Objectives: To examine national trends in unhealthy lifestyle factors among adults with cardiovascular disease (CVD) in the United States (US) between 1999 and 2018. Methods: We analyzed data from National Health and Nutrition Examination Survey (NHANES), a nationally representative survey of participants with CVD who were aged ≥20 years, which was conducted between 1999 and 2000 and 2017-2018. CVD was defined as a self-report of congestive heart failure, coronary heart disease, angina, heart attack, or stroke. The prevalence rate of each unhealthy lifestyle factor was calculated among adults with CVD for each of the 2-year cycle surveys. Regression analyses were used to assess the impact of sociodemographic characteristics (age, sex, race/ethnicity, family income, education level, marital status, and employment status). Results: The final sample included 5610 NHANES respondents with CVD. The prevalence rate of their current smoking status remained stable among respondents with CVD between 1999 and 2000 and 2017-2018. During the same period, there was a decreasing trend in the age-adjusted prevalence rate of poor diet [primary American Heart Association (AHA) score <20; 47.5% (37.9%-57.0%) to 37.5% (25.7%-49.3%), p < 0.01]. Physical inactivity marginally increased before decreasing, with no statistical significance. The prevalence rate of sedentary behavior increased from 2007 to 2014 but subsequently returned to its original level in 2018 with no statistical significance. The age-adjusted prevalence rate of obesity increased from 32% (27.2%-36.8%) in 1999-2000 to 47.9% (39.9%-55.8%) in 2017-2018 (p < 0.001). The age-adjusted prevalence rate of depression increased from 7% (4.2%-9.9%) in 1999-2000 to 13.9% (10.2%-17.6%) in 2017-2018 (p = 0.056). Trends in mean for each unhealthy lifestyle factor were similar after adjustment for age. We found that respondents who had low education and income levels were at a higher risk of being exposed to unhealthy lifestyle factors (i.e., smoking, poor diet, and physical inactivity) than those who had high education and income levels. Conclusions: There is a significant reduction in the prevalence rate of poor diet among US adults with CVD between 1999 and 2018, while the prevalence rate of obesity showed increasing trends over this period. The prevalence rate of current smoking status, sedentary behavior, and depression was either stable or showed an insignificant increase. These findings suggest that there is an urgent need for health policy interventions targeting unhealthy lifestyles among adults with CVD.
BACKGROUND: Unhealthy lifestyle factors are risk factors for stroke, and they play a key role in stroke secondary prevention. A better understanding of these factors may aid with improvements in public health policy. OBJECTIVE: Our objective was to comprehensively understand the trends in unhealthy lifestyle factors in people who have previously had a stroke in the US. METHODS: Utilizing data from the biannual United States National Health and Nutrition Examination Surveys (NHANESs) between 1999 and 2018, we collated data on unhealthy lifestyle factors (smoking, alcohol drinking, depression, unhealthy diet, high BMI, physical inactivity, and sedentary behavior) in adults with a history of stroke. The Joinpoint Regression model was used to calculate the annual percentage change (APC) and average annual percentage change (AAPC) to identify trends. Logistic regression modeling was used to identify the influence of sociodemographic factors (age, sex, race/ethnicity, marital status, employment status, family income, and highest education level). RESULTS: The analysis included 2017 respondents with a history of stroke. Current alcohol drinking (39.3% (95% confidence interval: 29.8, 48.7) to 57.4% (45.7, 69.0) p = 0.008) and obesity (39.2% (28.3, 50.2) to 49.4% (38.9, 59.8) p = 0.029) increased significantly from 1999 to 2018. The prevalence of smoking and depression remained generally stable. The proportion of respondents with an unhealthy diet decreased from 1999 (44.5% (32.4, 56.5)) to 2011 (29.0% (17.5, 40.4) p = 0.019), but then returned to its original prevalence in 2018 (42.0% (31.4, 52.7)). From 2007 to 2018, the proportion of respondents who were physically inactive decreased significantly, from 70.4% (64.4, 76.3) to 55.1% (46.1, 64.2; p = 0.017). After a gradual increase in sedentary activity from 2007 to 2012, this declined from 2013 to 2018, with no statistical significance. We found stroke survivors who were widowed, divorced, separated, or unemployed were at a higher risk of having unhealthy lifestyles than those who were employed or had other marital statuses. CONCLUSIONS: A modest reduction in the prevalence of physical inactivity was observed in Americans with a history of stroke between 1999 and 2018. The prevalences of smoking, drinking, depression, poor diet, obesity, and sedentary behavior were stable or increasing.
Background: Pompe disease (PD) is a rare, progressive, and autosomal recessive lysosomal storage disorder caused by mutations in the acid α-glucosidase gene. The clinical course and molecular mechanism of this disease in China have not been well defined. Methods: In this single-center cohort study, we investigated a total of 15 Chinese patients with Pompe disease to better understand the clinical manifestations, echocardiographic imaging and genetic characteristics in this population. Results: The median age of 15 patients at symptom onset was 5.07 months (1-24 months). The median age at diagnosis was 19.53 months (range: 3 to 109 months, n = 15). Average diagnostic delay was 13.46 months. None of the patients had received enzyme replacement therapy (ERT). Fifteen patients died at a median age of 24.80 months due to cardiorespiratory failure (range 3-120 months). Myasthenia symptoms and severe hypertrophic cardiomyopathy were universally present (15/15 = 100%). Global longitudinal strain (GLS) by echocardiography was significantly lower in these patients. After adjusting for gender, body surface area (BSA), left ventricular ejection fraction (LVEF), E/e'ratio, maximum left ventricular wall thickness (MLVWT), left ventricular posterior wall (LVPW), left ventricular outflow tract (LVOT)gradient, GLS was independently correlated with survival time (hazard ratio (HR) = 0.702, 95% confidence Interval (CI): 0.532-0.925, P = 0.012). In our cohort, we identified 4 novel GAA mutation: c.2102T > C (p.L701P), c.2006C > T (p.P669l), c.766T > A (p.Y256N), c.2405G > T (p.G802V). 12 patients were compound heterozygotes, and 4 homozygotes. Conclusions: Our study provides a comprehensive examination of PD clinical course and mutations of the GAA gene for patients in China. We showed clinical utility of echocardiography in quantifying heart involvement in patients with suspected PD. GLS can provide prognostic information for mortality prediction. We reported four novel mutations in the GAA gene for the first time. Our findings may improve early recognition of PD characteristics in Chinese patients.
Background: Prehospital delay is associated with non-modifiable factors such as age, residential region, and disease severity. However, the impact of psychosocial factors especially for job burnout on prehospital decision delay is still little understood. Method: This internet-based survey was conducted between 14 February 2021 and 5 March 2021 in China through the Wechat platform and web page. Self-designed questionnaires about the expected and actual length of prehospital decision time and the Chinese version of Maslach Burnout Inventory-General Survey, Type D Personality Scale-14, and Social Support Rating Scale were applied. A total of 1,039 general participants with a history of perceptible but tolerable body discomfort were included. Results: The top six reasons for prehospital decision delay were: (1) endure until self-healing (50.7%), (2) too busy to ask for leave (40.3%), (3) process for seeing a doctor too complicated (35.8%), (4) too tired after work (26.2%), (5) worry about the expenditure (16.6%), and (6) fear of being identified as with serious problem (14.5%). The univariate analyses revealed that older age (p = 0.001), type D personality (p = 0.025), job burnout (p = 0.055), and worrying about expenditure (p = 0.004) were associated with prolonged prehospital decision time, while engaged in medical-related job (p = 0.028) and with more social support (p = 0.066) would shorten the delay. The multivariate analysis using logistic regression model with forward selecting method showed that age [per 10 years, odds ratio (OR) 1.19 (1.09-1.31), p < 0.001], job burnout [per 10 points in Maslach Burnout Inventory-General Survey (MBI-GS), OR 1.17 (1.04-1.31), p = 0.007], and worrying about expenditure [OR 1.75 (1.25-2.47), p = 0.001] were the three determinants for prehospital decision delay (>7 days). Mediating effects were analyzed by using bias-corrected percentile bootstrap methods (N = 10,000). Social support was found partially mediated the relationship between the determinants and prehospital decision time. The partial mediating effect of social support accounted for 24.0% of the total effect for job burnout and 11.6% for worrying about expenditure. Conclusion: Psychosocial factors have a non-negligible impact on prehospital decision delay. The crucial part of prehospital decision delay may be the lack of motivation inside. Job burnout and lack of social support, as two commonly seen features in the modern world, should be given enough consideration in disease prevention and treatment.
Introduction: High-intensity interval training (HIIT) is an emerging method of cardiac rehabilitation, which is more and more popular in recent years. Research into the effect of HIIT on peak oxygen uptake (VO2 peak) and myocardial fibrosis among patients with myocardial infarction (MI) is lacking. Here, we describe the rationale along with the protocol for a clinical trial to test the following hypotheses: (1) compared with the control group, VO2 peak will be increased in both the moderate-intensity continuous training (MICT) and HIIT groups and (2) compared with the control group, myocardial fibrosis due to MI will be improved by HIIT and MICT. Methods and Analysis: This is a single-center, randomized controlled clinical trial. In total, 180 patients with MI are to be recruited for this study. VO2 peak will be tested by cardiopulmonary exercise testing (CPET) and myocardial fibrosis will be evaluated by cardiac MR. A variety of blood and psychometric tests and also the peripheral arterial tonometry, reactive hyperemia index for microvascular endothelial function, and microvascular blockage or digital vasomotor response are included. Ethics and Dissemination: The ethics committee of the Guangdong Provincial People's Hospital has authorized this mechanistic clinical research. Peer-reviewed articles and conference presentations will be used to disseminate the findings. Trial Registration Number: NCT04863677.
AIM: Exploring the risk factors of prognosis in patients undergoing percutaneous coronary intervention (PCI) is of great importance. Our aim of the study is to investigate the association between variability in total cholesterol (TC) level and major adverse cardiovascular and cerebrovascular events (MACCE) in patients after PCI. METHODS: Between April 2004 and December 2009, 909 patients who underwent primary PCI and with at least three TC values were included in the final study. TC variability was calculated using four indices: standard deviation (SD), coefficient of variation (CV), the average successive variability (ASV), variability independent of the mean (VIM). MACCE comprised all-cause mortality, non-fatal myocardial infarction (MI), unplanned revascularization, hospitalization for heart failure, and non-fatal stroke. RESULTS: There were 394 cases of MACCE during the follow-up period. When the subjects were divided into quartile groups by CV of TC, high CV groups were associated with a higher hazard ratio of MACCE than for lower CV groups. In multivariable adjusted models, TC variability and MACCE remained correlated [HR (95% CI): Q2, 1.17 (0.86-1.58); Q3, 1.38 (1.03-1.85); Q4, 1.63 (1.22-2.17)]. Similar patterns of MACCE were noted by quartiles of SD, ASV, and VIM. CONCLUSION: Visit-to-visit TC variability is positively correlated with MACCE in patients after PCI.
Percutaneous Coronary Intervention , Cholesterol , Hospitalization , Humans , Risk Factors
Members of the mitochondrial transcription terminator factor (mTERF) family, originally identified in vertebrate mitochondria, are involved in the termination of organellular transcription. In plants, mTERF proteins are mainly localized in chloroplasts and mitochondria. In Arabidopsis (Arabidopsis thaliana), mTERF8/pTAC15 was identified in the plastid-encoded RNA polymerase (PEP) complex, the major RNA polymerase of chloroplasts. In this work, we demonstrate that mTERF8 is associated with the PEP complex. An mTERF8 knockout line displayed a wild-type-like phenotype under standard growth conditions, but showed impaired efficiency of photosystem II electron flow. Transcription of most chloroplast genes was not substantially affected in the mterf8 mutant; however, the level of the psbJ transcript from the psbEFLJ polycistron was increased. RNA blot analysis showed that a larger transcript accumulates in mterf8 than in the wild type. Thus, abnormal transcription and/or RNA processing occur for the psbEFLJ polycistron. Circular reverse transcription PCR and sequence analysis showed that the psbJ transcript terminates 95 nucleotides downstream of the translation stop codon in the wild type, whereas its termination is aberrant in mterf8 Both electrophoresis mobility shift assays and chloroplast chromatin immunoprecipitation analysis showed that mTERF8 specifically binds to the 3' terminal region of psbJ Transcription analysis using the in vitro T7 RNA polymerase system showed that mTERF8 terminates psbJ transcription. Together, these results suggest that mTERF8 is specifically involved in the transcription termination of the chloroplast gene psbJ.
Arabidopsis Proteins/metabolism , Arabidopsis/metabolism , Chloroplasts/metabolism , Transcription, Genetic/genetics , Arabidopsis/genetics , Arabidopsis Proteins/genetics , Chloroplasts/genetics , Chromatin Immunoprecipitation , DNA-Directed RNA Polymerases/genetics , DNA-Directed RNA Polymerases/metabolism , Electrophoretic Mobility Shift Assay , Protein Binding
Apolipoprotein A-I (apoA-I) mimetic peptide, D-4F, exhibits anti-atherogenic effects similar to high-density lipoprotein (HDL). However, it remains elusive whether D-4F and HDL share similar molecular mechanisms underlying anti-atherogenic effects and endothelial cell protections. We here compared the metabolic changes in endothelial cells induced by D-4F and HDL against oxidized low-density lipoprotein (ox-LDL), which may be of benefit to understanding the protective mechanisms of HDL and D-4F. Functional assays, including wound healing, transwell migration, and tube formation, were used to evaluate the pro-angiogenic effects of HDL and D-4F. NMR-based metabolomic analysis was employed to explore the protective mechanisms underlying HDL and D-4F. Partial least-squares discriminant analysis (PLS-DA) was performed to assess metabolic profiles, and orthogonal PLS-DA (OPLS-DA) was carried out to identify characteristic metabolites. Moreover, significantly altered metabolic pathways were also analyzed. We found that ox-LDL impaired the migration and tube formation of endothelial cells. Metabolomic analysis showed that ox-LDL triggered oxidative stress, impaired glycolysis, and enhanced glycerophospholipid metabolism. Both HDL and D-4F improved the migration and angiogenesis of endothelial cells, alleviated oxidative stress, and ameliorated disordered glycolysis impaired by ox-LDL. Strikingly, HDL partially attenuated the disturbed glycerophospholipid metabolism, whereas D-4F did not show this effect. In summary, although D-4F shared the similar protective effects with HDL on the migration and angiogenesis of endothelial cells, it could not deduce the molecular mechanisms of HDL completely. Nevertheless, D-4F possesses the potentiality to be exploited as clinically applicable agent for endothelial cell protection and cardiovascular disease treatment.
Nuclear ubiquitous casein and cyclin-dependent kinase substrate (NUCKS) is highly expressed in the brain and peripheral metabolic organs, and regulates transcription of a number of genes involved in insulin signalling. Whole-body depletion of NUCKS (NKO) in mice leads to obesity, glucose intolerance and insulin resistance. However, a tissue-specific contribution of NUCKS to the observed phenotypes remains unknown. Considering the pivotal roles of insulin signalling in the brain, especially in the hypothalamus, we examined the functions of hypothalamic NUCKS in the regulation of peripheral glucose metabolism. Insulin signalling in the hypothalamus was impaired in the NKO mice when insulin was delivered through intracerebroventricular injection. To validate the hypothalamic specificity, we crossed transgenic mice expressing Cre-recombinase under the Nkx2.1 promoter with floxed NUCKS mice to generate mice with hypothalamus-specific deletion of NUCKS (HNKO). We fed the HNKO and littermate control mice with a normal chow diet (NCD) and a high-fat diet (HFD), and assessed glucose tolerance, insulin tolerance and metabolic parameters. HNKO mice showed mild glucose intolerance under an NCD, but exacerbated obesity and insulin resistance phenotypes under an HFD. In addition, NUCKS regulated levels of insulin receptor in the brain. Unlike HNKO mice, mice with immune-cell-specific deletion of NUCKS (VNKO) did not develop obesity or insulin-resistant phenotypes under an HFD. These studies indicate that hypothalamic NUCKS plays an essential role in regulating glucose homoeostasis and insulin signalling in vivo.
Glucose/metabolism , Hypothalamus/metabolism , Nuclear Proteins/metabolism , Obesity/metabolism , Phosphoproteins/metabolism , Animals , Homeostasis , Humans , Insulin/metabolism , Insulin Resistance , Mice , Mice, Knockout , Nuclear Proteins/genetics , Obesity/genetics , Phosphoproteins/genetics
FoxO1, which is up-regulated during early stages of diet-induced leptin resistance, directly interacts with STAT3 and prevents STAT3 from binding to specificity protein 1 (SP1)-pro-opiomelanocortin (POMC) promoter complex, and thereby inhibits STAT3-mediated regulation of POMC transcription. FoxO1 also binds directly to the POMC promoter and negatively regulates its transcription. The present study aims to understand the relative contribution of the two interactions in regulating POMC expression. We studied the structural requirement of FoxO1 for its interaction with STAT3 and POMC promoter, and tested the inhibitory action of FoxO1 mutants by using biochemical assays, molecular biology and computer modelling. FoxO1 mutant with deletion of residues Ala137-Leu160 failed to bind to STAT3 or inhibit STAT3-mediated POMC activation, although its binding to the POMC promoter was unaffected. Further analysis mapped Gly140-Leu160 to be critical for STAT3 binding. The identified region Gly140-Leu160 was conserved among mammalian FoxO1 proteins, and showed a high degree of sequence identity with FoxO3, but not FoxO4. Consistently, FoxO3 could interact with STAT3 and inhibit POMC promoter activity, whereas FoxO4 could not bind to STAT3 or affect POMC promoter activity. We further identified that five residues (Gln145, Arg147, Lys148, Arg153 and Arg154) in FoxO1 were necessary in FoxO1-STAT3 interaction, and mutation of these residues abolished its interaction with STAT3 and inhibition of POMC promoter activity. Finally, a FoxO1-STAT3 interaction interface model generated by computational docking simulations confirmed that the identified residues of FoxO1 were in close proximity to STAT3. These results show that FoxO1 inhibits STAT3-mediated leptin signalling through direct interaction with STAT3.
Down-Regulation , Forkhead Transcription Factors/metabolism , Leptin/metabolism , Models, Biological , Pro-Opiomelanocortin/agonists , STAT3 Transcription Factor/metabolism , Transcription, Genetic , Animals , Conserved Sequence , Forkhead Box Protein O1 , Forkhead Box Protein O3 , Forkhead Transcription Factors/chemistry , Forkhead Transcription Factors/genetics , HEK293 Cells , Humans , Leptin/genetics , Mice , Molecular Docking Simulation , Mutant Proteins/chemistry , Mutant Proteins/metabolism , Peptide Fragments/chemistry , Peptide Fragments/genetics , Peptide Fragments/metabolism , Pro-Opiomelanocortin/genetics , Pro-Opiomelanocortin/metabolism , Promoter Regions, Genetic , Receptors, Leptin/agonists , Receptors, Leptin/genetics , Receptors, Leptin/metabolism , Recombinant Proteins/chemistry , Recombinant Proteins/metabolism , Sequence Deletion , Sequence Homology, Amino Acid , Signal Transduction
Although much is known about the molecular players in insulin signaling, there is scant information about transcriptional regulation of its key components. We now find that NUCKS is a transcriptional regulator of the insulin signaling components, including the insulin receptor (IR). Knockdown of NUCKS leads to impaired insulin signaling in endocrine cells. NUCKS knockout mice exhibit decreased insulin signaling and increased body weight/fat mass along with impaired glucose tolerance and reduced insulin sensitivity, all of which are further exacerbated by a high-fat diet (HFD). Genome-wide ChIP-seq identifies metabolism and insulin signaling as NUCKS targets. Importantly, NUCKS is downregulated in individuals with a high body mass index and in HFD-fed mice, and conversely, its levels increase upon starvation. Altogether, NUCKS is a physiological regulator of energy homeostasis and glucose metabolism that works by regulating chromatin accessibility and RNA polymerase II recruitment to the promoters of IR and other insulin pathway modulators.
Diabetes Mellitus, Type 2/metabolism , Glucose Intolerance/metabolism , Glucose/metabolism , Insulin/metabolism , Nuclear Proteins/metabolism , Phosphoproteins/metabolism , Animals , Body Weight , Diabetes Mellitus, Type 2/genetics , Homeostasis , Humans , Insulin Resistance , Mice , Mice, Knockout , Nuclear Proteins/genetics , Phosphoproteins/genetics , Signal Transduction , Transcriptional Activation
Protein disordered regions are associated with some critical cellular functions such as transcriptional regulation, translation and cellular signal transduction, and they are responsible for various diseases. Although experimental methods have been developed to determine these regions, they are time-consuming and expensive. Therefore, it is highly desired to develop computational methods that can provide us with this kind information in a rapid and inexpensive manner. Here we propose a sequence-based computational approach for predicting protein disordered regions by means of the Nearest Neighbor algorithm, in which conservation, amino acid factor and secondary structure status of each amino acid in a fixed-length sliding window are taken as the encoding features. Also, the feature selection based on mRMR (maximum Relevancy Minimum Redundancy) is applied to obtain an optimal 51-feature set that includes 39 conservation features and 12 secondary structure features. With the optimal 51 features, our predictor yielded quite promising MCC (Mathew's correlation coefficients): 0.371 on a rigorous benchmark dataset tested by 5-fold cross-validation and 0.219 on an independent test dataset. Our results suggest that conservation and secondary structure play important roles in intrinsically disordered proteins.
Amino Acids/chemistry , Protein Structure, Secondary , Proteins/chemistry , Sequence Analysis, Protein , Algorithms , Humans
Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ~30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the ß-strand-turn-ß-strand motif in A-ßpeptide amyloid and ß-solenoid structure of HET-s(218-289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues.
Amyloid/metabolism , Algorithms , Amino Acid Sequence , Amyloid/genetics , Databases, Protein , Humans , Protein Structure, Tertiary
Given a compounds-forming system, i.e., a system consisting of some compounds and their relationship, can it form a biologically meaningful pathway? It is a fundamental problem in systems biology. Nowadays, a lot of information on different organisms, at both genetic and metabolic levels, has been collected and stored in some specific databases. Based on these data, it is feasible to address such an essential problem. Metabolic pathway is one kind of compounds-forming systems and we analyzed them in yeast by extracting different (biological and graphic) features from each of the 13,736 compounds-forming systems, of which 136 are positive pathways, i.e., known metabolic pathway from KEGG; while 13,600 were negative. Each of these compounds-forming systems was represented by 144 features, of which 88 are graph features and 56 biological features. "Minimum Redundancy Maximum Relevance" and "Incremental Feature Selection" were utilized to analyze these features and 16 optimal features were selected as being able to predict a query compounds- forming system most successfully. It was found through Jackknife cross-validation that the overall success rate of identifying the positive pathways was 74.26%. It is anticipated that this novel approach and encouraging result may give meaningful illumination to investigate this important topic.
Algorithms , Metabolic Networks and Pathways , Saccharomyces cerevisiae/metabolism , Databases, Factual , Predictive Value of Tests , Saccharomyces cerevisiae/chemistry , Systems Biology
Phosphorylation is one of the most important post-translational modifications, and the identification of protein phosphorylation sites is particularly important for studying disease diagnosis. However, experimental detection of phosphorylation sites is labor intensive. It would be beneficial if computational methods are available to provide an extra reference for the phosphorylation sites. Here we developed a novel sequence-based method for serine, threonine, and tyrosine phosphorylation site prediction. Nearest Neighbor algorithm was employed as the prediction engine. The peptides around the phosphorylation sites with a fixed length of thirteen amino acid residues were extracted via a sliding window along the protein chains concerned. Each of such peptides was coded into a vector with 6,072 features, derived from Amino Acid Index (AAIndex) database, for the classification/detection. Incremental Feature Selection, a feature selection algorithm based on the Maximum Relevancy Minimum Redundancy (mRMR) method was used to select a compact feature set for a further improvement of the classification performance. Three predictors were established for identifying the three types of phosphorylation sites, achieving the overall accuracies of 66.64%, 66.11%% and 66.69%, respectively. These rates were obtained by rigorous jackknife cross-validation tests.
Peptides/chemistry , Phosphoproteins/chemistry , Sequence Analysis, Protein/methods , Support Vector Machine , Binding Sites , Computational Biology , Data Mining , Databases, Protein , Peptides/metabolism , Phosphoproteins/metabolism , Phosphorylation , Predictive Value of Tests , Protein Processing, Post-Translational , Serine/metabolism , Threonine/metabolism , Tyrosine/metabolism
It is of great use to find out and clear up the interactions between enzymes and small molecules, for understanding the molecular and cellular functions of organisms. In this study, we developed a novel method for the prediction of enzyme-small molecules interactions based on machine learning approach. The biochemical and physicochemical description of proteins and the functional group composition of small molecules are used for representing enzyme-small molecules pairs. Tested by jackknife cross-validation, our predictor achieved an overall accuracy of 87.47%, showing an acceptable efficiency. The 39 features selected by feature selection were analyzed for further understanding of enzyme-small molecule interactions.
Algorithms , Proteins/chemistry , Sequence Analysis, Protein/methods , Small Molecule Libraries/chemistry , Software , Support Vector Machine , Amino Acid Sequence , Computational Biology , Databases, Protein , Hydrophobic and Hydrophilic Interactions , Molecular Sequence Data , Predictive Value of Tests , Protein Binding , Proteins/metabolism , Small Molecule Libraries/metabolism
It is well known that protein subcellular localizations are closely related to their functions. Although many computational methods and tools are available from Internet, it is still necessary to develop new algorithms in this filed to gain a better understanding of the complex mechanism of plant subcellular localization. Here, we provide a new web server named PSCL for plant protein subcellular localization prediction by employing optimized functional domains. After feature optimization, 848 optimal functional domains from InterPro were obtained to represent each protein. By calculating the distances to each of the seven categories, PSCL showing the possibilities of a protein located into each of those categories in ascending order. Toward our dataset, PSCL achieved a first-order predicted accuracy of 75.7% by jackknife test. Gene Ontology enrichment analysis showing that catalytic activity, cellular process and metabolic process are strongly correlated with the localization of plant proteins. Finally, PSCL, a Linux Operate System based web interface for the predictor was designed and is accessible for public use at http://pscl.biosino.org/.
Plant Cells/chemistry , Plant Proteins/chemistry , Plants/chemistry , Software , Subcellular Fractions/chemistry , Algorithms , Biological Evolution , Computational Biology , Databases, Protein , Phylogeny , Plant Cells/physiology , Plant Proteins/genetics , Protein Structure, Tertiary
Ubiquitination, one of the most important post-translational modifications of proteins, occurs when ubiquitin (a small 76-amino acid protein) is attached to lysine on a target protein. It often commits the labeled protein to degradation and plays important roles in regulating many cellular processes implicated in a variety of diseases. Since ubiquitination is rapid and reversible, it is time-consuming and labor-intensive to identify ubiquitination sites using conventional experimental approaches. To efficiently discover lysine-ubiquitination sites, a sequence-based predictor of ubiquitination site was developed based on nearest neighbor algorithm. We used the maximum relevance and minimum redundancy principle to identify the key features and the incremental feature selection procedure to optimize the prediction engine. PSSM conservation scores, amino acid factors and disorder scores of the surrounding sequence formed the optimized 456 features. The Mathew's correlation coefficient (MCC) of our ubiquitination site predictor achieved 0.142 by jackknife cross-validation test on a large benchmark dataset. In independent test, the MCC of our method was 0.139, higher than the existing ubiquitination site predictor UbiPred and UbPred. The MCCs of UbiPred and UbPred on the same test set were 0.135 and 0.117, respectively. Our analysis shows that the conservation of amino acids at and around lysine plays an important role in ubiquitination site prediction. What's more, disorder and ubiquitination have a strong relevance. These findings might provide useful insights for studying the mechanisms of ubiquitination and modulating the ubiquitination pathway, potentially leading to potential therapeutic strategies in the future.
Computational Biology/methods , Lysine/metabolism , Proteins/metabolism , Algorithms , Amino Acid Sequence , Databases, Protein , Ubiquitination
Protein methylation, one of the most important post-translational modifications, typically takes place on arginine or lysine residue. The reversible modification involves a series of basic cellular processes. Identification of methyl proteins with their sites will facilitate the understanding of the molecular mechanism of methylation. Besides the experimental methods, computational predictions of methylated sites are much more desirable for their convenience and fast speed. Here, we propose a method dedicated to predicting methylated sites of proteins. Feature selection was made on sequence conservation, physicochemical/biochemical properties, and structural disorder by applying maximum relevance minimum redundancy and incremental feature selection methods. The prediction models were built according to nearest the neighbor algorithm and evaluated by the jackknife cross-validation. We built 11 and 9 predictors for methylarginine and methyllysine, respectively, and integrated them to predict methylated sites. As a result, the average prediction accuracies are 74.25%, 77.02% for methylarginine and methyllysine training sets, respectively. Feature analysis suggested evolutionary information, and physicochemical/biochemical properties play important roles in the recognition of methylated sites. These findings may provide valuable information for exploiting the mechanisms of methylation. Our method may serve as a useful tool for biologists to find the potential methylated sites of proteins.
Arginine/chemistry , Lysine/chemistry , Methylation , Models, Biological
BACKGROUND: With the huge amount of uncharacterized protein sequences generated in the post-genomic age, it is highly desirable to develop effective computational methods for quickly and accurately predicting their functions. The information thus obtained would be very useful for both basic research and drug development in a timely manner. METHODOLOGY/PRINCIPAL FINDINGS: Although many efforts have been made in this regard, most of them were based on either sequence similarity or protein-protein interaction (PPI) information. However, the former often fails to work if a query protein has no or very little sequence similarity to any function-known proteins, while the latter had similar problem if the relevant PPI information is not available. In view of this, a new approach is proposed by hybridizing the PPI information and the biochemical/physicochemical features of protein sequences. The overall first-order success rates by the new predictor for the functions of mouse proteins on training set and test set were 69.1% and 70.2%, respectively, and the success rate covered by the results of the top-4 order from a total of 24 orders was 65.2%. CONCLUSIONS/SIGNIFICANCE: The results indicate that the new approach is quite promising that may open a new avenue or direction for addressing the difficult and complicated problem.
Computational Biology/methods , Proteins/physiology , Systems Biology/methods , Animals , Artificial Intelligence , Mice , Protein Binding , Protein Interaction Mapping
