of ubiquitylation sites is a potentially useful alternative strategy for whole proteome annotation. Because of various limitations, current ubiquitylation site prediction tools were not well designed to comprehensively assess proteomes. RESULTS: We present a novel tool known as UbiProber, specifically designed for large-scale predictions of both general and species-specific ubiquitylation sites. We collected proteomics data for ubiquitylation from multiple species from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates the information from key positions and key amino acid residues. Cross-validation tests reveal that UbiProber achieves some improvement over existing tools in predicting species-specific ubiquitylation sites. Moreover, independent tests show that UbiProber improves the areas under receiver operating characteristic curves by ~15% by using the Combined model. AVAILABILITY: The UbiProber server is freely available on the web at http://bioinfo.ncu.edu.cn/UbiProber.aspx. The software system of UbiProber can be downloaded at the same site. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The prediction of palmitoylation site locations using a multiple feature extraction method.

As an extremely important and ubiquitous post-translational lipid modification, palmitoylation plays a significant role in a variety of biological and physiological processes. Unlike other lipid modifications, protein palmitoylation and depalmitoylation are highly dynamic and can regulate both protein function and localization. The dynamic nature of palmitoylation is poorly understood because of the limitations in current assay methods. The in vivo or in vitro experimental identification of palmitoylation sites is both time consuming and expensive. Due to the large volume of protein sequences generated in the post-genomic era, it is extraordinarily important in both basic research and drug discovery to rapidly identify the attributes of a new protein's palmitoylation sites. In this work, a new computational method, WAP-Palm, combining multiple feature extraction, has been developed to predict the palmitoylation sites of proteins. The performance of the WAP-Palm model is measured herein and was found to have a sensitivity of 81.53%, a specificity of 90.45%, an accuracy of 85.99% and a Matthews correlation coefficient of 72.26% in 10-fold cross-validation test. The results obtained from both the cross-validation and independent tests suggest that the WAP-Palm model might facilitate the identification and annotation of protein palmitoylation locations. The online service is available at http://bioinfo.ncu.edu.cn/WAP-Palm.aspx.

Proteome-wide analysis of amino acid variations that influence protein lysine acetylation.

Next-generation sequencing (NGS) technologies are yielding ever higher volumes of genetic variation data. Given this large amount of data, it has become both a possibility and a priority to determine what the functional implication of genetic variations is. Considering the essential roles of acetylation in protein functions, it is highly likely that acetylation related genetic variations change protein functions. In this work, we performed a proteome-wide analysis of amino acid variations that could potentially influence protein lysine acetylation characteristics in human variant proteins. Here, we defined the AcetylAAVs as acetylation related amino acid variations that affect acetylation sites or their interacting acetyltransferases, and categorized three types of AcetylAAVs. Using the developed prediction system, named KAcePred, we detected that 50.87% of amino acid variations are potential AcetylAAVs and 12.32% of disease mutations could result in AcetylAAVs. More interestingly, from the statistical analysis, we found that the amino acid variations that directly create new potential lysine acetylation sites have more chance to cause diseases. It can be anticipated that the analysis of AcetylAAVs might be useful to screen important polymorphisms and help to identify the mechanism of genetic diseases. A user-friendly web interface for analysis of AcetylAAVs is now freely available at http://bioinfo.ncu.edu.cn/AcetylAAVs_Home.aspx .

Position-specific analysis and prediction for protein lysine acetylation based on multiple features.

Protein lysine acetylation is a type of reversible post-translational modification that plays a vital role in many cellular processes, such as transcriptional regulation, apoptosis and cytokine signaling. To fully decipher the molecular mechanisms of acetylation-related biological processes, an initial but crucial step is the recognition of acetylated substrates and the corresponding acetylation sites. In this study, we developed a position-specific method named PSKAcePred for lysine acetylation prediction based on support vector machines. The residues around the acetylation sites were selected or excluded based on their entropy values. We incorporated features of amino acid composition information, evolutionary similarity and physicochemical properties to predict lysine acetylation sites. The prediction model achieved an accuracy of 79.84% and a Matthews correlation coefficient of 59.72% using the 10-fold cross-validation on balanced positive and negative samples. A feature analysis showed that all features applied in this method contributed to the acetylation process. A position-specific analysis showed that the features derived from the critical neighboring residues contributed profoundly to the acetylation site determination. The detailed analysis in this paper can help us to understand more of the acetylation mechanism and can provide guidance for the related experimental validation.

[Effects of sodium tanshinone B on the protein expression of NMDAR1 in rat hippocampal subfields following focal ischemia/reperfusion injury].

UNLABELLED: OBJECTIVE To observe the changing laws of the protein expression of N-methyl D-aspartate receptor (NMDAR) in rat hippocampal subfields following focal ischemia/reperfusion injury, and to study the effects of sodium tanshinone B (STB) on it, thus exploring the possible mechanism of STB for treating cerebral ischemia. METHODS: The rat model of focal cerebral ischemia/reperfusion injury was established using middle cerebral artery occlusion (MCAO) by reversibly inserting a nylon thread. The Wistar rats were randomly divided into the sham-operation group, the I/R model group, and the low, middle, and high dose STB groups. The neural functional disturbance was scored referring to the 5-grade Zea Longa EL standard. The protein expression of NMDAR1 in the ischemic side was detected using immunohistochemical assay. RESULTS: There was statistical difference in the scores of the neural functional disturbance in the middle and high dose STB groups when compared with the model group (P < 0.01). Results of the immunohistochemical assay showed the expression of NMDAR1 in CA1 region was obviously higher in the I/R model group, the low and middle dose STB groups than in the sham-operation group (P < 0.01). The expression of NMDAR1 in CA1 region was obviously lower in the high dose STB group than in the I/R model group (P < 0.01), the low (P < 0.01) and middle dose STB groups (P < 0.05). The expression of NMDAR1 in CA3 region was obviously higher in the low dose STB group and the I/R model group than in the sham-operation group, the middle and high dose STB groups (P < 0.01). The expression of NMDAR1 in CA3 region was obviously higher in the high and middle dose STB groups than in the sham-operation group (P < 0.05). CONCLUSIONS: STB could promote the recovery of neural functions in cerebral ischemia/reperfusion injury rats. STB fought against cerebral ischemia/reperfusion injury by lowering excitable neurotransmitter glumatic acid and reducing the protein expression of NMDAR1.

Identifying protein quaternary structural attributes by incorporating physicochemical properties into the general form of Chou's PseAAC via discrete wavelet transform.

In vivo, some proteins exist as monomers and others as oligomers. Oligomers can be further classified into homo-oligomers (formed by identical subunits) and hetero-oligomers (formed by different subunits), and they form the structural components of various biological functions, including cooperative effects, allosteric mechanism and ion-channel gating. Therefore, with the avalanche of protein sequences generated in the post-genomic era, it is very important for both basic research and the pharmaceutical industry to acquire the possible knowledge about quaternary structural attributes of their proteins of interest. In view of this, a high throughput method (DWT_DT), a 2-layer approach by fusing discrete wavelet transform (DWT) and decision-tree algorithm (DT) with physicochemical features, has been developed to predict protein quaternary structures. The 1st layer is to assign a query protein to one of the 10 main quaternary structural attributes. The 2nd layer is to evaluate whether the protein in question is composed of homo- or hetero-oligomers. The overall accuracy by jackknife test for the 1st layer identification was 89.60%. The overall accuracy of the 2nd layer varies from 88.23 to 100%. The results suggest that this newly developed protocol (DWT_DT) is very promising in predicting quaternary structures with complicated composition.

A method to distinguish between lysine acetylation and lysine methylation from protein sequences.

Lysine acetylation and methylation are two major post-translational modifications of lysine residues. They play vital roles in both biological and pathological processes. Specific lysine residues in H3 histone protein tails appear to be targeted for either acetylation or methylation. Hence it is very challenging to distinguish between acetylated and methylated lysine residues using computational methods. This work presents a method that incorporates protein sequence information, secondary structure and amino acid properties to differentiate acetyl-lysine from methyl-lysine. We apply an encoding scheme based on grouped weight and position weight amino acid composition to extract sequence information and physicochemical properties around lysine sites. The proposed method achieves an accuracy of 93.3% using a jackknife test. Feature analysis demonstrates that the prediction model with multiple features can take full advantage of the supplementary information from different features to improve classification performance and prediction robustness. Analysis of the characteristics of lysine residues which can be either methylated or acetylated shows that they are more similar to methyl-lysine than to acetyl-lysine.

PMeS: prediction of methylation sites based on enhanced feature encoding scheme.

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew's correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew's correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx.

PredSulSite: prediction of protein tyrosine sulfation sites with multiple features and analysis.

Tyrosine sulfation is a ubiquitous posttranslational modification that regulates extracellular protein-protein interactions, intracellular protein transportation modulation, and protein proteolytic process. However, identifying tyrosine sulfation sites remains a challenge due to the lability of sulfation sequences. In this study, we developed a method called PredSulSite that incorporates protein secondary structure, physicochemical properties of amino acids, and residue sequence order information based on support vector machine to predict sulfotyrosine sites. Three types of encoding algorithms-secondary structure, grouped weight, and autocorrelation function-were applied to mine features from tyrosine sulfation proteins. The prediction model with multiple features achieved an accuracy of 92.89% in 10-fold cross-validation. Feature analysis showed that the coil structure, acidic amino acids, and residue interactions around the tyrosine sulfation sites all contributed to the sulfation site determination. The detailed feature analysis in this work can help us to understand the sulfation mechanism and provide guidance for the related experimental validation. PredSulSite is available as a community resource at http://www.bioinfo.ncu.edu.cn/inquiries_PredSulSite.aspx.

PLMLA: prediction of lysine methylation and lysine acetylation by combining multiple features.

Post-translational lysine methylation and acetylation are two major modifications of lysine residues. They play critical roles in various biological processes, especially in gene regulation. Identification of protein methylation and acetylation sites would be a foundation for understanding their modification dynamics and molecular mechanism. This work presents a method called PLMLA that incorporates protein sequence information, secondary structure and amino acid properties to predict methylation and acetylation of lysine residues in whole protein sequences. We apply an encoding scheme based on grouped weight and position weight amino acid composition to extract sequence information and physicochemical properties around lysine sites. The prediction accuracy for methyllysine and acetyllysine are 83.02% and 83.08%, respectively. Feature analysis reveals that methyllysine is likely to occur at the coil region and acetyllysine prefers to occur at the helix region of protein. The upstream residues away from the central site may be close to methylated lysine in three-dimensional structure and have a significant influence on methyllysine, while the positively charged residues may have a significant influence on acetyllysine. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PLMLA.aspx.

A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization.

Knowing the subcellular localization of proteins within the cell is an important step in elucidating its role in biological processes, its function and its potential as a drug target for disease diagnosis. As the number of complete genomes rapidly increases, accurate and efficient methods that automatically predict the subcellular localizations become more urgent. In the current paper, we developed a novel method that coupled the discrete wavelet transform with support vector machine based on the amino acid polarity to predict the subcellular localizations of prokaryotic and eukaryotic proteins. The results obtained by the jackknife test were quite promising, and indicated that the proposed method remarkably improved the prediction accuracy of subcellular locations, and could be as an effective and promising high-throughput method in the subcellular localization research.