In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

[Q]: Paragraph: The open reading frame 3 of the severe acute respiratory syndrome coronavirus (SARS-CoV) genome encodes a predicted protein 3a, consisting of 274 amino acids, that lacks any significant similarities to any known protein. We generated specific antibodies against SARS protein 3a by using a synthetic peptide (P2) corresponding to amino acids 261-274 of the putative protein. Anti-P2 antibodies and the sera from SARS patients could specifically detect the recombinant SARS protein 3a expressed in Escherichia coli and in Vero E6 cells. Expression of SARS protein 3a was detected at 8-12 h after infection and reached a higher level after $24 h in SARS-CoV-infected Vero E6 cells. Protein 3a was also detected in the alveolar lining pneumocytes and some intra-alveolar cells of a SARS-CoVinfected patient's lung specimen. Recombinant protein 3a expressed in Vero E6 cells and protein 3a in the SARS-CoVinfected cells was distributed over the cytoplasm in a fine punctate pattern with partly concentrated staining in the Golgi apparatus. Our study demonstrates that SARS-CoV indeed expresses a novel protein 3a, which is present only in SARS-CoV and not in other known CoVs.
Title: Identification of a novel protein 3a from severe acute respiratory syndrome coronavirus
[A]: False

[Q]: Paragraph: Successful vaccine development remains a huge challenge for infectious diseases such as malaria, HIV and influenza. As a novel way to present antigenic epitopes to the immune system, we have developed icosahedral self-assembling protein nanoparticles (SAPNs) to serve as a prototypical vaccine platform for infectious diseases. Here we examine some biophysical factors that affect the self-assembly of these nanoparticles, which have as basic building blocks coiled-coil oligomerization domains joined by a short linker region. Relying on in silico computer modeling predictions, we selected five different linker regions from the RCSB protein database that connect oligomerization domains, and then further studied the self-assembly and stability of in vitro produced nanoparticles through biophysical characterization of formed particles. One design in particular, T2i88, revealed excellent self-assembly and homogeneity thus paving the way toward a more optimized nanoparticle for vaccine applications. From the Clinical Editor: Despite the widespread use of vaccines worldwide, successful development of vaccines against some diseases remains a challenge still. In this article, the authors investigated the physic-chemical and biological properties of icosahedral self-assembling protein nanoparticles (SAPNs), which mimic viral particles, in order to utilize this technology as potential platform for future design of vaccines.
Title: Optimizing the design of protein nanoparticles as carriers for vaccine applications
[A]: False

[Q]: Paragraph: To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing a knowledge base by organizing various experimental and computational observations as a collection of if-then rules. Here we report an expert system, which utilizes this knowledge base, for predicting localization sites of proteins only from the information on the amino acid sequence and the source origin. We collected data for 401 eukaryotic proteins with known localization sites (subcellular and extracellular) and divided them into training data and testing data. Fourteen localization sites were distinguished for animal cells and 17 for plant cells. When sorting signals were not well characterized experimentally, various sequence features were computationally derived from the training data. It was found that 66% of the training data and 59% of the testing data were correctly predicted by our expert system. This artificial intelligence approach is powerful and flexible enough to be used in genome analyses.
Title: A Knowledge Base for Predicting Protein Localization Sites in Eukaryotic Cells
[A]:
True