Textmining in support of knowledge discovery for vaccine development

T-cell Epitope-based Vaccine Design for Nipah Virus by Reverse Vaccinology Approach

AIM AND OBJECTIVE

Nipah virus (NiV) is a zoonotic virus of the paramyxovirus family that sporadically breaks out from livestock and spreads in humans through breathing resulting in an indication of encephalitis syndrome. In the current study, T cell epitopes with the NiV W protein antigens were predicted.

MATERIALS AND METHODS

Modelling of unavailable 3D structure of W protein followed by docking studies of respective Human MHC - class I and MHC - class II alleles predicted was carried out for the highest binding rates. In the computational analysis, epitopes were assessed for immunogenicity, conservation, and toxicity analysis. T - cell-based vaccine development against NiV was screened for eight epitopes of Indian - Asian origin.

RESULTS

Two epitopes, SPVIAEHYY and LVNDGLNII, have been screened and selected for further docking study based on toxicity and conservancy analyses. These epitopes showed a significant score of -1.19 kcal/mol and 0.15 kcal/mol with HLA- B*35:03 and HLA- DRB1 * 07:03, respectively by using allele - Class I and Class II from AutoDock. These two peptides predicted by the reverse vaccinology approach are likely to induce immune response mediated by T - cells.

CONCLUSION

Simulation using GROMACS has revealed that LVNDGLNII epitope forms a more stable complex with HLA molecule and will be useful in developing the epitope-based Nipah virus vaccine.

Literature classification for semi-automated updating of biological knowledgebases

Background

As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature.

Results

We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature.

Conclusion

We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases.

Development of a Google-based search engine for data mining radiology reports

The aim of this study is to develop a secure, Google-based data-mining tool for radiology reports using free and open source technologies and to explore its use within an academic radiology department. A Health Insurance Portability and Accountability Act (HIPAA)-compliant data repository, search engine and user interface were created to facilitate treatment, operations, and reviews preparatory to research. The Institutional Review Board waived review of the project, and informed consent was not required. Comprising 7.9 GB of disk space, 2.9 million text reports were downloaded from our radiology information system to a fileserver. Extensible markup language (XML) representations of the reports were indexed using Google Desktop Enterprise search engine software. A hypertext markup language (HTML) form allowed users to submit queries to Google Desktop, and Google's XML response was interpreted by a practical extraction and report language (PERL) script, presenting ranked results in a web browser window. The query, reason for search, results, and documents visited were logged to maintain HIPAA compliance. Indexing averaged approximately 25,000 reports per hour. Keyword search of a common term like "pneumothorax" yielded the first ten most relevant results of 705,550 total results in 1.36 s. Keyword search of a rare term like "hemangioendothelioma" yielded the first ten most relevant results of 167 total results in 0.23 s; retrieval of all 167 results took 0.26 s. Data mining tools for radiology reports will improve the productivity of academic radiologists in clinical, educational, research, and administrative tasks. By leveraging existing knowledge of Google's interface, radiologists can quickly perform useful searches.