Accurate Prediction of Immunogenic T-Cell Epitopes from Epitope Sequences Using the Genetic Algorithm-Based Ensemble Learning

<div><p>Background</p><p>T-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.</p><p>Methods</p><p>In this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set.</p><p>Results</p><p>Two datasets named ‘IMMA2’ and ‘PAAQD’ are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant.</p><p>Conclusions</p><p>The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128194#pone.0128194.s001" target="_blank">S1 File</a>.</p></div

The absolute values of correlation coefficients of AUC scores yielded by individual feature-based models

<p>The absolute values of correlation coefficients of AUC scores yielded by individual feature-based models</p

The flowchart of GA-based ensemble method.

<p>The flowchart of GA-based ensemble method.</p

The frequencies of features in the optimal feature subsets.

<p>The frequencies of features in the optimal feature subsets.</p

Details about sequence-derived features.

<p>* <i>n</i> is the number of sequences in the dataset, 0<λ< <i>L(sequence length)</i>, the new feature means that the features were not used in the immunogenic epitope prediction</p><p>Details about sequence-derived features.</p

The average performances of different individual feature-based models, evaluated on IMMA2 by 20 independent runs of the 10-CV.

<p>The average performances of different individual feature-based models, evaluated on IMMA2 by 20 independent runs of the 10-CV.</p

The average AUC scores of individual feature-based models using different values for λ, evaluated on IMMA2 by 20 independent runs of the 10-CV.

<p>The average AUC scores of individual feature-based models using different values for λ, evaluated on IMMA2 by 20 independent runs of the 10-CV.</p

Anaerobic Transformation of DDT Related to Iron(III) Reduction and Microbial Community Structure in Paddy Soils

We studied the mechanisms of microbial transformation in functional bacteria on 1,1,1-trichloro-2,2-bis­(<i>p</i>-chlorophenyl)­ethane (DDT) in two different field soils, Haiyan (HY) and Chenghai (CH). The results showed that microbial activities had a steady dechlorination effect on DDT and its metabolites (DDx). Adding lactate or glucose as carbon sources increased the amount of <i>Desulfuromonas</i>, <i>Sedimentibacter</i>, and <i>Clostridium</i> bacteria, which led to an increase in adsorbed Fe­(II) and resulted in increased DDT transformation rates. The electron shuttle of anthraquinone-2,6-disulfonic disodium salt resulted in an increase in the negative potential of soil by mediating the electron transfer from the bacteria to the DDT. Moreover, the DDT-degrading bacteria in the CH soil were more abundant than those in the HY soil, which led to higher DDT transformation rates in the CH soil. The most stable compound of DDx was 1,1-dichloro-2,2-bis­(<i>p</i>-chloro-phenyl)­ethane, which also was the major dechlorination metabolite of DDT, and 1-chloro-2,2-bis-(<i>p</i>-chlorophenyl)­ethane and 4,4′-dichlorobenzo-phenone were found to be the terminal metabolites in the anaerobic soils

Biostimulation of Indigenous Microbial Communities for Anaerobic Transformation of Pentachlorophenol in Paddy Soils of Southern China

This study explored biostimulation mechanisms with an electron donor and a shuttle for accelerating pentachlorophenol (PCP) transformation in iron-rich soils. The results indicated that indigenous microbial communities are important for PCP transformation in soils. Biostimulation of indigenous microbial communities by the addition of lactate and anthraquinone-2,6-disulfonate (AQDS) led to the enhanced rates of PCP dechlorination by the dechlorinating- and iron-reducing bacteria in soils. The electrochemical studies using cyclic voltammograms and microbial current measurements confirmed the high reduction potential and the large amount of electrons generated under biostimulation conditions, which were responsible for the higher rates of PCP transformation. After biostimulation treatments by the additions of lactate and/or AQDS during PCP dechlorination processes, microbial community analysis by the terminal restriction fragment length polymorphism (T-RFLP) method showed the abundance terminal restricted fragments (T-RFs), an indicator of bacterial abundance, which represents the dechlorinating- and iron-reducing bacteria, suggesting their critical roles in PCP dechlorination in soils