Aims: The study aims to isolate, evaluate bile tolerance and antibiogram studies of potential probiotics (Lactobacillus spp) from locally fermented Food Products (Akamu, Aqua Rafa® Yoghurt, Ogiri, Okpeye) and Kunu at Beach Market, Nsukka. Study Design: A ten - fold serial dilution and spread plate method using De Man, Rogosa and Sharpe (MRS) medium was adopted for isolation of potential Probionts. Place and Duration of Study: Department of Pharmaceutical Microbiology and Biotechnology, Faculty of Pharmaceutical Sciences, University of Nigeria Nsukka, between March - September, 2018. Methodology: Only catalase negative and Gram positive isolates characteristic of lactobacilli were used. Bile tolerance test was performed by monitoring the bacterial growth at different Bile salt concentrations (0.2%, 0.3% and 2%). The antibiogram of the isolates was assessed using the Kirby-Bauer disc diffusion method against commercial antibiotic discs of ampicillin, vancomycin, gentamycin, ciprofloxacin, methicillin and erythromycin. Results: All the 18 screened isolates were tolerant to bile salt at 0.2 % and 0.3 % with inhibition of growth at 2 % bile concentration. All isolates were observed to be resistant to methicillin (100 %) but very sensitive to gentamycin (11%) and ciprofloxacin (22%) respectively. The isolates showed intermediate resistance to other antibiotics: vancomycin (33%), erythromycin (33%) and ampicillin (44%). The decreasing pattern of resistance was thus: methicillin > ampicillin > vancomycin and erythromycin > ciprofloxacin > gentamycin. Isolates from Yoghurt (66.67%) and Ogiri (53.33%) provided most of the resistant isolates. Methicillin would provide best antagonist potential as all the isolates exhibited very high level of resistance (100 %). Conclusion: These results suggest that all the eighteen potential Lactobacillus spp strain show potential for probiotic applications and the locally fermented food products are rich sources of probionts.
Background In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models. Results Among the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%). Conclusions Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.
Background: In most parts of the world, especially in underdeveloped countries, Acquired Immunodeficiency Syndrome (AIDS) still remains a major cause of death, disability and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of Human Immunodeficiency Virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset and thereafter used a "stratified" ten-fold cross-validation technique for training and testing of the models.Results: Findings from this study show that logistic regression (AUC 0.97, F1 score 0.934 and balanced accuracy 87.2 %), and multi-layer perceptron classifier (AUC 0.97, F1 score 0.907 and balanced accuracy 87.4 %) have close predictive performance to the state-of-the-art model, linear support vector machine (AUC 0.97, F1 score 0.915 and balanced accuracy 90.0 %). Linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier also have good predictive performances (AUC 0.95 - 0.96, F1 score 0.919 - 0.931 and balanced accuracy 82.0 % - 85.7 %).Conclusions: Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.
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