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".

[EX Q]: Paragraph: The tertiary structures of the S1 and S2 domains of the spike protein of the coronavirus which is responsible of the severe acute respiratory syndrome (SARS) have been recently predicted. Here a molecular assembly of SARS coronavirus peplomer which accounts for the available functional data is suggested. The interaction between S1 and S2 appears to be stabilised by a large hydrophobic network of aromatic side chains present in both domains. This feature results to be common to all coronaviruses, suggesting potential targeting for drugs preventing coronavirus-related infections. 
 Title: Prediction of quaternary assembly of SARS coronavirus peplomer
[EX A]: False

[EX Q]: Paragraph: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns. 
 Title: How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk
[EX A]: True

[EX Q]: Paragraph: Direct-acting antivirals are effective tools to control viral infections. SARS-CoV-2 is a coronavirus associated with the epidemiological outbreak in late 2019. Previous reports showed that HIV-1 protease inhibitors could block SARS-CoV main protease. Based on that and using an in silico approach, we evaluated SARS-CoV-2 main protease as a target for HIV-1 protease inhibitors to reveal the structural features related to their antiviral effect. Our results showed that several HIV inhibitors such as lopinavir, ritonavir, and saquinavir produce strong interaction with the active site of SARS-CoV-2 main protease. Furthermore, broad library protease inhibitors obtained from PubChem and ZINC (www.zinc.docking.org) were evaluated. Our analysis revealed 20 compounds that could be clustered into three groups based on their chemical features. Then, these structures could serve as leading compounds to develop a series of derivatives optimizing their activity against SARS-CoV-2 and other coronaviruses. Altogether, the results presented in this work contribute to gain a deep understanding of the molecular pharmacology of SARS-CoV-2 treatment and validate the use of protease inhibitors against SARS-CoV-2. 
 Title: UNREVEALING SEQUENCE AND STRUCTURAL FEATURES OF NOVEL CORONAVIRUS USING IN SILICO APPROACHES: THE MAIN PROTEASE AS MOLECULAR TARGET
[EX A]:
True