In this communication, we report a new type of amphiphilic carboxymethyl-hexanoyl chitosan (CHC)poly(acrylic acid) (PAA) hybrid macromolecule, which was successfully prepared through polymerization of acrylic acid (AA) in the presence of amphiphilic CHC. The chemical structure of the hybrid was characterized by FT-IR and 1 H NMR, which confirmed a chemical linkage between the amine groups of CHC and hydroxyl groups of AA. This CHC-PAA hybrid exhibited amphiphilic property as the original CHC rendering the self-assembling capability to be tunable in terms of AA concentration. Compared with the existing pristine chitosan-PAA co-polymers, this CHC-PAA hybrid exhibited a relatively high pHresponsive volumetric change by 10-100 times compared with the existing alternatives reported in the literature. The hybrid nanoparticles showed excellent encapsulation efficiency greater than 90% for a hydrophobic anti-cancer substance, (S)-(+)-camptothecin. A pH-responsive drug release behavior was systematically evaluated. Besides, this hybrid also revealed excellent cytocompatibility towards the MCF-7 and BCE cell lines, which, associated with its structural stability, suggests this new type of CHC-PAA hybrid to be a promising biomaterial for oral drug delivery application.

BackgroundOne of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.ResultsWe developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.ConclusionsComputational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users.