25 research outputs found
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Peptides play an important role in all aspects of the immunological reactions to invading cancer and pathogen cells. It has been known for over 40-years that peptides are critical influences in assembling the immune system against foreign invaders. Since then, new knowledge about the generation and function of peptides in immunology has supported efforts to harness the immune system to treat disease. Yet, with little immunological insight, most of the highly productive treatments, including vaccines, have been developed empirically. Nonetheless, increased knowledge of the biology of antigen processing as well as chemistry and pharmacological properties of antigenic and antimicrobial peptides has now permitted to development of drugs and vaccines. Due to advanced technologies, it is vitally important to develop automatic computational methods for rapidly and accurately predicting immune-peptides. In this thesis, the author focuses on the machine learning approaches for addressing classification problems of four types of immune-peptides (anti-inflammatory, proinflammatory, anti-tuberculosis, and linear B-cell peptides).Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate a set of informative probabilistic features by making the use of random forest models with eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by the linearly combined models of the informative probabilistic features. The generalization capability of our proposed method evaluated through independent test showed that ProIn-Fuse yielded an accuracy of 0.746, which was over 10% higher than those obtained by the state-of-the-art PIP predictors. Cross-validation and independent results consistently demonstrated that ProIn-Fuse is more precise and promising in the identification of PIPs than existing PIP predictors. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse/. We believe that the proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. Herein, we have developed an effective computational predictor iAntiTB (identification of anti-tubercular peptides) that integrates multiple feature vectors deriving from the amino acid sequences via Random Forest (RF) and Support Vector Machine (SVM) classifiers. The iAntiTB combined the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor we prepared the two datasets with different types of negative samples. The iAntiTB achieved AUC values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors. Thus, the iAntiTB is a robust and accurate predictor that is helpful for researchers working on peptide therapeutics and immunotherapy. All the employed datasets and software application are accessible at http://kurata14.bio.kyutech.ac.jp/iAntiTB/. Linear B-cell peptides are critically important for immunological applications such as vaccine design, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. The accurate identification of linear B-cell peptides remains challenging despite several decades of research. In this work, we have developed a novel predictor, iLBE (Identification of B-Cell Epitope), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon rank-sum test. Then the random forest (RF) algorithm used the optimal consecutive feature vectors to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. The performance of the final iLBE yielded an AUC score of 0.809 on the training dataset. It outperformed other existing prediction models on a comprehensive independent dataset. The iLBE is suggested to be a powerful computational tool to identify the linear B-cell peptides and development of penetrating diagnostic tests. A web application with curated datasets is freely accessible of iLBE at http://kurata14.bio.kyutech.ac.jp/iLBE/. Taken together, the above results suggest that our proposed predictors (PreAIP, ProIn-Fuse, iAntiTB, and iLBE) would be helpful computational resources for the prediction of anti-inflammatory, pro-inflammatory, tuberculosis, and linear B-cell peptides. / ããããã¯ãçãç
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å·¥åç²ç¬¬358å· åŠäœæäžå¹Žææ¥ïŒä»€å3幎3æ25æ¥1 Introduction|2 Prediction of Anti-Inflammatory Peptides by Integrating Mulptle Complementary Features|3 Prediction of Proinflammatory Peptides by Fusing of Multiple Feature Representations|4 Prediction of Anti-Tubercular Peptides by Exploiting Amino Acid Pattern and Properties|5 Prediction of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features|6 Conclusions and Perspectivesä¹å·å·¥æ¥å€§åŠä»€å2幎
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Prediction of S-nitrosylation Sites by Integrating Support Vector Machine and Random Forest
Cysteine S-nitrosylation is a type of reversible post-translational modification of the protein, which controls many cellular plasticity and dynamics. It is associated with redox-based cellular signaling to protect against oxidative stress and exposed various biological diseases. The identification of S-nitrosylation sites is an important step to reveal the function of proteins; however, experimental identification of S-nitrosylation is expensive and time-consuming work. The sequence-based computational prediction of potential S-nitrosylation sites is highly sought before experimentation. Herein, to identify S-nitrosylation sites, a novel predictor PreSNO has been developed that integrates multiple encoding schemes by the support vector machine and random forest. The PreSNO achieved an AUC score of 0.837 on the training model and greatly outperformed other existing computational models on comprehensive independent datasets
PreAIP: Computational Prediction of Anti-inflammatory Peptides by Integrating Multiple Complementary Features
Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time-consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying AIPs and contributes to the development of AIPs therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/
Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information
Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/
Restoration of uterine redox-balance by methanolic extract of Camellia sinensis in arsenicated rats
Arsenic, an environmental and industrial pollutant causes female reproductive disturbances and female infertility. Several researchers found that the use of Camellia sinensis (CS) (green tea) is effective as an alternative therapeutic strategy in the management of several health ailments. This study explores the role of CS extract against arsenic-induced rat uterine tissue damage. Methanolic extract of CS (10 mg/kg BW) was tested concomitantly in arsenic-treated (10 mg/kg BW) rats for a duration of two-oestrous cycle length (8 days). CS effectively attenuated arsenic-induced antioxidantdepletion and necrosis in uterine tissue. Rats treated with sodium arsenite showed significantly
reduced activities of enzymatic antioxidants like superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (GPx) in uterine tissue as evidenced by the results of spectrophotometric and electrozymographic analysis. Co-administration of CS significantly reversed the above oxidative stress markers in uterine tissue along with the histopathological changes in ovarian and uterine tissue. Moreover, an increase in the level of transcription factor NF-κB in the uterine tissue in association with reduced serum levels of vitamin B12 and folic acid were mitigated in arsenic fed rats following CS co-administration
Ways of Improving the Maternal Health of Women Garmentâs Worker â A Bangladesh perspective
Objective: The primary source of earning foreign currency in Bangladesh is the Garment sector which is mainly based on women. On the other hand, women are directly related to maternal health. So, the aim of this study is to identify the condition of maternal health and find out the ways of improving the maternal health of the women working in the RMG (Ready Made Garments) sectors in Bangladesh.
Materials &Methods: This study has conducted exploratory qualitative research design following two different styles: 1) in-depth interviews(20) and 2) open-ended questions (52) and thereby analysis of both content where the total sample number is 72.
Results: Results shows that 7.5% pregnant mother attended World Health Organization recommended (absolute) 4 antenatal visits, 22.50% any time or first time antenatal visit of their whole pregnancy life, 70% pregnant women did not take any antenatal visit for their pregnancy period. In addition to that, 59.72% of female garment workers are in underprivileged maternity leave, 40.28 % are getting in different periods that are 16.66% on 112 days, 12.5% on 90 days and 11.11% are within 45 days.
Conclusion: Inadequate antenatal visits & maternity leave, work load, illiteracy, lack of health knowledge about pregnancy and mother-unfriendly factory environment are the root problems of pregnant garment workers in Bangladesh. There are different positive steps from different sites which could improve maternal health of female garment workers in Bangladesh. Combined effort of factory owner, worker union and government is finding the ways to improve maternal health of women garment workers in Bangladesh
Machine learning approaches for addressing classification problems of four types of immune-peptides
1 Introduction||2 Prediction of Anti-Inflammatory Peptides by Integrating Mulptle Complementary Features||3 Prediction of Proinflammatory Peptides by Fusing of Multiple Feature Representations||4 Prediction of Anti-Tubercular Peptides by Exploiting Amino Acid Pattern and Properties||5 Prediction of Linear B-Cell Epitopes by Integrating Sequence and Evolutionary Features||6 Conclusions and PerspectivesPeptides play an important role in all aspects of the immunological reactions to invading cancer and pathogen cells. It has been known for over 40-years that peptides are critical influences in assembling the immune system against foreign invaders. Since then, new knowledge about the generation and function of peptides in immunology has supported efforts to harness the immune system to treat disease. Yet, with little immunological insight, most of the highly productive treatments, including vaccines, have been developed empirically. Nonetheless, increased knowledge of the biology of antigen processing as well as chemistry and pharmacological properties of antigenic and antimicrobial peptides has now permitted to development of drugs and vaccines. Due to advanced technologies, it is vitally important to develop automatic computational methods for rapidly and accurately predicting immune-peptides. In this thesis, the author focuses on the machine learning approaches for addressing classification problems of four types of immune-peptides ïŒanti-inflammatory, proinflammatory, anti-tuberculosis, and linear B-cell peptidesïŒ.Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP ïŒPredictor of Anti-Inflammatory PeptidesïŒ was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying anti-inflammatory peptides and contributes to the development of anti-inflammatory peptides therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/. A proinflammatory peptide ïŒPIPïŒ is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate a set of informative probabilistic features by making the use of random forest models with eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by the linearly combined models of the informative probabilistic features. The generalization capability of our proposed method evaluated through independent test showed that ProIn-Fuse yielded an accuracy of 0.746, which was over 10% higher than those obtained by the state-of-the-art PIP predictors. Cross-validation and independent results consistently demonstrated that ProIn-Fuse is more precise and promising in the identification of PIPs than existing PIP predictors. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse/. We believe that the proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. Tuberculosis ïŒTBïŒ is a leading killer caused by Mycobacterium tuberculosis. Recently anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. Herein, we have developed an effective computational predictor iAntiTB ïŒidentification of anti-tubercular peptidesïŒ that integrates multiple feature vectors deriving from the amino acid sequences via Random Forest ïŒRFïŒ and Support Vector Machine ïŒSVMïŒ classifiers. The iAntiTB combined the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor we prepared the two datasets with different types of negative samples. The iAntiTB achieved AUC values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors. Thus, the iAntiTB is a robust and accurate predictor that is helpful for researchers working on peptide therapeutics and immunotherapy. All the employed datasets and software application are accessible at http://kurata14.bio.kyutech.ac.jp/iAntiTB/. Linear B-cell peptides are critically important for immunological applications such as vaccine design, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. The accurate identification of linear B-cell peptides remains challenging despite several decades of research. In this work, we have developed a novel predictor, iLBE ïŒIdentification of B-Cell EpitopeïŒ, by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon rank-sum test. Then the random forest ïŒRFïŒ algorithm used the optimal consecutive feature vectors to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. The performance of the final iLBE yielded an AUC score of 0.809 on the training dataset. It outperformed other existing prediction models on a comprehensive independent dataset. The iLBE is suggested to be a powerful computational tool to identify the linear B-cell peptides and development of penetrating diagnostic tests. A web application with curated datasets is freely accessible of iLBE at http://kurata14.bio.kyutech.ac.jp/iLBE/. Taken together, the above results suggest that our proposed predictors ïŒPreAIP, ProIn-Fuse, iAntiTB, and iLBEïŒ would be helpful computational resources for the prediction of anti-inflammatory, pro-inflammatory, tuberculosis, and linear B-cell peptides. / ããããã¯ãçãç
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Prediction of protein Post-Translational Modification sites: An overview
Post-translational modification (PTM) refers to the covalent and enzymatic modification of proteins during or after protein biosynthesis. In the protein biosynthesis process, the ribosomal mRNA is translated into polypeptide chains, which may further undergo PTM to form the product of mature protein [1]. PTM is a common biological mechanism of both eukaryotic and prokaryotic organisms, which regulates the protein functions, the proteolytic cleavage of regulatory subunits or the degradation of entire proteins and affects all aspects of cellular life. The PTM of a protein can also determine the cell signaling state, turnover, localization, and interactions with other proteins [2]. Therefore, the analysis of proteins and their PTMs are particularly important for the study of heart disease, cancer, neurodegenerative diseases and diabetes [3,4]. Although the characterization of PTMs gets invaluable insight into the cellular functions in etiological processes, there are still challenges. Technically, the major challenges in studying PTMs are the development of specific detection and purification methods
Combined Dietary Action of Spirulina and Probiotics Mitigates Female Reproductive Ailments in Arsenicated Rats
Heavy metals contaminations in food and water are increased due to the environmental pollution.
Managing arsenic toxicosis by dietary therapy is yet to be explored, although the conventional
therapeutic strategy emphasizes the invasive chelating therapy. In this present study we elucidated
the ameliorative effect of Spirulina and probiotics against arsenic-mediated female gonadal injury. The
treatment was continued for 8 days (2 estrus cycles) on rats with sodium arsenite (1.0 mg/ 100g body
weight) orally, but spirulina (40 mg/100g body weight) and commercially available probiotics mixture
(2 mg/ 100g body weight) were added in rat chow, fresh daily. Uterine and ovarian tissue experienced
a significant impairment of antioxidant status, while a pronounced ovarian follicular degeneration was
apparent from the increased number of follicular atresia in arsenic treated rats. All these deleterious
effects of sodium arsenite were diminished significantly by spirulina and probiotics in arsenic fed rat.
Moreover, an increase in the serum levels of of homocysteine (Hcy) in association with reduced serum
levels of vitamin B12 and folic acid were mitigated in arsenic fed rats following spirulina and probiotics
dietary co-administration. However, the outcome of this study may indicate that spirulina and probiotics
may be incorporated in the meal as nutraceuticals in limiting arsenic-mediated health hazards