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".

Paragraph: The yeast two-hybrid (Y2H) system is a powerful method to identify and analyze binary protein interactions. In the fi eld of virology, the Y2H system has signifi cantly increased our knowledge of structure and function of viral proteins by systematically assessing intraviral protein interactions. Several comprehensive approaches to determine virus-host interactions have provided insight into viral strategies to manipulate the host for effi cient replication and to escape host-derived countermeasures. To expand our knowledge of intraviral and virus-host protein interactions, we here present a Y2H protocol that is well suited for high-throughput screening. Yeast mating followed by liquid handling in a 96-well format as well as fl uorescent readout of the reporter system provides a highly standardized and fully automated screening situation. The protocol can either be applied to screen complex host cDNA libraries or protein pairs arrayed for cross-testing. The ease of use, the cost-effectiveness as well as the robotic handling allows for extensive and multiple rounds of screening providing high coverage of protein-protein interactions. Thus, this protocol represents an improved "deep" screening method for high-throughput Y2H assays. 
 Title: Chapter 1 A High-Throughput Yeast Two-Hybrid Protocol to Determine Virus-Host Protein Interactions
True

Paragraph: MRNA2 of the arteriviruses lactate dehydrogenase-elevating virus (LDV) and equine arteritis virus (EAV) encodes two proteins that are read in different frames, an about 26 kDa minor envelope glycoprotein and an about 8 kDa protein that lacks N-glycosylation sites and a signal peptide, but possesses a central hydrophobic segment. Recent studies have shown that both proteins of EAV are translated from mRNA 2 in EAV infected BHK cells, that the 8 kDa protein is membrane associated and that small amounts of it are recovered in purified virions (Snijder, E.J., van Tol, H., Pederson, K.W., Raamsman, M.J.B., de Vries, A.A.F., 1999. Identification of a novel structural protein of arteriviruses. J. Virol. 73, 6335-6345). The authors concluded that the 8 kDa protein is another arterivirus envelope protein and designated it E protein. However, we have not detected a significant level of an 8 kDa protein in LDV virions and thus conclude that it is not a structural virion component. 
 Title: An ORF-2a protein is not present at a significant level in virions of the arterivirus lactate dehydrogenase-elevating virus
True

Paragraph: Motivation: Accurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm. Results: We trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours. Availability: The repository of this project will be published. There are two main approaches to predict protein structures: homology modeling and de novo structure prediction. They differ in such that homology modeling requires structural knowledge about a homologous protein for its prediction while de novo methods do not. This limits the 
 Title: DeepTracer: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Den- sity Maps of Protein Complexes XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX

True