LitCovid-sentences  

PMC:7111504 / 16552-18751 JSONTXT 11 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T185 0-10 Sentence denotes Discussion
T186 11-172 Sentence denotes In this study we provide a profile of computationally predicted immunogenic peptides from 2019-nCoV for functional validation and potential vaccine developments.
T187 173-307 Sentence denotes We are fully aware that an effective vaccine development will require a very thorough investigation of immune correlates to 2019-nCoV.
T188 308-476 Sentence denotes However, due to the emergency and severity of the outbreak as well as the lack of access to samples from infected subjects, such approaches would not serve the urgency.
T189 477-683 Sentence denotes Therefore, computational prediction is instrumental for guiding biologists towards a quick and cost-effective solution to prevent the spread and ultimately help eliminate the infection from the individuals.
T190 684-824 Sentence denotes With a rising global concern of novel coronavirus outbreak, numerous research groups have started to investigate and publish their findings.
T191 825-981 Sentence denotes At the time of preparing this manuscript, we became aware of a similar study conducted in comparing 2019-nCoV proteome with SARS CoV immunogenic peptides 9.
T192 982-1218 Sentence denotes Our in silico approach takes the search beyond presenting only common immunogenic peptide between SARS and 2019-nCoV and provides the experimental community with a more comprehensive list including de novo and cross reactive candidates.
T193 1219-1420 Sentence denotes On the other hand, considering the fact that two studies have been accomplished independently with distinct approaches, this serves to demonstrate a high level of confidence in reproducing the results.
T194 1421-1571 Sentence denotes Reproducibility of computational prediction is always of high importance and becomes even more significant under urgent scenarios as of this outbreak.
T195 1572-1727 Sentence denotes Our study also suggests the need for further efforts to develop accurate predictive models and algorithms for the characterization of immunogenic peptides.
T196 1728-2075 Sentence denotes In this study, we provide potential immunogenic peptides from 2019-nCoV for vaccine targets that i) have been characterized immunogenic by previous studies on SARS CoV, ii) have high degree of similarity with immunogenic SARS CoV peptides and iii) are predicted immunogenic by combination of NetMHCpan and iPred/1G4 TCR positional weight matrices.
T197 2076-2199 Sentence denotes Given the limited time and resources, our work serves as a guide to save time and cost for further experimental validation.